Journal Description
Applied System Innovation
Applied System Innovation
is an international, peer-reviewed, open access journal on integrated engineering and technology. The journal is owned by the International Institute of Knowledge Innovation and Invention (IIKII) and is published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and other databases.
- Journal Rank: CiteScore - Q1 (Applied Mathematics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.7 days after submission; acceptance to publication is undertaken in 4.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.8 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
Appl. Syst. Innov. 2024, 7(2), 25; https://doi.org/10.3390/asi7020025 - 18 Mar 2024
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Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections.
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Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.
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Open AccessArticle
Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control
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Lucas Schmidt Goecks, Anderson Felipe Habekost, Antonio Maria Coruzzolo and Miguel Afonso Sellitto
Appl. Syst. Innov. 2024, 7(2), 24; https://doi.org/10.3390/asi7020024 - 16 Mar 2024
Abstract
Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration
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Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems.
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(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
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Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
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Ahad Alotaibi, Chris Chatwin and Phil Birch
Appl. Syst. Innov. 2024, 7(2), 23; https://doi.org/10.3390/asi7020023 - 11 Mar 2024
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In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology
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In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.
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Adaptive Active Disturbance Rejection Control for Vehicle Steer-by-Wire under Communication Time Delays
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Kamal Rsetam, Yusai Zheng, Zhenwei Cao and Zhihong Man
Appl. Syst. Innov. 2024, 7(2), 22; https://doi.org/10.3390/asi7020022 - 08 Mar 2024
Abstract
In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An
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In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance estimation performances. Based on the accurate estimations of the adaptive extended state observer, the proposed adaptive full state error feedback controller is equipped with variable gains driven by the tracking error to develop control precision. The integration of the advantages of the adaptive extended state observer and the adaptive full state error feedback controller can improve the dynamic transient and static steady-state effectiveness, respectively. To assess the superior performance of the proposed adaptive active disturbance rejection control, a comparative analysis is conducted between the proposed control scheme and the classical active disturbance rejection control in two different cases. It is worth noting that the active disturbance rejection control serves as a benchmark for evaluating the performance of the proposed control approach. The results from the comparison studies executing two simulated cases validate the superiority of the suggested control, in which estimation, tracking response rate, and steering angle precision are greatly improved by the scheme proposed in this article.
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(This article belongs to the Section Control and Systems Engineering)
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How Can the Circular Economy Contribute to Resolving Social Housing Challenges?
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Fernanda Paes de Barros Gomide, Luís Bragança and Eloy Fassi Casagrande Junior
Appl. Syst. Innov. 2024, 7(2), 21; https://doi.org/10.3390/asi7020021 - 07 Mar 2024
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The construction sector stands as the predominant consumer of cement, steel, and plastic and is accountable for a substantial 55% of industrial carbon emissions. Greenhouse gases and other forms of pollution linked to the housing sector significantly contribute to the adverse environmental impact
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The construction sector stands as the predominant consumer of cement, steel, and plastic and is accountable for a substantial 55% of industrial carbon emissions. Greenhouse gases and other forms of pollution linked to the housing sector significantly contribute to the adverse environmental impact of the construction industry. This study underscores the need to incorporate pertinent issues into the Circular Economy (CE) agenda for a lasting and effective mitigation strategy. Through a Systematic Literature Review (SLR), this article explores answers to the research question: “How can the Circular Economy contribute to resolving social housing challenges?” The findings from this comprehensive review highlight that refurbishing the social housing (SH) built environment and formulating public policies targeted at the SH sector emerge as pivotal themes for effective solutions. The principles of the Circular Economy present a sustainable model that can play a crucial role in addressing the social housing challenge. In conclusion, this SLR demonstrates that Circular Economy principles offer a viable approach to tackling the social housing crisis. By embracing these principles, a sustainable model can be established to address the challenges posed by social housing, thereby contributing to the broader goal of environmental conservation in the construction sector.
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Open AccessArticle
Redispatch Model for Real-Time Operation with High Solar-Wind Penetration and Its Adaptation to the Ancillary Services Market
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Kristian Balzer and David Watts
Appl. Syst. Innov. 2024, 7(2), 20; https://doi.org/10.3390/asi7020020 - 29 Feb 2024
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Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis
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Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.
