Application of Big Data and Deep Learning in Engineering Analysis and Design

Topic Information

Dear Colleagues,

With the rapid growth in the Internet of Things (IoT), data collection has become a routine procedure. Big data refer to a large and complex set of structured, semi-structured, and unstructured data that are collected and can be mined for information, used in advanced data analytics, and applied to machine and deep learning to develop predictive models. When big data generated from wide sensor networks with remarkable sampling rates are employed to monitor large systems, real-time and adaptive filtering is essential to ensure functionality.

Some common characteristics for big data are validity, value, variety, variability, velocity, veracity, visualization, volatility, and volume. These 9V characteristics have been extensively studied, and various heuristic approaches and numerical techniques have been proposed to collect, store, manage, process. and access these data, as well as to apply them to solve real-world engineering problems.

This Topic seeks papers that address general subject areas related to any of these 9Vs, as well as big data and machine/deep learning, scalability and fault tolerance, parallel data processing, cloud storage and computing, graph databases and processing, data pattern recognition, advanced biometrics, biomimetics, systems diagnostics and prognostics, anomaly detection, noise and fake data identification, dimensional reduction, training and inference methods, neural machine translation (NMT), context-sensitive and generative based conversational systems, explainable artificial intelligence, system data integration (e.g., for fleet management), etc.

Additional topics of interest include but are not limited to:

• Artificial intelligence;
• Cluster analysis;
• Efficient storage, access, and transfer;
• Image-based applications;
• Information mining and classification;
• Machine vision;
• Population-based optimization;
• Quantum computing for big data analytics;
• Real-time big data adoption and analytics ;
• Security and privacy;
• Sentiment analysis;
• Uncertainty modeling;
• Voice recognition and classification.

Dr. Eric M. Lui
Dr. Antonio Concilio
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Automation automation	-	2.9	2020	24.1 Days	CHF 1000
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	25.3 Days	CHF 1800
Biomimetics biomimetics	3.4	3.5	2016	19.5 Days	CHF 2200
Designs designs	-	3.9	2017	21.7 Days	CHF 1600
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600

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Published Papers (21 papers)

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15 pages, 1687 KiB  

Open AccessArticle

MC-YOLOv5: A Multi-Class Small Object Detection Algorithm

by Haonan Chen, Haiying Liu, Tao Sun, Haitong Lou, Xuehu Duan, Lingyun Bi and Lida Liu

Biomimetics 2023, 8(4), 342; https://doi.org/10.3390/biomimetics8040342 - 2 Aug 2023

Cited by 14 | Viewed by 4839

Abstract

The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed [...] Read more.

The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed MC-YOLOv5, an algorithm specifically designed for multi-class small object detection. Our approach incorporates three key innovations: (1) the application of an improved CB module during feature extraction to capture edge information that may be less apparent in small objects, thereby enhancing detection precision; (2) the introduction of a new shallow network optimization strategy (SNO) to expand the receptive field of convolutional layers and reduce missed detections in dense small object scenarios; and (3) the utilization of an anchor frame-based decoupled head to expedite training and improve overall efficiency. Extensive evaluations on VisDrone2019, Tinyperson, and RSOD datasets demonstrate the feasibility of MC-YOLOv5 in detecting multi-class small objects. Taking VisDrone2019 dataset as an example, our algorithm outperforms the original YOLOv5L with improvements observed across various metrics: mAP50 increased by 8.2%, mAP50-95 improved by 5.3%, F1 score increased by 7%, inference time accelerated by 1.8 ms, and computational requirements reduced by 35.3%. Similar performance gains were also achieved on other datasets. Overall, our findings validate MC-YOLOv5 as a viable solution for accurate multi-class small object detection. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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17 pages, 12969 KiB  

Open AccessArticle

Construction of Data-Driven Performance Digital Twin for a Real-World Gas Turbine Anomaly Detection Considering Uncertainty

by Yangfeifei Ma, Xinyun Zhu, Jilong Lu, Pan Yang and Jianzhong Sun

Sensors 2023, 23(15), 6660; https://doi.org/10.3390/s23156660 - 25 Jul 2023

Cited by 7 | Viewed by 2991

Abstract

Anomaly detection and failure prediction of gas turbines is of great importance for ensuring reliable operation. This work presents a novel approach for anomaly detection based on a data-driven performance digital twin of gas turbine engines. The developed digital twin consists of two [...] Read more.

