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10 pages, 1490 KiB

Open AccessProceeding Paper

A Quantitative Model of Supply Chain Disruption Propagation Dynamics

by Shudong Liu, Shili Xiang and Lu Wang

Eng. Proc. 2025, 89(1), 1; https://doi.org/10.3390/engproc2025089001 - 21 Feb 2025

Viewed by 349

Supply chain disruptions caused by natural disasters and human-made incidents have inflicted substantial losses on numerous companies. The management of supply chain risks, including disruption risk, has garnered significant attention from both academia and industries over the past few decades. Companies must develop [...] Read more.

Supply chain disruptions caused by natural disasters and human-made incidents have inflicted substantial losses on numerous companies. The management of supply chain risks, including disruption risk, has garnered significant attention from both academia and industries over the past few decades. Companies must develop effective solutions for disruption risk management. To seek an effective solution with disruption event monitoring and mitigation plans, we investigated the mechanisms and dynamics of disruption propagation along the supply chain. Specifically, we developed a quantitative mathematical model for the entire supply chain using system dynamics to understand the characteristics of disruption propagation, such as time spent for the disruption from the event location to a focal company and its impact. The model provides important management information on the development of a disruption monitoring system to enhance the resilience and robustness of supply chains, develop mitigation strategies, and minimize the adverse effects of disruptions. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 202 KiB

Open AccessProceeding Paper

Enhancing Stroke Risk Prediction: Leveraging Machine Learning and Magnetic Resonance Imaging Data for Advanced Assessment

by Pranav Kunderu, Salik Mian and Shivm Patel

Eng. Proc. 2025, 89(1), 2; https://doi.org/10.3390/engproc2025089002 - 21 Feb 2025

Viewed by 383

Abstract

As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. We developed a quantitative method to predict [...] Read more.

As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. We developed a quantitative method to predict strokes before happening. We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre-stroke and post-stroke. We trained machine learning models using the data, including support vector machines (SVMs), K-nearest neighbors (KNNs), and random forests. The models predicted the risk of a stroke accurately. The models allow for diagnosing and enable clinicians to care for patients promptly, potentially saving lives and improving outcomes. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

9 pages, 1014 KiB

Open AccessProceeding Paper

Application of XGBoost Algorithm to Develop Mutual Fund Marketing Prediction Model for Banks’ Wealth Management

by Jen-Ying Shih

Eng. Proc. 2025, 89(1), 3; https://doi.org/10.3390/engproc2025089003 - 21 Feb 2025

Viewed by 351

Abstract

Competition in Taiwan’s banking industry is becoming fierce. Banks’ traditional income based on interest rates is insufficient to support their growth. Therefore, banks are eager to expand their wealth management business to increase profits. The fee income from the sale of mutual funds [...] Read more.

Competition in Taiwan’s banking industry is becoming fierce. Banks’ traditional income based on interest rates is insufficient to support their growth. Therefore, banks are eager to expand their wealth management business to increase profits. The fee income from the sale of mutual funds is one of the major sources of banks’ wealth management business. The problem is how to look for the right customers and contact them effectively. Therefore, it is necessary to develop classification prediction models for these banks to evaluate their customers’ potential to buy mutual fund products sold by commercial banks and then deploy marketing resources on these customers to increase banks’ profits. Recently, the XGBoost algorithm has been widely used in conducting classification tasks. Therefore, using the eXtreme Gradient Boosting algorithm, a mutual fund marketing prediction model is developed based on a commercial bank’s data in this study. The results show that whether a customer has an unsecured loan, a customer’s amount of assets in the bank, the number of months for transactions, a place of residence, and whether the bank is the main bank for the total amount of credit card bills in the past six months are the top five factors for the models, providing valuable information for effective wealth management and marketing. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 1139 KiB

Open AccessProceeding Paper

Deepening Mathematical Understanding Using Visualization and Interactive Learning for Deaf Students

by Stefanie Amiruzzaman, Md Amiruzzaman, Heena Begum, Deepshikha Bhati and Tsung Heng Wu

Eng. Proc. 2025, 89(1), 4; https://doi.org/10.3390/engproc2025089004 - 21 Feb 2025

Viewed by 251

Abstract

Learning mathematical concepts is challenging for several students. Paying attention to class lectures and following instructions for different steps to solve a problem are the keys to success in learning mathematical concepts. However, deaf and hard-of-hearing (DHH) students have challenges in focusing on [...] Read more.

Learning mathematical concepts is challenging for several students. Paying attention to class lectures and following instructions for different steps to solve a problem are the keys to success in learning mathematical concepts. However, deaf and hard-of-hearing (DHH) students have challenges in focusing on their teacher’s mouths to lipread or depend on an interpreter’s sign language. Visualization is important in learning as it enhances attention and keeps students focused on a subject. Interactive learning or a hands-on approach can help learners engage in a topic and provide an opportunity to better understand a concept. Combining these two techniques (i.e., visualization and interactive learning), we present an interactive number line (INL) tool to help DHH students understand mathematical concepts such as mean, median, mode, and range. This tool has proven to be useful for visual learning and/or for new learners as it helps provide an activity-based learning environment and feedback. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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9 pages, 1672 KiB

Open AccessProceeding Paper

Algorithm Design for Multicellular Molecular Communication Simulations

by Shohei Imanaka and Tadashi Nakano

Eng. Proc. 2025, 89(1), 5; https://doi.org/10.3390/engproc2025089005 - 21 Feb 2025

Viewed by 171

Abstract

Computing interactions among cells is computationally complex in multicellular molecular communication simulations, making the design of efficient algorithms crucial. We review simulation algorithms for computing interactions among cells in multicellular molecular communication simulations. The naïve algorithm, the Cell List algorithm, the Barnes–Hut algorithm, [...] Read more.

Computing interactions among cells is computationally complex in multicellular molecular communication simulations, making the design of efficient algorithms crucial. We review simulation algorithms for computing interactions among cells in multicellular molecular communication simulations. The naïve algorithm, the Cell List algorithm, the Barnes–Hut algorithm, and a hybrid of the Cell List and Barnes–Hut algorithms are explored. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 455 KiB

Open AccessProceeding Paper

Disaster Response System Dynamic Assessment Model Based on Questionnaire Investigation: A Case Study of Nuclear Accident Preparedness Education

by Ching-Yi Wu and Yi-Lung Yeh

Eng. Proc. 2025, 89(1), 6; https://doi.org/10.3390/engproc2025089006 - 21 Feb 2025

Viewed by 216

Abstract

Based on the results of the nuclear accident response education questionnaire, we use system dynamics to develop a nuclear accident response education effectiveness evaluation model. A questionnaire survey was conducted for the pre- and post-tests of the course to understand course effectiveness and [...] Read more.

Based on the results of the nuclear accident response education questionnaire, we use system dynamics to develop a nuclear accident response education effectiveness evaluation model. A questionnaire survey was conducted for the pre- and post-tests of the course to understand course effectiveness and explore the interactive relationship between factors. The system dynamics is used to develop a nuclear accident response education effectiveness evaluation model. Age and education are significantly related to education and training experience (number of times), and the age, occupation, and education level are significantly related to individuals’ experience (number of times) of participating in a nuclear accident evacuation exercise. In the workshop courses, the public’s awareness of self-protection against nuclear accidents was significantly improved. Experience in educational training was significantly related to evacuation exercises. Individuals’ age and experience of participating in education and training can be used to predict their willingness to participate in evacuation exercises. Using systems thinking and analysis, an evaluation model for nuclear incident response education effectiveness is constructed as a reference for evaluating effectiveness. The designed education and training courses can increase public participation in nuclear accident response education and strengthen the cognitive benefits of response protection. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 209 KiB

Open AccessProceeding Paper

Advanced Machine Learning for Preschooler Magnetic Resonance Imaging Analysis in Classification of Anxiety Disorders

by Salik Mian, Pranav Kunderu and Shivm Patel

Eng. Proc. 2025, 89(1), 7; https://doi.org/10.3390/engproc2025089007 - 23 Feb 2025

Viewed by 226

Abstract

Numerous individuals suffer from anxiety disorder. Treatments for anxiety usually involve psychologists and counselors based on qualitative data from interviews and conversations to make an educated guess to classify their anxiety. We built a quantitative method for the diagnosis of anxiety, which can [...] Read more.