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(This article belongs to the Section Applied Mathematics)
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Propulsion and Suspension Concept of the Technical University of Munich Hyperloop Full-Scale Demonstrator
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Domenik Radeck, Felix He-Mao Hsu, Florian Janke, Gabriele Semino, Tim Hofmann, Sebastian Rink and Agnes Jocher
Appl. Syst. Innov. 2024, 7(2), 19; https://doi.org/10.3390/asi7020019 - 22 Feb 2024
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The hyperloop concept envisions a low pressure tube and capsules, called pods, traveling at the speed of commercial aircraft as a sustainable, future-proof mass transportation system between cities. However, in contrast to the use case of such a system, the detailed technical concept
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The hyperloop concept envisions a low pressure tube and capsules, called pods, traveling at the speed of commercial aircraft as a sustainable, future-proof mass transportation system between cities. However, in contrast to the use case of such a system, the detailed technical concept is still under development. One challenging difference in comparison to other modes of transportation lies in the technical concept of the infrastructure, which is hard to change in the long term and therefore allows a few iterations only. This study’s key contribution is to showcase the conceptual design decisions of the 24 m full-scale Hyperloop Demonstrator at the Technical University of Munich (TUM) for the propulsion and suspension system, featuring the design decision tree (DDT) method as a framework to visualize and explain the technical design decisions and dependencies of complex hardware systems. The construction of the full-scale demonstrator not only proved the feasibility of the concept but also provided valuable concept-level experiences, which are shared within this work. Compared to existing maglev and hyperloop concepts, the presented concept features a separated air-cored long stator propulsion system and a homopolar electromagnetic suspension at the bottom with the track wrapping around the vehicle, revealing promising advantages like the structural simplification of the infrastructure and the independence of the guideway and tube.
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Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
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Woo-Hyun Choi and Jongwon Kim
Appl. Syst. Innov. 2024, 7(2), 18; https://doi.org/10.3390/asi7020018 - 21 Feb 2024
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Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in
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Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.
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(This article belongs to the Special Issue Industrial Cybersecurity)
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Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
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Diana Bratić, Marko Šapina, Denis Jurečić and Jana Žiljak Gršić
Appl. Syst. Innov. 2024, 7(1), 17; https://doi.org/10.3390/asi7010017 - 15 Feb 2024
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This paper addresses the challenges associated with the centralized storage of educational materials in the context of a fragmented and disparate database. In response to the increasing demands of modern education, efficient and accessible retrieval of materials for educators and students is essential.
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This paper addresses the challenges associated with the centralized storage of educational materials in the context of a fragmented and disparate database. In response to the increasing demands of modern education, efficient and accessible retrieval of materials for educators and students is essential. This paper presents a hybrid model based on the transformer framework and utilizing an API for an existing large language model (LLM)/chatbot. This integration ensures precise responses drawn from a comprehensive educational materials database. The model architecture uses mathematically defined algorithms for precise functions that enable deep text processing through advanced word embedding methods. This approach improves accuracy in natural language processing and ensures both high efficiency and adaptability. Therefore, this paper not only provides a technical solution to a prevalent problem but also highlights the potential for the continued development and integration of emerging technologies in education. The aim is to create a more efficient, transparent, and accessible educational environment. The importance of this research lies in its ability to streamline material access, benefiting the global scientific community and contributing to the continuous advancement of educational technology.
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(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)
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Deep Learning Method to Detect Missing Welds for Joist Assembly Line
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Hamed Raoofi, Asa Sabahnia, Daniel Barbeau and Ali Motamedi
Appl. Syst. Innov. 2024, 7(1), 16; https://doi.org/10.3390/asi7010016 - 13 Feb 2024
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Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well
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Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well as improvements in product quality. This research focuses on the application of computer vision approaches to monitor the quality of welding in prefabricated steel elements. A high-performance network was designed, consisting of a video capturing station, a customized classifier based on a YOLOv4 detector and an IoU tracker, and a user interface software for any interaction with quality control workers. The network demonstrated over 98% accuracy in identifying steel connection types and detecting missed welds on the assembly line in real-time. Extensive validation was conducted using a large dataset from a real production environment. The proposed framework aims to reduce rework, minimize hazards, and enhance product quality. This research contributes to the automation of quality control processes in the construction industry.
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(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports
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Zhikang Peng, Dongli Liu, Xiaoyun Song, Meihua Wang, Yiwen Rao, Yanjie Guo and Jun Peng
Appl. Syst. Innov. 2024, 7(1), 15; https://doi.org/10.3390/asi7010015 - 12 Feb 2024
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Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare. However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft
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Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare. However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft robotic gripper with rigid supports to enhance lifting force by 150 ± 20% in comparison with that of the same gripper without supports, which successfully lifted a metallic wrench (672 g). The soft gripper also achieves excellent adaptivity for irregularly shaped objects. The design, fabrication, and performance of soft grippers with rigid supports are discussed in this paper.