Anomaly detection and failure prediction of gas turbines is of great importance for ensuring reliable operation. This work presents a novel approach for anomaly detection based on a data-driven performance digital twin of gas turbine engines. The developed digital twin consists of two parts: uncertain performance digital twin (UPDT) and fault detection capability. UPDT is a probabilistic digital representation of the expected performance behavior of real-world gas turbine engines operating under various conditions. Fault detection capability is developed based on detecting UPDT outputs that have low probability under the training distribution. A novel anomaly measure based on the first Wasserstein distance is proposed to characterize the entire flight data, and a threshold can be applied to this measure to detect anomaly flights. The proposed UPDT with uncertainty quantification is trained with the sensor data from an individual physical reality and the outcome of the UPDT is intended to deliver the health assessment and fault detection results to support operation and maintenance decision-making. The proposed method is demonstrated on a real-world dataset from a typical type of commercial turbofan engine and the result shows that the F1 score reaches a maximum of 0.99 with a threshold of 0.45. The case study demonstrated that the proposed novel anomaly detection method can effectively identify the abnormal samples, and it is also possible to isolate anomalous behavior in a single performance signal, which is helpful for further fault diagnosis once an anomaly is detected. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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22 pages, 1529 KiB  

Open AccessArticle

Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning

by Ze-Yang Tang, Qi-Biao Hu, Yi-Bo Cui, Lei Hu, Yi-Wen Li and Yu-Jie Li

Big Data Cogn. Comput. 2023, 7(3), 133; https://doi.org/10.3390/bdcc7030133 - 24 Jul 2023

Cited by 2 | Viewed by 2351

Abstract

This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome [...] Read more.

This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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12 pages, 2923 KiB  

Open AccessCommunication

Smartphone Authentication System Using Personal Gaits and a Deep Learning Model

by Jiwoo Choi, Sangil Choi and Taewon Kang

Sensors 2023, 23(14), 6395; https://doi.org/10.3390/s23146395 - 14 Jul 2023

Cited by 2 | Viewed by 1743

Abstract

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. [...] Read more.

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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22 pages, 5008 KiB  

Open AccessArticle

A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning

by Talha Burak Alakuş

Biomimetics 2023, 8(2), 218; https://doi.org/10.3390/biomimetics8020218 - 23 May 2023

Cited by 3 | Viewed by 2345

Abstract

Recent studies have shown that DNA enhancers have an important role in the regulation of gene expression. They are responsible for different important biological elements and processes such as development, homeostasis, and embryogenesis. However, experimental prediction of these DNA enhancers is time-consuming and [...] Read more.