Numerous individuals suffer from anxiety disorder. Treatments for anxiety usually involve psychologists and counselors based on qualitative data from interviews and conversations to make an educated guess to classify their anxiety. We built a quantitative method for the diagnosis of anxiety, which can be used by psychologists and doctors to obtain accurate data to treat it. The data were obtained from OpenNeuro and Magnetic Resonance Imaging (MRI) scan images of preschoolers with different types of anxiety: generalized anxiety and separation anxiety. These data were used to train machine learning models: Support Vector Machines (SVMs), decision trees, and Logistic Regression. The dataset consisted of MRI images. The method was refined until the desired accuracy was obtained. The model can be used to diagnose anxiety disorders for patients to be treated with a personalized approach. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

12 pages, 24950 KiB

Open AccessProceeding Paper

Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality

by Hung-Hsiang Wang and Ching-Yi Chen

Eng. Proc. 2025, 89(1), 8; https://doi.org/10.3390/engproc2025089008 - 21 Feb 2025

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Abstract

Brand typicality is crucial in shaping consumer perceptions of brands and poses challenges for novice designers to capture due to their limited tacit knowledge. Using Weka’s image classification, we developed a brand product classification model. A dataset with 600 images was obtained from [...] Read more.

Brand typicality is crucial in shaping consumer perceptions of brands and poses challenges for novice designers to capture due to their limited tacit knowledge. Using Weka’s image classification, we developed a brand product classification model. A dataset with 600 images was obtained from Asus and MSI, the leading eSports brands, covering various products such as controllers, mouse devices, headsets, and PC gaming components. The random forest classifier achieved an accuracy of 81 to 85%, slightly higher in the PC gaming category. The design features from Asus ROG and MSI game series products were extracted to generate 36 test images. We used keywords as prompts in Midjurney and Stable Diffusion to generate 36 test images. The developed brand product classification model in this study correctly classified 30 images. However, in the OP category, two graphics card images and one casing image were misclassified. In the PC category, two mouse images and a laptop picture were misclassified. Discrepancies between AI-generated images and personal expertise were improved in terms of the efficiency of the model for new designers. The developed model deepens the understanding of brand characteristics, maintains brand coherence, and strengthens product design innovation and market competitiveness. The model effectively assesses brand characteristics in product appearances using AI, highlighting its role in improving early design processes and new product development strategies. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 2464 KiB

Open AccessProceeding Paper

CAVE Automatic Virtual Environment Technology: A Patent Analysis

by Fatma Beji, William de Paula Ferreira, Isabelle Pivotto Dabat and Vitor Matias

Eng. Proc. 2025, 89(1), 9; https://doi.org/10.3390/engproc2025089009 - 24 Feb 2025

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Abstract

Cave automatic virtual environment (CAVE) technology provides a highly immersive experience in virtual reality (VR) environments, transcending traditional boundaries of VR head-mounted devices. CAVE is applied to many fields, including education, construction, healthcare, and manufacturing. Despite its relevance, studies examining CAVE technology evolution [...] Read more.

Cave automatic virtual environment (CAVE) technology provides a highly immersive experience in virtual reality (VR) environments, transcending traditional boundaries of VR head-mounted devices. CAVE is applied to many fields, including education, construction, healthcare, and manufacturing. Despite its relevance, studies examining CAVE technology evolution and research directions are still lacking. To address this research gap, we analyzed patents using CAVE to understand the technology’s development and identify opportunities for future research, development, and innovation. Patent data were collected from the Lens database and analyzed using data mining techniques. An increasing number of CAVE patents were granted, reflecting significant growth and investments in this field. The results highlight emerging trends in the development of CAVE systems, emphasizing various technical configurations and innovative applications across a wide range of fields. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 431 KiB

Open AccessProceeding Paper

Design of Maximally Permissive Controllers for Solving Deadlock Problems in Flexible Manufacturing Systems

by Yen-Liang Pan, Wen-Yi Chuang, Kuang-Hsiung Tan and Ching-Yun Tseng

Eng. Proc. 2025, 89(1), 10; https://doi.org/10.3390/engproc2025089010 - 24 Feb 2025

Viewed by 233

Abstract

Industry 5.0 aims to integrate humans and machines to achieve greater productivity, personalization, and sustainable development in the production process. Built on the foundation of Industry 4.0 which emphasizes automation, digitalization, and intelligent production processes, Industry 5.0 highlights the importance of human resources [...] Read more.

Industry 5.0 aims to integrate humans and machines to achieve greater productivity, personalization, and sustainable development in the production process. Built on the foundation of Industry 4.0 which emphasizes automation, digitalization, and intelligent production processes, Industry 5.0 highlights the importance of human resources in modern manufacturing. Robotic arms have replaced traditional manpower, particularly in flexible manufacturing systems. However, integrating advanced machinery into workflows has increased competition in terms of securing resources, resulting in frequent deadlocks. Various deadlock prevention policies have been proposed to address this issue. Despite these efforts, resolving system deadlocks while achieving the optimal number of reachable states remains challenging. Based on existing research, we have developed a novel deadlock recovery method applicable to various flexible manufacturing systems. We designed an adaptable system and a controller that can restore the system to its fully operational state. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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9 pages, 2797 KiB

Open AccessProceeding Paper

Improved Control Mechanism of Bottleneck Bandwidth and Round-Trip Propagation Time v3 Congestion with Enhanced Fairness and Efficiency

by Hung-Chi Chu and Hao-Chu Chiang

Eng. Proc. 2025, 89(1), 11; https://doi.org/10.3390/engproc2025089011 - 24 Feb 2025

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Abstract

The widespread adoption and popularity of various applications have led to large and frequent data transmissions, resulting in network congestion, high packet delays, and packet loss. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm to mitigate network congestion. [...] Read more.

The widespread adoption and popularity of various applications have led to large and frequent data transmissions, resulting in network congestion, high packet delays, and packet loss. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm to mitigate network congestion. However, its network fairness is poor. Consequently, BBRv2 and BBRv3 were introduced in 2018 and 2023 as improved versions. Although BBRv2 exhibited enhanced fairness, its bandwidth utilization rate was lower than that of other existing methods. Meanwhile, BBRv3 still lacked bandwidth fairness in its initial transmission. Therefore, we have improved the fairness based on BBRv3 by considering the maximum sending rate and utilizing connections at different times. Good fairness and bandwidth utilization are maintained on the bottleneck bandwidth with the improved method. The method outperforms Cubic Binary Increase Congestion Control (CUBIC) and BBRv3 in terms of bandwidth utilization and network usage fairness. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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11 pages, 2265 KiB

Open AccessProceeding Paper

Synergies Between Virtual Commissioning and Digital Twins

by Hermann Boris Djeulezeck Tanegue, William de Paula Ferreira, Rodrigo Furlan de Assis and David Brodeur

Eng. Proc. 2025, 89(1), 12; https://doi.org/10.3390/engproc2025089012 - 24 Feb 2025

Viewed by 463

Abstract

Virtual commissioning (VC) and digital twins (DTs) are two key enabling technologies of Industry 4.0 (I4.0), which can enhance operational efficiency, optimize resource utilization, streamline the development and deployment of automation systems, and accelerate innovation in manufacturing and industrial processes. However, studies examining [...] Read more.

Virtual commissioning (VC) and digital twins (DTs) are two key enabling technologies of Industry 4.0 (I4.0), which can enhance operational efficiency, optimize resource utilization, streamline the development and deployment of automation systems, and accelerate innovation in manufacturing and industrial processes. However, studies examining the synergy between these two technologies are still lacking. To address this research gap, this study aims to investigate the technological relationships and synergies between VC and DTs in the context of I4.0. The results suggest a strong relationship between VC and DTs since they share similar technological components, such as the digital model. Moreover, the result indicates that the concurrent use of VC and DTs can help leverage both technologies and their added value by increasing model reusability. Different strategies are proposed for combining VC and DT functionalities, highlighting how VC can support DTs, how DTs can support VC, and how both technologies can be integrated. It also provides future research directions. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 4621 KiB

Open AccessProceeding Paper

Semantic Classification of Car Styling Using Machine Learning

by Hung-Hsiang Wang and Yen-Ting Lu

Eng. Proc. 2025, 89(1), 13; https://doi.org/10.3390/engproc2025089013 - 24 Feb 2025

Viewed by 259

Abstract

Product semantics is essential for car styling because it shapes how consumers perceive and interact with cars, influences user experiences, and allows for product differentiation. Although many AI tools are available to assist car designers, research on applying machine learning techniques to evaluate [...] Read more.