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(This article belongs to the Special Issue Smart Soft Robotics: Design, Control and Applications)
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Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation
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Abdulaziz Aldoseri, Khalifa N. Al-Khalifa and Abdel Magid Hamouda
Appl. Syst. Innov. 2024, 7(1), 14; https://doi.org/10.3390/asi7010014 - 08 Feb 2024
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In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and
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In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.
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Organizational Processes for Adopting Breakthrough Technology: Text Mining of AI Perception among Japanese Firms
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Yusuke Hoshino and Takashi Hirao
Appl. Syst. Innov. 2024, 7(1), 13; https://doi.org/10.3390/asi7010013 - 31 Jan 2024
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Artificial intelligence (AI) has become popular worldwide after technological breakthroughs in the early 2010s. Accordingly, many organizations and individuals have been using AI for various applications. Previous research has been dominated by case studies regarding the industrial use of AI, although how time-series
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Artificial intelligence (AI) has become popular worldwide after technological breakthroughs in the early 2010s. Accordingly, many organizations and individuals have been using AI for various applications. Previous research has been dominated by case studies regarding the industrial use of AI, although how time-series changes affect users’ perceptions has not been clarified yet. This study analyzes time-series changes in AI perceptions through text mining from nonfinancial information obtained from Japanese firms’ disclosures. The main findings of this study are as follows: first, perceptions of AI vary across industries; second, the business sector has progressed through the stages of recognition, investment, strategization, commercialization, and monetization. This transition is concurrent with each category’s evolving interpretation of the innovator theory proposed by Rogers (2003), to some extent. Third, it took approximately a decade from the breakthrough technology to the monetization by Japanese firms. Our findings underline the importance of speeding up the organizational process through intervention and contribution to the areas regarding “diffusion of innovation” and perceptual characteristics.
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Elevating Academic Advising: Natural Language Processing of Student Reviews
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Omiros Iatrellis, Nicholas Samaras, Konstantinos Kokkinos and Apostolis Xenakis
Appl. Syst. Innov. 2024, 7(1), 12; https://doi.org/10.3390/asi7010012 - 31 Jan 2024
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Academic advising is often pivotal in shaping students’ educational experiences and choices. This study leverages natural language processing to quantitatively evaluate reviews of academic advisors, aiming to provide actionable insights on key feedback phrases and demographic factors for enhancing advising services. This analysis
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Academic advising is often pivotal in shaping students’ educational experiences and choices. This study leverages natural language processing to quantitatively evaluate reviews of academic advisors, aiming to provide actionable insights on key feedback phrases and demographic factors for enhancing advising services. This analysis encompassed a comprehensive evaluation of 1151 reviews of undergraduate students for academic advisors, which were collected within a European University alliance consisting of five universities, offering a diverse pool of feedback from a wide range of academic interactions. Employing sentiment analysis powered by artificial intelligence, we computed compound sentiment scores for each academic advisor’s reviews. Subsequently, statistical analyses were conducted to provide insights into how demographic factors may or may not influence students’ sentiment and evaluations of academic advisory services. The results indicated that advisor’s gender had no substantial influence on the sentiment of the reviews. On the contrary, the academic advisors’ age showed a notable impact, with younger advisors surprisingly receiving more favorable evaluations. Word frequency analyses, both for positive and negative expressions, were also performed to contextualize the language used in describing academic advisors. The prevalent word combinations in reviews of highly rated academic advisors emphasized attributes like empathy, approachability, and effectiveness in guiding students towards achieving their academic goals. Conversely, advisors with less favorable reviews were often perceived as inadequate in addressing students’ concerns related to their academic journey, revealing persistent challenges in the student–advisor interaction that impacted their evaluation. This analysis of academic advisor reviews contributes to the body of literature by highlighting the significance of managing student expectations and enhancing advisor skills and qualities to foster positive interactions and academic success.
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(This article belongs to the Special Issue Advanced Technologies and Methodologies in Education 4.0)
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Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers
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Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes and Tobias Meisen
Appl. Syst. Innov. 2024, 7(1), 11; https://doi.org/10.3390/asi7010011 - 22 Jan 2024
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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual
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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.
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Open AccessArticle
Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods
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Jih-Ching Chiu, Guan-Yi Lee, Chih-Yang Hsieh and Qing-You Lin
Appl. Syst. Innov. 2024, 7(1), 10; https://doi.org/10.3390/asi7010010 - 19 Jan 2024
Abstract
In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional
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In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents.