Recent studies have shown that DNA enhancers have an important role in the regulation of gene expression. They are responsible for different important biological elements and processes such as development, homeostasis, and embryogenesis. However, experimental prediction of these DNA enhancers is time-consuming and costly as it requires laboratory work. Therefore, researchers started to look for alternative ways and started to apply computation-based deep learning algorithms to this field. Yet, the inconsistency and unsuccessful prediction performance of computational-based approaches among various cell lines led to the investigation of these approaches as well. Therefore, in this study, a novel DNA encoding scheme was proposed, and solutions were sought to the problems mentioned and DNA enhancers were predicted with BiLSTM. The study consisted of four different stages for two scenarios. In the first stage, DNA enhancer data were obtained. In the second stage, DNA sequences were converted to numerical representations by both the proposed encoding scheme and various DNA encoding schemes including EIIP, integer number, and atomic number. In the third stage, the BiLSTM model was designed, and the data were classified. In the final stage, the performance of DNA encoding schemes was determined by accuracy, precision, recall, F1-score, CSI, MCC, G-mean, Kappa coefficient, and AUC scores. In the first scenario, it was determined whether the DNA enhancers belonged to humans or mice. As a result of the prediction process, the highest performance was achieved with the proposed DNA encoding scheme, and an accuracy of 92.16% and an AUC score of 0.85 were calculated, respectively. The closest accuracy score to the proposed scheme was obtained with the EIIP DNA encoding scheme and the result was observed as 89.14%. The AUC score of this scheme was measured as 0.87. Among the remaining DNA encoding schemes, the atomic number showed an accuracy score of 86.61%, while this rate decreased to 76.96% with the integer scheme. The AUC values of these schemes were 0.84 and 0.82, respectively. In the second scenario, it was determined whether there was a DNA enhancer and, if so, it was decided to which species this enhancer belonged. In this scenario, the highest accuracy score was obtained with the proposed DNA encoding scheme and the result was 84.59%. Moreover, the AUC score of the proposed scheme was determined as 0.92. EIIP and integer DNA encoding schemes showed accuracy scores of 77.80% and 73.68%, respectively, while their AUC scores were close to 0.90. The most ineffective prediction was performed with the atomic number and the accuracy score of this scheme was calculated as 68.27%. Finally, the AUC score of this scheme was 0.81. At the end of the study, it was observed that the proposed DNA encoding scheme was successful and effective in predicting DNA enhancers. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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15 pages, 6631 KiB  

Open AccessArticle

Finding the Time-Period-Based Most Frequent Path from Trajectory–Topology

by Jianing Ding, Xin Jin and Zhiheng Li

Big Data Cogn. Comput. 2023, 7(2), 88; https://doi.org/10.3390/bdcc7020088 - 8 May 2023

Viewed by 1793

Abstract

The Time-Period-Based Most Frequent Path (TPMFP) problem has been a hot topic in traffic studies for many years. The TPMFP problem involves finding the most frequent path between two locations by observing the travelling behaviors of drivers in a specific time period. However, [...] Read more.

The Time-Period-Based Most Frequent Path (TPMFP) problem has been a hot topic in traffic studies for many years. The TPMFP problem involves finding the most frequent path between two locations by observing the travelling behaviors of drivers in a specific time period. However, the previous researchers over-simplify the road network, which results in the ignorance of transfer costs at intersections. To address this problem more elegantly, we built up an urban topology model consisting of Intersection Vertices and Connection Vertices. Specifically, we split the Intersection Vertices to eliminate the influence of transfer cost on finding TPMFP and generate Trajectory–Topology from GPS records data. In addition, we further leveraged the Footmark Graph method to find the TPMFP. Finally, we conducted extensive experiments using a real-world dataset containing over eight million GPS records. Compared to the current state-of-the-art method, our proposed approach can find more reasonable MFP in approximately 10% of cases during off-peak hours and 40% of cases during peak hours. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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14 pages, 4994 KiB  

Open AccessArticle

The Dataset for Optimal Circulant Topologies

by Aleksandr Romanov

Big Data Cogn. Comput. 2023, 7(2), 80; https://doi.org/10.3390/bdcc7020080 - 20 Apr 2023

Cited by 3 | Viewed by 2001

Abstract

This article presents software for the synthesis of circulant graphs and the dataset obtained. An algorithm and new methods, which increase the speed of finding optimal circulant topologies, are proposed. The results obtained confirm an increase in performance and a decrease in memory [...] Read more.

This article presents software for the synthesis of circulant graphs and the dataset obtained. An algorithm and new methods, which increase the speed of finding optimal circulant topologies, are proposed. The results obtained confirm an increase in performance and a decrease in memory consumption compared to the previous implementation of the circulant topologies synthesis method. The developed software is designed to generate circulant topologies for the construction of networks-on-chip (NoCs) and multi-core systems reaching thousands of computing nodes. The developed software makes it possible to achieve high performance on an ordinary research workstation commensurate with similar solutions created for a supercomputer. The use cases of application of the created software for the analysis of routing algorithms in circulants and the regression analysis of the generated dataset of graph signatures to predict the characteristics of graphs of any size are described. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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28 pages, 8507 KiB  