Product semantics is essential for car styling because it shapes how consumers perceive and interact with cars, influences user experiences, and allows for product differentiation. Although many AI tools are available to assist car designers, research on applying machine learning techniques to evaluate product semantics is rare. Therefore, we developed a classification model that helps designers identify and evaluate the semantics conveyed by car styling using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool. We used Python web scraping to collect isometric drawings and introductory articles of 1320 SUV cars of various brands from 2009 to 2024 via websites such as Car Body Design and Car Design News. We also summarized four semantic types of car styling, namely “aggressive”, “sporty”, “clean”, and “off-road”, to create the dataset. We used WEKA image classification to randomly select 792 (60%) images from the dataset to train a classification model of car styling semantics. The remaining 528 images (40%) were used for verification. The classification model trained with the Binary Pattern Pyramid Filter and the Random Forest classifier achieved an accuracy of 84.6%. The model was evaluated in terms of whether 10 SUVs created by 10 graduate design students using AI conveyed the anticipated product semantics. Seven of the ten SUVs were correctly classified and the rest were not. All of the participants agreed that the predictions were satisfactory. However, it is necessary to improve the accuracy of each semantic classification, especially the “clean” type. The results of this study demonstrate the capability of machine learning to identify the semantics of car styling effectively, improve the communication and evaluation of product semantics by designers in the design process, and create a car styling with a good appearance that resonates with consumers. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 1299 KiB

Open AccessProceeding Paper

Adopting Artificial Intelligence and Artificial Reality in an Interactive Sign Language Learning System: Acceptance of Interactive Technology

by Kuo-Feng Hwang, Yu-Han Wang, Tsui-Ying Tsao, Shu-Hui Chou, Shih-Ying Chueh, Cheng-Chun Wu and Kuan-Yi Ho

Eng. Proc. 2025, 89(1), 14; https://doi.org/10.3390/engproc2025089014 - 23 Feb 2025

Viewed by 289

Abstract

Growing awareness of inclusivity for people with disabilities has led the National Taiwan Library to promote digital and interactive approaches for improved communication between sign language users and the hearing community. The “Interactive System of Digital Sign Language Picture Books”, developed with augmented [...] Read more.

Growing awareness of inclusivity for people with disabilities has led the National Taiwan Library to promote digital and interactive approaches for improved communication between sign language users and the hearing community. The “Interactive System of Digital Sign Language Picture Books”, developed with augmented reality (AR) and artificial intelligence (AI), enhances inclusive learning experiences. In this study, 82 participants with hearing disabilities aged 31–60 used the system and completed a technology acceptance model questionnaire. Results indicate that ease of use positively influences perceived usefulness, attitude toward use, and intention to use, supporting enriched learning and fostering social communication. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 3066 KiB

Open AccessProceeding Paper

Comparison of Armillary Sphere in Ancient China and Western World

by Jian-Liang Lin and Kuo-Hung Hsiao

Eng. Proc. 2025, 89(1), 15; https://doi.org/10.3390/engproc2025089015 - 25 Feb 2025

Viewed by 390

Abstract

Armillary spheres were developed in the East and the West for a long time. They independently developed various functions for astronomy. In this article, we discuss the differences in mechanical structures, appearance, and functions between the armillary spheres in ancient China and Europe. [...] Read more.

Armillary spheres were developed in the East and the West for a long time. They independently developed various functions for astronomy. In this article, we discuss the differences in mechanical structures, appearance, and functions between the armillary spheres in ancient China and Europe. The earliest armillary sphere in ancient China was invented by Luo Xia Hong (落下閎) between 156 BC and 87 BC. Then, the armillary sphere in ancient China improved with the historical development of astronomy. The famous armillary sphere was built in an astronomical clock tower (水運儀象台) by Su Song (蘇頌) in the Song (宋) dynasty. This armillary sphere was an astronomical apparatus for the observation of celestial phenomena and the correction of time standards. However, the armillary sphere in Europe had different applications, even though the structures were similar. The armillary spheres in Europe simulated the sun’s trajectory in one day to predict the sunrise and sunset positions. They adjusted the tilting angle of the celestial sphere with the altitude of observation to observe the path of the stars around the ecliptic. Through this review, the armillary spheres in ancient China and Europe are defined clearly. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 2588 KiB

Open AccessProceeding Paper

Combining Interactive Technology and Visual Cognition—A Case Study on Preventing Dementia in Older Adults

by Chung-Shun Feng and Chao-Ming Wang

Eng. Proc. 2025, 89(1), 16; https://doi.org/10.3390/engproc2025089016 - 25 Feb 2025

Viewed by 312

Abstract

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to [...] Read more.

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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12 pages, 3503 KiB

Open AccessProceeding Paper

One-Node One-Edge Dimension-Balanced Hamiltonian Problem on Toroidal Mesh Graph

by Yancy Yu-Chen Chang and Justie Su-Tzu Juan

Eng. Proc. 2025, 89(1), 17; https://doi.org/10.3390/engproc2025089017 - 23 Feb 2025

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Abstract

Given a graph G = (V, E), the edge set can be partitioned into k dimensions, for a positive integer k. The set of all i-dimensional edges of G is a subset of E(G) denoted [...] Read more.

Given a graph G = (V, E), the edge set can be partitioned into k dimensions, for a positive integer k. The set of all i-dimensional edges of G is a subset of E(G) denoted by E_i. A Hamiltonian cycle C on G contains all vertices on G. Let E_i(C) = E(C) ∩ E_i. For any 1 ≤ i ≤ k, C is called a dimension-balanced Hamiltonian cycle (DBH, for short) on G if ||E_i(C)| − |E_j(C)|| ≤ 1 for all 1 ≤ i < j ≤ k. The dimension-balanced cycle problem is generated with the 3-D scanning problem. Graph G is called p-node q-edge dimension-balanced Hamiltonian (p-node q-edge DBH) if it has a DBH after removing any p nodes and any q edges. G is called h-fault dimension-balanced Hamiltonian (h-fault DBH, for short) if it remains Hamiltonian after removing any h node and/or edges. The design for the network-on-chip (NoC) problem is important. One of the most famous NoC is the toroidal mesh graph T_m_,n. The DBC problem on toroidal mesh graph T_m_,n is appropriate for designing simple algorithms with low communication costs and avoiding congestion. Recently, the problem of a one-fault DBH on T_m_,n has been studied. This paper solves the one-node one-edge DBH problem in the two-fault DBH problem on T_m_,n. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 7019 KiB

Open AccessProceeding Paper

Multiple Comparisons of the Influential Factors on the Quality of Government Information Systems

by Yi-Luen Lin and Wei-Hsi Hung

Eng. Proc. 2025, 89(1), 18; https://doi.org/10.3390/engproc2025089018 - 26 Feb 2025

Viewed by 183

Abstract

The satisfaction of users is important for the success of government information systems. According to a satisfaction survey of X city’s Road Excavation Management Information System with 221 valid questionnaires in 2023 in Taiwan, however, the rate of those who were dissatisfied and [...] Read more.

The satisfaction of users is important for the success of government information systems. According to a satisfaction survey of X city’s Road Excavation Management Information System with 221 valid questionnaires in 2023 in Taiwan, however, the rate of those who were dissatisfied and felt a lack of systematical enhancement reached 20.81%. In this case, the reason for user dissatisfaction and the related aspects of user dissatisfaction must be discussed. Quality played an important role in influencing user satisfaction. Information, system, and service qualities have been ignored in the past. Yet, few studies have explored the application of these qualities to evaluate the context of government information systems. Government information systems in Taiwan have multiple stakeholders: the authorities concerned (i.e., government agencies), outsourcing contractors (i.e., IT vendors), and users (i.e., public infrastructure pipeline units). In this study, an e-service quality conceptual model and an information system success model were constructed to explore the differences in the quality of government information systems. We identified reasons for dissatisfaction. Using the Analytic Hierarchy Process, we analyzed the data of expert questionnaire surveys. The results showed that the authorities prioritized information quality, the outsourcing contractors prioritized system quality, and the public prioritized service quality. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 794 KiB

Open AccessProceeding Paper

Role of Mathematics Teachers in Learner’s Diversity Using AI Tools

by Wing-Kin Cheng

Eng. Proc. 2025, 89(1), 19; https://doi.org/10.3390/engproc2025089019 - 26 Feb 2025

Viewed by 415

Abstract

The advancement of artificial intelligence (AI) has attracted attention across disciplines. Different research has revealed the role of AI and the outcomes of AI in education (AIEd). However, teachers need to use AI to cater to learner diversities in mathematics education, which needs [...] Read more.