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(This article belongs to the Section Human-Computer Interaction)
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Open AccessArticle
A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients
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Bhawnath Tiwari, Kenny Jeanmonod, Paolo Germano, Christian Koechli, Sofia Lydia Ntella, Zoltan Pataky, Yoan Civet and Yves Perriard
Appl. Syst. Innov. 2024, 7(1), 9; https://doi.org/10.3390/asi7010009 - 18 Jan 2024
Abstract
Plantar pressure plays a crucial role in the pathogenesis of foot ulcers among patients with diabetes and peripheral polyneuropathy. Pressure relief is a key requirement for both the prevention and treatment of plantar ulcers. Conventional medical practice to enable such an action is
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Plantar pressure plays a crucial role in the pathogenesis of foot ulcers among patients with diabetes and peripheral polyneuropathy. Pressure relief is a key requirement for both the prevention and treatment of plantar ulcers. Conventional medical practice to enable such an action is usually realized by means of dedicated insoles and special footwear. Another technique for foot pressure offloading (not in medical practice) can be achieved by sensing/estimating the current state (pressure) and, accordingly, enabling a pressure release mechanism once a defined threshold is reached. Though these mechanisms can make plantar pressure monitoring and release possible, overall, they make shoes bulkier, power-dependent, and expensive. In this work, we present a passive and self-offloading alternative to keep plantar pressure within a defined safe limit. Our approach is based on the use of a permanent magnet, taking advantage of its non-linear field reduction with distance. The proposed solution is free from electronics and is a low-cost alternative for smart shoe development. The overall size of the device is 13 mm in diameter and 30 mm in height. The device allows more than 20-times the tunability of the threshold pressure limit, which makes it possible to pre-set the limit as low as 38 kPa and as high as 778 kPa, leading to tunability within a wide range. Being a passive, reliable, and low-cost alternative, the proposed solution could be useful in smart shoe development to prevent foot ulcer development. The proposed device provides an alternative for offloading plantar pressure that is free from the power feeding requirement. The presented study provides preliminary results for the development of a complete offloading shoe that could be useful for the prevention/care of foot ulcers among diabetic patients.
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(This article belongs to the Section Medical Informatics and Healthcare Engineering)
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Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
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Weijun Li, Jintong Liu, Yuxiao Gao, Xinyong Zhang and Jianlai Gu
Appl. Syst. Innov. 2024, 7(1), 8; https://doi.org/10.3390/asi7010008 - 08 Jan 2024
Abstract
The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is
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The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER models have limited semantic extraction capabilities and cannot capture deep semantic information between nested entities in the text. To address these issues, this paper proposes a model that uses the GlobalPointer module to identify nested entities in the text and constructs the IDCNNLR semantic extraction module to extract deep semantic information. Furthermore, multiple-head self-attention mechanisms are incorporated into the model at multiple positions to achieve data denoising, enhancing the quality of semantic features. The proposed model considers each possible entity boundary through the GlobalPointer module, and the IDCNNLR semantic extraction module and multi-position attention mechanism are introduced to enhance the model’s semantic extraction capability. Experimental results demonstrate that the proposed model achieves F1 scores of 69.617% and 79.285% on the CMeEE Chinese nested entity recognition dataset and CLUENER2020 Chinese fine-grained entity recognition dataset, respectively. The model exhibits improvement compared to baseline models, and each innovation point shows effective performance enhancement in ablative experiments.
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(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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Broader Terms Curriculum Mapping: Using Natural Language Processing and Visual-Supported Communication to Create Representative Program Planning Experiences
by
Rogério Duarte, Ângela Lacerda Nobre, Fernando Pimentel and Marc Jacquinet
Appl. Syst. Innov. 2024, 7(1), 7; https://doi.org/10.3390/asi7010007 - 31 Dec 2023
Abstract
Accreditation bodies call for curriculum development processes that are open to all stakeholders, reflecting viewpoints of students, industry, university faculty, and society. However, communication difficulties between faculty and non-faculty groups leave an immense collaboration potential unexplored. Using the classification of learning objectives, natural
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Accreditation bodies call for curriculum development processes that are open to all stakeholders, reflecting viewpoints of students, industry, university faculty, and society. However, communication difficulties between faculty and non-faculty groups leave an immense collaboration potential unexplored. Using the classification of learning objectives, natural language processing, and data visualization, this paper presents a quantitative method that delivers program plan representations that are universal, self-explanatory, and empowering; promoting stronger links between program courses and curriculum development open to all stakeholders. A simple example shows how the method contributes to representative program planning experiences and a case study is used to confirm the method’s accuracy and utility.
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(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)
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AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests
by
Hajar Majjate, Youssra Bellarhmouch, Adil Jeghal, Ali Yahyaouy, Hamid Tairi and Khalid Alaoui Zidani
Appl. Syst. Innov. 2024, 7(1), 6; https://doi.org/10.3390/asi7010006 - 28 Dec 2023
Abstract
Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and
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Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.
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(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)
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