Open AccessArticle

Efficient Continuous Subgraph Matching Scheme Based on Trie Indexing for Graph Stream Processing

by Dojin Choi, Somin Lee, Sanghyeuk Kim, Hyeonbyeong Lee, Jongtae Lim, Kyoungsoo Bok and Jaesoo Yoo

Appl. Sci. 2023, 13(8), 5137; https://doi.org/10.3390/app13085137 - 20 Apr 2023

Cited by 1 | Viewed by 1913

Abstract

With the expansion of the application range of big data and artificial intelligence technologies, graph data have been increasingly used to analyze the relationships among objects. With the advancement of network technology and the spread of social network services, there has been an [...] Read more.

With the expansion of the application range of big data and artificial intelligence technologies, graph data have been increasingly used to analyze the relationships among objects. With the advancement of network technology and the spread of social network services, there has been an increasing need for a continuous query processing algorithm that can manage large-volume graph streams generated in real time. In this paper, a sliding-window-based continuous subgraph matching algorithm that can efficiently control graph streams is proposed. The proposed scheme uses a query processing technique based on trie indexing. It establishes an index based on a materialized view of similar queries and conducts continuous query processing based on the materialized view to perform continuous query processing efficiently. It also provides wildcard operations on vertices and edges to consider various query types. Moreover, in this study, a two-level cache technique that can manage frequently used subgraphs and subgraphs that may be used in the future is developed, to handle intermediate query results in the form of a materialized view. Cache replacement techniques based on statistical data are also presented to improve the performance of the developed cache technique. The excellent performance of the proposed algorithm is verified by a conducting independent performance evaluation and comparative performance evaluation. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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27 pages, 5634 KiB  

Open AccessArticle

Biomaterials Research-Driven Design Visualized by AI Text-Prompt-Generated Images

by Yomna K. Abdallah and Alberto T. Estévez

Designs 2023, 7(2), 48; https://doi.org/10.3390/designs7020048 - 24 Mar 2023

Cited by 4 | Viewed by 5326

Abstract

AI text-to-image generated images have revolutionized the design process and its rapid development since 2022. Generating various iterations of perfect renders in few seconds by textually expressing the design concept. This high-potential tool has opened wide possibilities for biomaterials research-driven design. That is [...] Read more.

AI text-to-image generated images have revolutionized the design process and its rapid development since 2022. Generating various iterations of perfect renders in few seconds by textually expressing the design concept. This high-potential tool has opened wide possibilities for biomaterials research-driven design. That is based on developing biomaterials for multi-scale applications in the design realm and built environment. From furniture to architectural elements to architecture. This approach to the design process has been augmented by the massive capacity of AI text-to-image models to visualize high-fidelity and innovative renders that reflect very detailed physical characteristics of the proposed biomaterials from micro to macro. However, this biomaterials research-driven design approach aided by AI text-to-image models requires criteria for evaluating the role and efficiency of employing AI image generation models in this design process. Furthermore, since biomaterials research-driven design is focused not only on design studies but also the biomaterials engineering research and process, it requires a sufficient method for protecting its novelty and copyrights. Since their emergence in late 2022, AI text-to-image models have been raising alarming ethical concerns about design authorship and designer copyrights. This requires the establishment of a referencing method to protect the copyrights of the designers of these generated renders as well as the copyrights of the authors of their training data referencing by proposing an auxiliary AI model for automatic referencing of these AI-generated images and their training data as well. Thus, the current work assesses the role of AI text-to-image models in the biomaterials research-driven design process and their methodology of operation by analyzing two case studies of biomaterials research-driven design projects performed by the authors aided by AI text-to-image models. Based on the results of this analysis, design criteria will be presented for a fair practice of AI-aided biomaterials research-driven process. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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18 pages, 5371 KiB  

Open AccessArticle

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

by Mingming Zhang, Pan Kong, Aiguo Xia, Wei Tuo, Yongzhao Lv and Shaohong Wang

Biomimetics 2023, 8(2), 132; https://doi.org/10.3390/biomimetics8020132 - 23 Mar 2023

Cited by 1 | Viewed by 1959

Abstract

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and [...] Read more.