The advancement of artificial intelligence (AI) has attracted attention across disciplines. Different research has revealed the role of AI and the outcomes of AI in education (AIEd). However, teachers need to use AI to cater to learner diversities in mathematics education, which needs exploration. Therefore, how different AI tools assist mathematics teachers in developing teaching materials for students was investigated in this study. Teachers were invited to utilize AI techniques to develop their teaching and learning materials. The findings can be used to enhance the remedial and enrichment measures in teaching secondary mathematics and construct a framework to help teachers with learner diversities with AI tools. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 4932 KiB

Open AccessProceeding Paper

Application of Artificial Intelligence to IELTS Learning

by Yu Hou, Mohamad Ibrani Shahrimin Adam Assim and Shairil Izwan Taasim

Eng. Proc. 2025, 89(1), 20; https://doi.org/10.3390/engproc2025089020 - 26 Feb 2025

Viewed by 431

Abstract

In recent years, artificial intelligence (AI) has emerged as a transformative force in education, notably in language learning. We explored AI’s integration into International English Language Testing System (IELTS) learning environments, focusing on psychological aspects that impact learning outcomes. AI-driven tools, mobile applications, [...] Read more.

In recent years, artificial intelligence (AI) has emerged as a transformative force in education, notably in language learning. We explored AI’s integration into International English Language Testing System (IELTS) learning environments, focusing on psychological aspects that impact learning outcomes. AI-driven tools, mobile applications, and personalized platforms used in IELTS preparation were reviewed and discussed in terms of AI and educational psychology. Emphasis is placed on the benefits and challenges of AI in motivating learners, enhancing autonomy, providing feedback, and ultimately improving proficiency. Insights into how AI can be refined to address psychological and cultural challenges are also provided. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 2251 KiB

Open AccessProceeding Paper

Image Classification Models as a Balancer Between Product Typicality and Novelty

by Hung-Hsiang Wang and Hsueh-Kuan Chen

Eng. Proc. 2025, 89(1), 21; https://doi.org/10.3390/engproc2025089021 - 26 Feb 2025

Viewed by 198

Abstract

Car styling is crucial for consumer acceptance and market success. Since vehicle manufacturers produce electric vehicles, they have faced the challenge of maintaining the typicality of their original products and presenting the innovation of new technologies. We propose a method that integrates artificial [...] Read more.

Car styling is crucial for consumer acceptance and market success. Since vehicle manufacturers produce electric vehicles, they have faced the challenge of maintaining the typicality of their original products and presenting the innovation of new technologies. We propose a method that integrates artificial intelligence (AI)-generated images and image classification technology to help designers effectively balance between typicality and novelty. We collected 118 pictures of electric vehicles and 122 pictures of fuel vehicles in 2024 from the BMW official website. Focusing on seven key visual features of the vehicles, we used the Waikato environment for knowledge analysis (WEKA) to train an image classification model on the dataset through three separate training and testing sessions. First, we used the prompts that described typical BMW design to generate images of new BMW electric vehicles in Stable Diffusion. The images consisted of 21 front views, 20 side views, and 20 rear views. The accuracy of the model of front views trained with the pyramid histogram of oriented gradients filter (PHOG)-Filter and random forest classifier was 78.5%, and the test accuracy reached 95%. The accuracy of the model of rear views trained with BinaryPatternsPyramid-Filter and random forest classifier was 80.5%, and the test accuracy was 90%. However, the accuracy of the model of side views did not reach 70%. That implies the distinction between BMW fuel vehicles and its electric vehicles is mainly based on the front and rear views, rather than the side view. The results of this study showed that integrating image classification and AI-generated images can be used to examine the balance between product typicality and novelty, and the application of machine learning and AI tools to study car style. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 886 KiB

Open AccessProceeding Paper

Research on Optimal Fabrication of High-Quality Ceramic by Process Analysis Technology

by Jianhua Cheng and Minhui Tong

Eng. Proc. 2025, 89(1), 22; https://doi.org/10.3390/engproc2025089022 - 26 Feb 2025

Viewed by 284

Abstract

We developed a composite recognition method combining hybrid sensor recognition and artificial intelligence image recognition techniques and optimized quality monitoring of the ceramic production process. By integrating deep learning models and image preprocessing techniques, appearance defects (such as cracks and color differences) were [...] Read more.

We developed a composite recognition method combining hybrid sensor recognition and artificial intelligence image recognition techniques and optimized quality monitoring of the ceramic production process. By integrating deep learning models and image preprocessing techniques, appearance defects (such as cracks and color differences) were detected in ceramic products, and key parameters in the sintering process using temperature, pressure, and gas-sensitive sensors were monitored in real-time. The developed composite recognition method significantly improved the quality control level of ceramic production, reduced production energy consumption and wastes, and improved production efficiency. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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1034 KiB

Open AccessProceeding Paper

Tool Object Analysis and ChatGPT Integration for Creative Problem-Solving Algorithm Development

by Jung-Suk Hyun and Chan-Jung Park

Eng. Proc. 2025, 89(1), 23; https://doi.org/10.3390/engproc2025089023 - 26 Feb 2025

Viewed by 48

Abstract

TRIZ, the theory of inventive problem solving, can be used to solve contradictions in high-level problems in inventions and patents. Contradictions arise when two contradictory requirements or conditions cannot be satisfied simultaneously and resolving them can lead to innovative ideas. TRIZ experts have [...] Read more.

TRIZ, the theory of inventive problem solving, can be used to solve contradictions in high-level problems in inventions and patents. Contradictions arise when two contradictory requirements or conditions cannot be satisfied simultaneously and resolving them can lead to innovative ideas. TRIZ experts have developed 76 standard solutions for applying TRIZ. Though TRIZ’s 76 standard solutions can be applied to a wide range of problem types, they are too complex. We developed a simplified tool object analysis algorithm using TRIZ’s 76 standard solutions. The analysis was used to analyze the interaction between components of a system to identify where problems occur. The interaction between a tool and an object was categorized into beneficial actions (positive relationship), no effect (zero relationship), and harmful actions (negative relationship). When beneficial actions, and gaps or beneficial actions and harmful actions occur in the interaction between a tool and an object, conflicting relationships arise in problem-solving. We classified the interaction between tools and objects into beneficial actions and gaps and beneficial actions and harmful actions to provide appropriate problem-solving strategies for each situation. The analysis has the advantage of specifying the causes and solutions of problem-solving. The analysis was used in creative problem-solving to train ChatGPT. By training ChatGPT for it, specific problem-solving solutions were obtained. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 660 KiB

Open AccessProceeding Paper

Investigating the Effect of Performing Secondary Tasks on Reaction Time While Driving by Computer Analysis

by Chia-Wen Tsai and Dengchuan Cai

Eng. Proc. 2025, 89(1), 24; https://doi.org/10.3390/engproc2025089024 - 27 Feb 2025

Viewed by 245

Abstract

Performing secondary tasks affects the performance of primary tasks. Engaging in secondary tasks while driving often leads to accidents. Therefore, this study aimed to investigate the impact on reaction time when drivers perform secondary tasks while driving. The participants of this study were [...] Read more.

Performing secondary tasks affects the performance of primary tasks. Engaging in secondary tasks while driving often leads to accidents. Therefore, this study aimed to investigate the impact on reaction time when drivers perform secondary tasks while driving. The participants of this study were 30 participants, including 11 males and 19 females. During driving, participants watched a driving video. When a warning triangle appeared in the center of the road, participants immediately pressed the Enter button. When the participant pressed the Enter button, the computer automatically recorded the time of the button press. In a within-subjects design, all participants took part in secondary tasks during the primary driving task, and outcomes were compared between secondary tasks (no task, conversation with the passenger, and listening to the radio). The results show that the reaction time during conversation with the passenger in the primary task was significantly longer than that during listening to the radio or having no task. However, there was no significant difference in reaction time between having no task and listening to the radio. The average reaction time with no task was longer than while listening to the radio. Fatigue increased the reaction time for no task. Having a conversation with the passenger did not affect the average reaction time for both tests due to the conversation with the passenger mitigating the effects of fatigue. The results of this study provide a reference for further research on driving behavior. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 13668 KiB

Open AccessProceeding Paper

Internet of Things and Autonomous Robots to Develop Intelligent Solutions for Sterilization and Disease Prevention

by Ling-Hsiang Hung, Zong-Jie Wu, Chu-Hwa Yan and Chien-Liang Chen

Eng. Proc. 2025, 89(1), 25; https://doi.org/10.3390/engproc2025089025 - 27 Feb 2025

Viewed by 269

Abstract

As the epidemic affected everyone across the world, the solution to the epidemic was developed globally. Many applications adopt Internet of Things (IoT) technology to detect epidemics, and effective monitoring systems are developed to monitor air pollution, personal transmission, early detection of serious [...] Read more.

As the epidemic affected everyone across the world, the solution to the epidemic was developed globally. Many applications adopt Internet of Things (IoT) technology to detect epidemics, and effective monitoring systems are developed to monitor air pollution, personal transmission, early detection of serious cases, and remote assessment. However, care facilities in an aging society require effective disinfection and sterilization to prevent viral transmission. We integrated the interactive and real-time features of the Internet of Things (IoT) to design and build an intelligent self-propelled sterilization robot for sterilization. Intelligent sterilization and disinfection planning and task allocation mechanisms were designed for sterilization in clinics. For healthcare facilities, the developed robot can reduce the burden on healthcare professionals, help to manage the disinfection and sterilization process, and ensure patient safety. At the same time, robots promote the development of epidemic prevention industries and prepares for future attacks from harmful air pollutants or new infections. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 2697 KiB

Open AccessProceeding Paper

Image Classification to Identify Style Composition Ratios in Crossover Cars

by Hung-Hsiang Wang and Hung-Jui Su

Eng. Proc. 2025, 89(1), 26; https://doi.org/10.3390/engproc2025089026 - 27 Feb 2025

Viewed by 308

Abstract

As the global demand for multifunctional and high-performance vehicles increases, automotive manufacturers face significant challenges in designing new crossover models. Consumers expect vehicles to blend features from various car models, which pushes the industry to adopt innovative design tools and methods. We explored [...] Read more.

As the global demand for multifunctional and high-performance vehicles increases, automotive manufacturers face significant challenges in designing new crossover models. Consumers expect vehicles to blend features from various car models, which pushes the industry to adopt innovative design tools and methods. We explored the use of Waikato Environment for Knowledge Analysis (WEKA) image classification to predict the style composition ratios of sedans, hatchbacks, multi-purpose vehicles (MPVs), and sport utility vehicles (SUVs) as crossover vehicles. We collected 240 high-resolution side-view images of luxury vehicles from brands including Mercedes-Benz, BMW, and Lexus, and preprocessed the data using format unification and feature enhancement. We employed WEKA to extract image features and train a classification model using the edge histogram filter and sequential minimal optimization (SMO) classifier, which achieved an 86% classification accuracy. Subsequently, we used Vizcom, a generative Artificial Intelligence(AI) tool, to simulate realistic designs for new crossover cars and predict their style composition ratios. The proposed designs were evaluated by five experts, who found that the model accurately identified style composition ratios and helped designers create new car styles with market potential. The novel application of image classification can be used for analyzing blended styles in automotive design and enables designers to identify, evaluate, and control styles to meet market demands. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 1118 KiB

Open AccessProceeding Paper

Response Measures to Climate Change to Maintain Resilient Rice Production in Taiwan

by Chin-Song Wu and Tzu-Che Lin

Eng. Proc. 2025, 89(1), 27; https://doi.org/10.3390/engproc2025089027 - 27 Feb 2025

Viewed by 251

Abstract

Recently, the intensification of the contrast between Taiwan’s wet and dry seasons due to climate change has led to stringent water use restrictions in agriculture, significantly impacting Taiwan’s primary staple crop, rice. This necessitates the development of effective agricultural management strategies to ensure [...] Read more.

Recently, the intensification of the contrast between Taiwan’s wet and dry seasons due to climate change has led to stringent water use restrictions in agriculture, significantly impacting Taiwan’s primary staple crop, rice. This necessitates the development of effective agricultural management strategies to ensure the resilience of Taiwan’s agriculture. Therefore, we simulated potential challenges in rice cultivation due to climate change under specific conditions: flooded cultivation and soil water tension levels of −20 and −40 kPa. At −40 kPa, the soil becomes excessively dry, causing severe soil surface cracking. This results in a 20% reduction in plant height and a 30% decrease in yield compared to flooded cultivation. At −20 kPa, plant height and yield are comparable to those under flooded conditions. In resource efficiency, flooded cultivation demonstrates low irrigation water use efficiency (0.22 and 0.42 kg/m³) due to sustained high water levels. Conversely, the condition with −20 kPa shows the highest irrigation water use efficiency (2.15 and 2.16 kg/m³) with no significant difference in nitrogen use efficiency compared to flooded management. Although the irrigation water use efficiency under −40 kPa is better than flooded management (0.87 and 1.02 kg/m³), the nitrogen utilization efficiency is significantly low. Irrigation under the condition of −20 kPa climate change does not reduce yields and offers additional benefits. This strategy ensures stable crop production and conserves water resources for crop cultivation during the dry season, providing an effective means to stabilize production and mitigate the impacts of climate change on Taiwan’s agriculture. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 1656 KiB

Open AccessProceeding Paper

Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home

by Sinan Chen and Masahide Nakamura

Eng. Proc. 2025, 89(1), 28; https://doi.org/10.3390/engproc2025089028 - 1 Mar 2025

Viewed by 180

Abstract

This study aims to enhance human pose detection performance through image preprocessing. With the growing global elderly population, supporting in-home elderly individuals has become increasingly crucial, especially in Japan, where approximately 90% of the elderly live at home. In this study, abnormal behaviors [...] Read more.

This study aims to enhance human pose detection performance through image preprocessing. With the growing global elderly population, supporting in-home elderly individuals has become increasingly crucial, especially in Japan, where approximately 90% of the elderly live at home. In this study, abnormal behaviors in in-home elderly individuals were detected using high-precision pose detection technologies, such as the MoveNet model, based on machine learning. However, there was a discrepancy in pose detection accuracy between the standing and lying positions. Hence, preprocessing techniques such as image rotation, flipping, resizing, and noise reduction were applied, and their effects were analyzed in detail. As a result, an improvement in pose detection accuracy for most body parts, except for specific regions, was observed. By constructing an optimal preprocessing pipeline, it is expected to reduce false detection and missed detection, contributing to the practical application of pose detection technology. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 182 KiB

Open AccessProceeding Paper

Impact of Customer Perceptions of Virtual Influencers on Value Co-Creation in Streaming Platforms

by Jing-Wen Huang, Shan-Lin Yang and Shao-Lan Chang

Eng. Proc. 2025, 89(1), 29; https://doi.org/10.3390/engproc2025089029 - 27 Feb 2025

Viewed by 279

Abstract

The continuous advancement and application of generative artificial intelligence (AI) allows for virtual influencers to be active. Therefore, how virtual influencers respond to customer needs and create value has attracted the interest of streaming platforms. Using the stimulus–organism–response model, we explored how customer [...] Read more.

The continuous advancement and application of generative artificial intelligence (AI) allows for virtual influencers to be active. Therefore, how virtual influencers respond to customer needs and create value has attracted the interest of streaming platforms. Using the stimulus–organism–response model, we explored how customer perceptions of virtual influencers affect their involvement in streaming platforms and value co-creation behavior. Customer perceptions of virtual influencers are considered as the stimulus, including customer preferences, interactivity, and functionality. Enduring engagement is viewed as the psychological organism, and value co-creation is regarded as the behavioral response. A questionnaire survey was used to investigate the customer perceptions of virtual influencers on streaming platforms or applications to link their experiences into a virtual world and enhance value co-creation. Customer perceptions, including preferences, interactivity, and functionality, have positive effects on enduring involvement and value co-creation. Enduring involvement affects the relationship between customer perceptions and value co-creation. Such results help to understand customer perceptions and behaviors toward virtual influencers to enhance the service and user experience of streaming platforms. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

9 pages, 1993 KiB

Open AccessProceeding Paper

Synthetic Image Data Generation for Wiring Harness Component Detection Using Machine Learning

by Huong Giang Nguyen, Patrick Bründl and Jörg Franke

Eng. Proc. 2025, 89(1), 30; https://doi.org/10.3390/engproc2025089030 - 3 Mar 2025

Viewed by 362

Abstract

Machine learning is a powerful tool for computer vision tasks in manufacturing, as features are automatically extracted and a high variety of components or failures are reliably detected. A focal prerequisite for high-performing machine learning models is a database that is large in [...] Read more.