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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17 pages, 5098 KiB  

Open AccessArticle

A Two-Step Approach to Overcoming Data Imbalance in the Development of an Electrocardiography Data Quality Assessment Algorithm: A Real-World Data Challenge

by Hyun Joo Kim, S. Jayakumar Venkat, Hyoung Woo Chang, Yang Hyun Cho, Jee Yang Lee and Kyunghee Koo

Biomimetics 2023, 8(1), 119; https://doi.org/10.3390/biomimetics8010119 - 13 Mar 2023

Cited by 2 | Viewed by 2379

Abstract

Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to automatically classify the quality of ECG data. A total of [...] Read more.

Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to automatically classify the quality of ECG data. A total of 31,127 twenty-s ECG segments of 250 Hz were used as the training/validation dataset. Data quality was categorized into three classes: acceptable, unacceptable, and uncertain. In the training/validation dataset, 29,606 segments (95%) were in the acceptable class. Two one-step, three-class approaches and two two-step binary sequential approaches were developed using random forest (RF) and two-dimensional convolutional neural network (2D CNN) classifiers. Four approaches were tested on 9779 test samples from another hospital. On the test dataset, the two-step 2D CNN approach showed the best overall accuracy (0.85), and the one-step, three-class 2D CNN approach showed the worst overall accuracy (0.54). The most important parameter, precision in the acceptable class, was greater than 0.9 for all approaches, but recall in the acceptable class was better for the two-step approaches: one-step (0.77) vs. two-step RF (0.89) and one-step (0.51) vs. two-step 2D CNN (0.94) (p < 0.001 for both comparisons). For the ECG quality classification, where substantial data imbalance exists, the 2-step approaches showed more robust performance than the one-step approach. This algorithm can be used as a preprocessing step in artificial intelligence research using continuously acquired biosignals. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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22 pages, 4960 KiB  

Open AccessArticle

Designing Power Transformer Using Particle Swarm Optimization with Respect to Transformer Noise, Weight, and Losses

by Wahyudi Budi Pramono, Fransisco Danang Wijaya, Sasongko Pramono Hadi, Moh. Slamet Wahyudi and Agus Indarto

Designs 2023, 7(1), 31; https://doi.org/10.3390/designs7010031 - 10 Feb 2023

Cited by 5 | Viewed by 3254

Abstract

The increased use of electrical energy will encourage the installation of more power transformers in residential areas as well as in industrial areas. Each power transformer, in its operation, will generate noise that can interfere with comfort and, at some level, cause health [...] Read more.

The increased use of electrical energy will encourage the installation of more power transformers in residential areas as well as in industrial areas. Each power transformer, in its operation, will generate noise that can interfere with comfort and, at some level, cause health problems. The design of the power transformer currently focuses on optimizing its economic side, so noise has not been considered at this design stage. This research is about optimizing the low noise transformer design. The main goal is to obtain a low noise power transformer with low production costs. The method used in this optimization is particle swarm optimization with a multi-objective function. The objective function consists of the minimization of load noise, core weight, and winding weight. In this study, 11 optimized variables were used. Some variables that are optimized must be in the form of integers. Therefore, the optimization process needs a mechanism for mapping variables. The results showed that a low noise power transformer could be designed at optimal cost. Design validation was performed analytically and numerically with COMSOL software. The optimization results showed a decrease in load noise, core, and winding weight by 0.86 dB, 2.12%, and 47.46%, respectively. The results of this optimization are better than the designs used regularly in the industry. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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19 pages, 5346 KiB  

Open AccessArticle

Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning

by Xinye Wu, Shude Fu and Zujie He

Appl. Sci. 2023, 13(1), 308; https://doi.org/10.3390/app13010308 - 27 Dec 2022

Cited by 4 | Viewed by 2087

Abstract

Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on [...] Read more.