Machine learning is a powerful tool for computer vision tasks in manufacturing, as features are automatically extracted and a high variety of components or failures are reliably detected. A focal prerequisite for high-performing machine learning models is a database that is large in quantity as well as quality, and representative for the computer vision task in the manufacturing environment. In addition, manufacturing applications require a domain-specific dataset. Thus, we generated and integrated synthetic data for object detection using convolutional neural networks, specifically for wiring harness component detection. A synthetic data generation pipeline for images was developed and implemented. Experiments were conducted to assess the domain gap between synthetic and real images and to determine factors that are beneficial to synthetic data generation. The experimental findings demonstrate relevant training approaches to integrate synthetic data, factors that have a positive impact on training, and high-performance results comparable to using real data only. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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11 pages, 1974 KiB

Open AccessProceeding Paper

Chip Design of Multithreaded and Pipelined RISC-V Microcontroller Unit

by Mao-Hsu Yen, Yih-Hsia Lin, Tzu-Feng Lin, Yu-Hui Chen, Yuan-Fu Ku and Chien-Ting Kao

Eng. Proc. 2025, 89(1), 31; https://doi.org/10.3390/engproc2025089031 - 28 Feb 2025

Viewed by 335

Abstract

Multithreading is widely used in microcontroller unit (MCU) chips. Multithreaded hardware is composed of multiple identical single threads and provides instructions to different threads. Using the concept of thread-level parallelism (TLP), pauses are compensated for during single-thread operation to increase the throughput at [...] Read more.

Multithreading is widely used in microcontroller unit (MCU) chips. Multithreaded hardware is composed of multiple identical single threads and provides instructions to different threads. Using the concept of thread-level parallelism (TLP), pauses are compensated for during single-thread operation to increase the throughput at the same unit. The principle of pipelined management is to use instruction-level parallelism (ILP) to split the MCU into multiple stages. When an instruction is given in a certain stage, other instructions are provided to operate in other idle stages and improve their execution efficiency. Based on the four-thread and pipelined RISC-V MCU architecture, we analyzed the instruction types of three benchmarks, i.e., Coremark, SHA, and Dijkstra. A total of 94% of the instructions use the arithmetic logic unit (ALU). Based on the executable four-thread architecture, we developed two to four RISC-V architectures with different numbers of ALUs and a dispatch algorithm. This architecture allows for the simultaneous delivery of multiple instructions, enabling parallel processing of instructions and increasing efficiency. Compared to the traditional RISC-V architecture with only one ALU, the test results showed that the instructions per clock (IPCs) of RISC-V architectures with two, three, and four ALUs increased efficiency by 76, 128.9, and 154.3%, while the area increased by 12, 22.3, and 32.6% and the static power consumption increased by 5.1, 9.2, and 13.3%. The results showed a significant improvement in performance with only a slight increase in the area. Due to the limited area of chips, a two-thread microcontroller architecture was used for the IC design and tape-out. TSMC’s 180nm process with a chip area of 1190 × 1190 μm at 133 MHz was used in this study. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 3062 KiB

Open AccessProceeding Paper

The Use of Support Vector Machine to Classify Potential Customers for the Wealth Management of a Bank

by Chien-Hung Lai, Yi Lin, Ju-Wen Hsieh and Yuh-Shyan Hwang

Eng. Proc. 2025, 89(1), 32; https://doi.org/10.3390/engproc2025089032 - 3 Mar 2025

Viewed by 349

Abstract

We developed a method for the evaluation and selection of customer business analysis in two stages. First, using the bank’s existing expert model, artificial rules of thumb were used to evaluate the value of each field of the data and establish screening rules. [...] Read more.

We developed a method for the evaluation and selection of customer business analysis in two stages. First, using the bank’s existing expert model, artificial rules of thumb were used to evaluate the value of each field of the data and establish screening rules. Secondly, the machine learning feature screening method was applied based on the customer’s transaction data to find out whether the customer’s contribution to the bank had a significant impact as a feature of the model. Based on the results, the best classification model was selected through data verification. The effectiveness of the proposed model was validated through actual case analysis, taking wealth management in banks as an example. The classification method, using support vector machines (SVMs), effectively assists banks in identifying potential customers efficiently and in planning to manage customers. This method helps to avoid the traditional blind spots, which emerge based on subjective judgment, and allows bank wealth managers to promote customer relationship management (CRM). Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 7488 KiB

Open AccessProceeding Paper

Enhancing Fabric Detection and Classification Using YOLOv5 Models

by Makara Mao, Jun Ma, Ahyoung Lee and Min Hong

Eng. Proc. 2025, 89(1), 33; https://doi.org/10.3390/engproc2025089033 - 3 Mar 2025

Viewed by 331

Abstract

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a [...] Read more.

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a method for fabric type classification and object detection using the YOLOv5 architecture. The model was trained on a diverse dataset containing images of different fabrics, including cotton, hanbok, dyed cotton yarn, and a plain cotton blend. We conducted a dataset preparation process, including data collection, annotation, and training procedures for data augmentation to improve model generalization. The model’s performance was evaluated using precision, recall, and F1-score. The developed model detected and classified fabrics with an accuracy of 81.08%. YOLOv5s allowed a faster performance than other models. The model can be used for automated quality control, inventory tracking, and retail analytics. The deep learning-based object detection method with YOLOv5 addresses challenges related to fabric classification, improving the abilities and productivity of manufacturing and operations. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 3547 KiB

Open AccessProceeding Paper

Preparation and Electrochemical Properties of Molybdenum Disulfide Nanomaterials

by Pin-Syuan Chen, Yi Hu, Si-Ying Li, Marta Mazurkiewicz-Pawlicka and Artur Małolepszy

Eng. Proc. 2025, 89(1), 34; https://doi.org/10.3390/engproc2025089034 - 6 Mar 2025

Viewed by 253

Abstract

As a transition metal chalcogenide, molybdenum disulfide is an important two-dimensional material. Due to its structural anisotropy, its different morphological structures impact performance. Therefore, improving existing preparation methods enhances its applications. Single-layer molybdenum disulfide is a direct bandgap semiconductor with excellent mechanical properties [...] Read more.

As a transition metal chalcogenide, molybdenum disulfide is an important two-dimensional material. Due to its structural anisotropy, its different morphological structures impact performance. Therefore, improving existing preparation methods enhances its applications. Single-layer molybdenum disulfide is a direct bandgap semiconductor with excellent mechanical properties and chemical stability. We chose ammonium molybdate as the molybdenum source and L-cysteine as the sulfur source. By changing the pH and the reaction time in the environment, the hydrothermal method is used to synthesize the precursor and molybdenum disulfide with different morphologies to control its morphology. Electrochemical test results showed that the specific capacity of molybdenum disulfide synthesized at a current density of 0.6 A reaches 187.79 F/g at a reaction time of 24 h and a pH of 0.6. Its microstructure is in the shape of a flower ball, with a single piece size of about 50 nm and a thickness of about 5 nm. Its specific surface area reaches 36.88 m²/g, which provides enough active sites. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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8 pages, 5504 KiB

Open AccessProceeding Paper

Electrochromic Behavior of Manganese Oxides/Silver Thin Films from Electrochemical Deposition

by Yi Hu, Jiun-Shing Liu, Pin-Syuan Chen and Si-Ying Li

Eng. Proc. 2025, 89(1), 35; https://doi.org/10.3390/engproc2025089035 - 5 Mar 2025

Viewed by 220

Abstract

MnO_x thin films with silver additives were electrochemically deposited on an Indium Tin Oxide (ITO) substrate with silver acetate and potassium permanganate aqueous solution. The addition of Ag enhanced electrochromic behavior during cyclic voltammetry (CV). The morphology of the thin films was [...] Read more.

MnO_x thin films with silver additives were electrochemically deposited on an Indium Tin Oxide (ITO) substrate with silver acetate and potassium permanganate aqueous solution. The addition of Ag enhanced electrochromic behavior during cyclic voltammetry (CV). The morphology of the thin films was examined by using scanning electronic microscopy (SEM) and transmission electron microscopy (TEM). The chemical states of Mn and Ag ions on the surfaces of the thin films were examined using X-ray photoelectron spectroscopy (XPS). Spherical Ag₂O and Ag nanoparticles were homogeneously dispersed on the thin films. The electrochemistry of the thin films was examined by cyclic voltammetry in a conventional three-electrode system and an electrochemically tested system. The electrochromic behavior of the films was demonstrated through the cyclic voltammetry (CV) process in the KNO₃ electrolyte. The electrochromic behavior of the thin films depended on the redox reactions associated with the reaction between Ag and Ag₂O coupled with Mn⁴⁺ ions and Mn³⁺ ions in the KNO₃ electrolyte. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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11 pages, 3069 KiB

Open AccessProceeding Paper

Enhanced Comparative Analysis of Pretrained and Custom Deep Convolutional Neural Networks for Galaxy Morphology Classification

by Tram Le, Nickson Ibrahim, Thu Nguyen, Thanyaporn Noiplab, Jungyoon Kim and Deepshikha Bhati

Eng. Proc. 2025, 89(1), 36; https://doi.org/10.3390/engproc2025089036 - 11 Mar 2025

Viewed by 273

Abstract

Galaxy morphology classification is a crucial task in astronomy and astrophysics, providing information on galaxy formation and evolution. Traditionally, this classification has been a manual and labor-intensive process requiring significant astronomical expertise. However, advancements in artificial intelligence, particularly deep learning, offer more efficient [...] Read more.

Galaxy morphology classification is a crucial task in astronomy and astrophysics, providing information on galaxy formation and evolution. Traditionally, this classification has been a manual and labor-intensive process requiring significant astronomical expertise. However, advancements in artificial intelligence, particularly deep learning, offer more efficient and accurate solutions. We investigated the application of convolutional neural networks (CNNs) for galaxy morphology classification using the Galaxy10 DECals dataset. We developed and compared three models: a custom-built CNN using TensorFlow 2.18, a ResNet50 model initialized with random weights, and a pre-trained EfficientNetB5 model utilizing transfer learning, both implemented in PyTorch 2.6. Our results indicate that the custom model achieves an accuracy of 67%, while the ResNet50 and EfficientNetB5 models achieve 80 and 87% accuracy, respectively. The superior performance of the pre-trained Efficient-NetB5 model underscores the efficacy of transfer learning in astronomical image classification. These findings have significant implications for the application of deep learning techniques in astrophysical research. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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7 pages, 12219 KiB

Open AccessProceeding Paper

Fast Collision Detection Method with Octree-Based Parallel Processing in Unity3D

by Kunthroza Hor, Taeheon Kim and Min Hong

Eng. Proc. 2025, 89(1), 37; https://doi.org/10.3390/engproc2025089037 - 13 Mar 2025

Viewed by 253

Abstract

Performing accurate and precise collision detection is a key to real-time applications in computer graphics, games, physical-based simulation, virtual reality, augmented reality, and research and development. Researchers have developed numerous methods to minimize computation time and enhance the accuracy of collision detection for [...] Read more.

Performing accurate and precise collision detection is a key to real-time applications in computer graphics, games, physical-based simulation, virtual reality, augmented reality, and research and development. Researchers have developed numerous methods to minimize computation time and enhance the accuracy of collision detection for pair-object collisions. Although the performance of the central processing unit (CPU) has significantly improved in recent years, it is still insufficient for many applications. In this study, we have developed an improved algorithm for geometric bounding volume hierarchy (BHV) in 3D spatial subdivisions using an Octree-based axis-aligned bounding box (AABB) structure. The AABB structure is used for collision detection and its computation by the central processing unit and graphic processing unit (GPU), which is implemented on the compute shader in Unity3D. AABB was defined as the maximum and minimum hexahedron within an object that is parallel to the coordinate axis. While GPU computing is essential for enhancing the object’s performance. The proposed algorithm approaches Octree AABB-based GPU parallel processing to reduce the calculation or process of simulation for real-time collision detection and handles multiple computations. In the CPU environment, the algorithm spent 2.9 fps when simulating up to 20 objects of the Torus Model that contains 2.3 K vertices and 4.6 K triangles. In the GPU environment, it spent 635.62 fps with 20 objects, and the maximum number increased to 180 objects in real-time. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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9 pages, 1973 KiB

Open AccessProceeding Paper

Recommender System for Apparel Products Based on Image Recognition Using Convolutional Neural Networks

by Chin-Chih Chang, Chi-Hung Wei, Yen-Hsiang Wang, Chyuan-Huei Thomas Yang and Sean Hsiao

Eng. Proc. 2025, 89(1), 38; https://doi.org/10.3390/engproc2025089038 - 14 Mar 2025

Viewed by 343

Abstract

In e-commerce and fashion, personalized recommendations are used to enhance user experience and engagement. In this study, an apparel recognition and recommender system (ARRS) using convolutional neural networks (CNNs) was employed to analyze apparel images, extract features, and provide accurate recognition and recommendations. [...] Read more.

In e-commerce and fashion, personalized recommendations are used to enhance user experience and engagement. In this study, an apparel recognition and recommender system (ARRS) using convolutional neural networks (CNNs) was employed to analyze apparel images, extract features, and provide accurate recognition and recommendations. By learning patterns and features of clothes, the system enables robust recognition and personalized suggestions. The effectiveness of ARRS in recognizing apparel and generating relevant recommendations was validated. The system enhances user satisfaction and engagement on fashion e-commerce platforms. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 2881 KiB

Open AccessProceeding Paper

Comparison of Ultraviolet A/B and C Irradiation for Exosome Secretion Enhancement in HEK 293T Cell

by Ching-Chih Chan, Pohao Lin, Yi Xian, Ruey-Hwang Chou and Yi-Jui Liu

Eng. Proc. 2025, 89(1), 39; https://doi.org/10.3390/engproc2025089039 - 17 Mar 2025

Viewed by 220

Abstract

Exosomes, extracellular vesicles known for their stability, low immunogenicity, and excellent tissue penetration, are employed as delivery vehicles. These exosomes can traverse the tumor barrier and deliver therapeutic agents directly into pancreatic cancer cells. Targeted exosome vectors containing gene fragments to inhibit Kirsten [...] Read more.

Exosomes, extracellular vesicles known for their stability, low immunogenicity, and excellent tissue penetration, are employed as delivery vehicles. These exosomes can traverse the tumor barrier and deliver therapeutic agents directly into pancreatic cancer cells. Targeted exosome vectors containing gene fragments to inhibit Kirsten rat sarcoma viral oncogene homolog (KRAS) activity are crucial for treating pancreatic tumors. Therefore, the content of the exosomes is critical. This study aims to compare the function of exosomes released by HEK-293T cells when exposed to ultraviolet A/B and ultraviolet C irradiation to determine its impact. HEK-293T cells were irradiated with ultraviolet A/B, and ultraviolet C for various indicated times, after which the cell count and exosome secretion were measured. Exosomes derived from HEK-293T cells were isolated through differential centrifugation and identified using four methods: cell counting, Bradford assay, nanoparticle tracking analysis (NTA), and Western blot analysis. Preliminary studies demonstrated that the cell count and Bradford assay expression were reduced in ultraviolet C compared to the control, with similar levels observed for ultraviolet A/B and the control. Exosome expression in Western blot analysis showed ultraviolet C, but a higher amount of ultraviolet A/B compared to the control. We introduce a comprehensive approach to ultraviolet irradiation, including ultraviolet A/B and ultraviolet C, which enhanced the secretion of exosomes by HEK-293T targeted vectors for KRAS inhibition in pancreatic cancer. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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10 pages, 3532 KiB

Open AccessProceeding Paper

RoBuCACO: ChatGPT-Based Educational Model for Creative Problem-Solving

by Jung-Suk Hyun and Chan-Jung Park

Eng. Proc. 2025, 89(1), 40; https://doi.org/10.3390/engproc2025089040 - 20 Mar 2025

Viewed by 198

Abstract

Generative artificial intelligence (AI), including ChatGPT4o, is increasingly used across various sectors such as education. In this article, we introduce a new educational model, RoBuCACO, which combines the Butterfly Model for creative problem-solving in a ChatGPT-based framework. ChatGPT is trained on the Butterfly [...] Read more.