Traffic flow has the characteristics of randomness, complexity, and nonlinearity, which brings great difficulty to the prediction of short-term traffic flow. Based on considering the advantages and disadvantages of various prediction models, this paper proposes a short-term traffic flow prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and machine learning. Firstly, CEEMDAN is used to decompose the original traffic flow time series to obtain multiple component sequences with huge complexity differences. In order to measure the complexity of each component sequence, the permutation entropy of each component sequence is calculated. According to the permutation entropy, the component sequence is divided into three types: high-frequency components, intermediate-frequency components, and low-frequency components. Secondly, according to the different volatility of the three types of components, the high-frequency components, intermediate-frequency components, and low-frequency components are predicted by long short-term memory (LSTM), support vector machine (SVM), and k-nearest neighbor (KNN), respectively. Finally, the accurate traffic flow prediction value can be obtained by the linear superposition of the prediction results of the three component prediction models. Through a measured traffic flow data, the combined model proposed in this paper is compared to the binary gray wolf algorithm–long short-term memory (BGWO-LSTM) model, the improved gray wolf algorithm–support vector machine (IGWO-SVM) model, and the KNN model. The mean square error (MSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 41.26, 44.98, and 57.69, respectively. The mean absolute error (MAE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.33, 2.44, and 2.70, respectively. The root mean square error (RMSE) of the combined model is less than that of the BGWO-LSTM model, the IGWO-SVM model, and the KNN model by 2.89, 3.11, and 3.80, respectively. The three error indexes of the combined model are far smaller than those of the other single models. At the same time, the decision coefficient (R²) of the combined model is also closer to 1 compared to the other models, indicating that the prediction result of the combined model is the closest to the actual traffic flow. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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18 pages, 5799 KiB  

Open AccessArticle

Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm

by Baidi Shi, Yongfeng Jiang, Yefeng Bao, Bingyan Chen, Ke Yang and Xianming Chen

Sensors 2023, 23(1), 250; https://doi.org/10.3390/s23010250 - 26 Dec 2022

Cited by 3 | Viewed by 2945

Abstract

A weigh-in-motion (WIM) system continuously and automatically detects an object’s weight during transmission. The WIM system is used widely in logistics and industry due to increasing labor and time costs. However, the accuracy and stability of WIM system measurements could be affected by [...] Read more.

A weigh-in-motion (WIM) system continuously and automatically detects an object’s weight during transmission. The WIM system is used widely in logistics and industry due to increasing labor and time costs. However, the accuracy and stability of WIM system measurements could be affected by shock and vibration under high speed and heavy load. A novel six degrees-of-freedom (DOF), mass–spring damping-based Kalman filter with time scale (KFTS) algorithm was proposed to filter noise due to the multiple-input noise and its frequency that is highly coupled with the basic sensor signal. Additionally, an attention-based long short-term memory (LSTM) model was built to predict the object’s mass by using multiple time-series sensor signals. The results showed that the model has superior performance compared to support vector machine (SVM), fully connected network (FCN) and extreme gradient boosting (XGBoost) models. Experiments showed this improved deep learning model can provide remarkable accuracy under different loads, speed and working situations, which can be applied to the high-precision logistics industry. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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15 pages, 5398 KiB  

Open AccessArticle

GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

by Young-Hwa Sung, Soo-Jae Park, Dong-Yeon Kim and Sungho Kim

Sensors 2022, 22(23), 9412; https://doi.org/10.3390/s22239412 - 2 Dec 2022

Cited by 20 | Viewed by 5935

Abstract

The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial [...] Read more.

The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial GPS simulator can cause a GPS-guided drone to deviate from its intended course by transmitting counterfeit GPS signals. Therefore, an anti-spoofing technique is essential to ensure the operational safety of UAVs. Various methods have been introduced to detect GPS spoofing; however, most methods require additional hardware. This may not be appropriate for small UAVs with limited capacity. This study proposes a deep learning-based anti-spoofing method equipped with 1D convolutional neural network. The proposed method is lightweight and power-efficient, enabling real-time detection on mobile platforms. Furthermore, the performance of our approach can be enhanced by increasing training data and adjusting the network architecture. We evaluated our algorithm on the embedded board of a drone in terms of power consumption and inference time. Compared to the support vector machine, the proposed method showed better performance in terms of precision, recall, and F-1 score. Flight test demonstrated our algorithm could successfully detect GPS spoofing attacks. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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17 pages, 2128 KiB  

Open AccessArticle

A Novel Joint Adversarial Domain Adaptation Method for Rotary Machine Fault Diagnosis under Different Working Conditions

by Xiaoping Zhao, Fan Shao and Yonghong Zhang

Sensors 2022, 22(22), 9007; https://doi.org/10.3390/s22229007 - 21 Nov 2022

Cited by 6 | Viewed by 2190

Abstract

In real-world applications of detecting faults, many factors—such as changes in working conditions, equipment wear, and environmental causes—can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, [...] Read more.