Generative artificial intelligence (AI), including ChatGPT4o, is increasingly used across various sectors such as education. In this article, we introduce a new educational model, RoBuCACO, which combines the Butterfly Model for creative problem-solving in a ChatGPT-based framework. ChatGPT is trained on the Butterfly Model using Korean patent data to generate patent metadata. Users follow a structured learning process that includes the definition of roles for ChatGPT (Ro), learning the Butterfly Model (Bu), defining problems and contradictions (C), developing both abstract (A) and concrete solutions (C), and refining optimal (O) solutions. Korean patent metadata are used to obtain concrete solutions and collaborate with ChatGPT to iteratively refine optimal solutions. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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11 pages, 8074 KiB

Open AccessProceeding Paper

The (n, n) Visual Multi-Secrets Sharing Scheme with Abilities of OR and XOR Decryption

by An-Hui Lo and Justie Su-Tzu Juan

Eng. Proc. 2025, 89(1), 41; https://doi.org/10.3390/engproc2025089041 - 25 Mar 2025

Viewed by 92

Abstract

Naor and Shamir introduced the fundamental concept of visual cryptography (VC) in 1994. In that model, the secret image is split into two meaningless shares, allowing the secret to be revealed and recognized by the human eye just by superimposing the two shares. [...] Read more.

Naor and Shamir introduced the fundamental concept of visual cryptography (VC) in 1994. In that model, the secret image is split into two meaningless shares, allowing the secret to be revealed and recognized by the human eye just by superimposing the two shares. Since then, many scholars have studied the VC problem. To improve the efficiency of transmitting secrets, multi-secret visual cryptography has been proposed to encrypt multiple secret images at the same time. On the other hand, with the reduction in hardware costs, research on XOR-based VC has become popular to address the issue of poor image quality in the recovered image of OR-based visual cryptography, though it requires computing equipment. Scholars also have developed VC schemes utilizing OR-based decryption (equivalent to traditional VC) and XOR-based decryption. These schemes recover secrets without additional hardware (using OR-based decryption) and provide higher-quality images when extra hardware is available (using XOR-based decryption). We propose a visual multi-secret sharing scheme (VMSSS) to encrypt multiple secret images into n (>1) shares. When all shares are collected, all original secret images are decrypted by using the OR or XOR operation. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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13 pages, 2161 KiB

Open AccessProceeding Paper

Review of Electronic Cooling and Thermal Management in Space and Aerospace Applications

by Kivilcim Ersoy

Eng. Proc. 2025, 89(1), 42; https://doi.org/10.3390/engproc2025089042 - 26 Mar 2025

Viewed by 393

Abstract

The continuous miniaturization of electronics, high processing capacity, compact microelectronic devices, and high circuit density contribute to an increasing demand for the efficient cooling of electronics. For aerospace and space applications, where packaging and the optimal use of space, weight, and power are [...] Read more.

The continuous miniaturization of electronics, high processing capacity, compact microelectronic devices, and high circuit density contribute to an increasing demand for the efficient cooling of electronics. For aerospace and space applications, where packaging and the optimal use of space, weight, and power are important, adequate and efficient cooling is a limiting factor due to the increased heat flux rates from compact-design electronic units. As a technology enabler, thermal management applications become important with the increasing demand for longer component operation times. This study aims to review the literature and the analysis results of thermal engineering applications on cooling of electronics and thermal management approaches in space and aerospace applications. Many advanced cooling applications with interdisciplinary advancements and their benefits are discussed. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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9 pages, 3190 KiB

Open AccessProceeding Paper

Predicting Hit Songs Using Audio and Visual Features

by Cheng-Yuan Lee and Yi-Ning Tu

Eng. Proc. 2025, 89(1), 43; https://doi.org/10.3390/engproc2025089043 - 28 Mar 2025

Viewed by 380

Abstract

Factors contributing to a song’s popularity are explored in this study. Recent studies have mainly focused on using acoustic features to identify popular songs. However, we combined audio and visual data to make predictions on 1000 YouTube songs. In total, 1000 songs were [...] Read more.

Factors contributing to a song’s popularity are explored in this study. Recent studies have mainly focused on using acoustic features to identify popular songs. However, we combined audio and visual data to make predictions on 1000 YouTube songs. In total, 1000 songs were grouped into two categories based on YouTube view counts: popular and non-popular. The visual features were extracted using OpenCV. These features were applied using machine learning algorithms, including random forest, support vector machines, decision trees, K-nearest neural networks, and logistic regression. Random forest performed the best, with an accuracy of 82%. Average accuracy increased by 9% in all models when using audio and visual features together. This indicates that visual elements are beneficial for identifying hit songs. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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11 pages, 8265 KiB

Open AccessProceeding Paper

Development of Innovative 3D Spherical Retrieval System and Virtual Reality for Insomnia Prescriptions in Traditional Chinese Medicine

by Chia-Hui Shih, Geng-Hao Liu and Ting-An Fan

Eng. Proc. 2025, 89(1), 44; https://doi.org/10.3390/engproc2025089044 - 28 Mar 2025

Viewed by 154

Abstract

Insomnia is prevalent in modern society, and traditional Chinese medicine is gradually replacing Western medicine in its treatment. This study utilized insomnia symptoms and prescriptions from the “Dictionary of Chinese Medicine Prescriptions” to establish a 3D TCM spherical retrieval system, which intuitively displays [...] Read more.

Insomnia is prevalent in modern society, and traditional Chinese medicine is gradually replacing Western medicine in its treatment. This study utilized insomnia symptoms and prescriptions from the “Dictionary of Chinese Medicine Prescriptions” to establish a 3D TCM spherical retrieval system, which intuitively displays the relationship between TCM prescriptions and insomnia symptoms. It enhances public and student interest in learning about TCM. Survey results indicate that the system effectively improves public knowledge of TCM and supports the United Nations Sustainable Development Goal 4: Quality Education. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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9 pages, 1511 KiB

Open AccessProceeding Paper

Digital Twin for Developing and Verifying Semiconductor Packaging License Models

by Lai-Chung Lee, Shou-Yen Zhao and Whei-Jane Wei

Eng. Proc. 2025, 89(1), 45; https://doi.org/10.3390/engproc2025089045 - 15 Apr 2025

Viewed by 165

Abstract

The traditional semiconductor packaging training process is time-consuming and carries the risk of damaging precision equipment due to improper operation. Additionally, the retirement of experienced trainers has led to loss of specialized training and testing expertise. To address these challenges, digital twin technology [...] Read more.

The traditional semiconductor packaging training process is time-consuming and carries the risk of damaging precision equipment due to improper operation. Additionally, the retirement of experienced trainers has led to loss of specialized training and testing expertise. To address these challenges, digital twin technology is applied to training packaging engineers. We conducted an empirical study at the packaging production line of the Minghsin University of Science and Technology to address talent training bottlenecks and imbalances between supply and demand. First, an integrated software and hardware system was designed by combining digital twin and mixed reality (MR). The development process of the digital twin system for the wafer-dicing machine includes on-site visits, machine operation instructions, certification content development, expert validity construction, small-scale testing and modifications. We compared the pre- and post-experiment scores of industry experts to evaluate the operation time of five participants and their feedback. Digital twin and MR for simulated training increased proficiency in operation. The digital twin training and certification model developed in this study improved students’ pass rates in certification exams. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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6 pages, 299 KiB

Open AccessProceeding Paper

Three-Dimensional Creation and Physical Movement in Art Therapy Using Virtual Reality Painting

by Chia-Chieh Lee and Min-Chai Hsieh

Eng. Proc. 2025, 89(1), 46; https://doi.org/10.3390/engproc2025089046 - 17 Apr 2025

Viewed by 91

Abstract

Virtual Reality (VR) painting, an emerging form of artistic expression under 5G technology, showcases a broader range of expressive styles and dynamic visual effects compared to traditional computer graphics. The creative process in VR painting enhances spatial depth, exhibiting different spatial abilities and [...] Read more.

Virtual Reality (VR) painting, an emerging form of artistic expression under 5G technology, showcases a broader range of expressive styles and dynamic visual effects compared to traditional computer graphics. The creative process in VR painting enhances spatial depth, exhibiting different spatial abilities and necessitating more physical movements, including hand controllers and foot movements in the virtual space. Furthermore, VR painting in art therapy encourages users to engage in physical activities, contributing to better emotional expression. This study involved digital-native users in VR painting, using Meta Quest 2 to operate Open Brush for their creations. Through observational methods, we examined user operational behaviors and conducted semi-structured interviews post-experiment to explore their painting performance and usage behaviors in the virtual environment. The results of this study indicate that VR painting enhances the sense of space and dynamic expression in creative work and improves users’ emotional and physical engagement, providing new avenues for artistic expression. These findings contribute to improving the usability and application value of VR paintings. Full article

(This article belongs to the Proceedings of 2024 IEEE 7th International Conference on Knowledge Innovation and Invention)

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