In real-world applications of detecting faults, many factors—such as changes in working conditions, equipment wear, and environmental causes—can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, existing deep network algorithms perform poorly under different working conditions. To solve this problem, we propose a novel fault diagnosis method named Joint Adversarial Domain Adaptation (JADA) for fault detection under different working conditions. Our approach simultaneously aligns marginal distribution and conditional distribution across the source and target through a unified adversarial learning process. JADA aims to construct domain-invariant and category-discriminative feature representation that is effective and robust for substantial distribution difference caused by working conditions. We also introduce a supervision signal, namely center loss, that penalizes the distances between the deep features and their corresponding class centers. This makes the learned features better equipped with more discriminative structures and effectively prevents mode collapse. Twenty-four transfer fault diagnosis tasks based on two experimental platforms were conducted to evaluate the effectiveness of the proposed methods. Extensive experiments verified that the JADA can significantly outperform several popular methods under different transfer diagnosis tasks. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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20 pages, 3874 KiB  

Open AccessArticle

Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques

by Kennedy C. Onyelowe, Jagan Jayabalan, Ahmed M. Ebid, Pijush Samui, Rahul Pratap Singh, Atefeh Soleymani and Hashem Jahangir

Designs 2022, 6(6), 112; https://doi.org/10.3390/designs6060112 - 9 Nov 2022

Cited by 16 | Viewed by 3277

Abstract

The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from [...] Read more.

The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8% and a coefficient of determination R² of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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14 pages, 3391 KiB  

Open AccessArticle

Load-Settlement Curve and Subgrade Reaction of Strip Footing on Bi-Layered Soil Using Constitutive FEM-AI Coupled Techniques

by Ahmed M. Ebid, Kennedy C. Onyelowe and Mohamed Salah

Designs 2022, 6(6), 104; https://doi.org/10.3390/designs6060104 - 1 Nov 2022

Cited by 5 | Viewed by 3326

Abstract

This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings [...] Read more.

This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings with width (B) and rested on a bi-layered profile with top layer thickness (h) and bottom layer thickness (H). The soil was modeled using the well-known Mohr-Coulomb’s constitutive law. The extracted load-settlement curve from each FEM model is approximated to hyperbolic function and its factors (a, b) were determined. The subgrade reaction value (Ks) is the (stress/settlement), hence (1/Ks = a·Δ + b). Both inputs and outputs of the parametric study were collected in a single database containing the geometrical factors (B, h & H), soil properties of the top and bottom layers (c, φ & γ) and the extracted hyperbolic factors (a, b). Finally, three AI techniques—Genetic Programming (GP), Evolutionary Polynomial Regression (EPR) and Artificial Neural Networks (ANN)—were implemented to develop three predictive models to estimate the values of (a, b) using the collected database. The three developed models showed different accuracy values of (50%, 65% and 80%) for (GP, EPR and ANN), respectively. The innovation of the developed model is its ability to capture the degradation of a subgrade reaction by increasing the stress (or the settlement) according to the hyperbolic formula. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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20 pages, 5645 KiB  

Open AccessArticle

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure

by Songtong Han, Bo Zhang, Zhu Wen, Chunwei Zhang and Yong He

Sensors 2022, 22(19), 7653; https://doi.org/10.3390/s22197653 - 9 Oct 2022

Cited by 2 | Viewed by 2199

Abstract

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy [...] Read more.

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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24 pages, 3397 KiB  

Open AccessArticle

Deep Reinforcement Learning for Vehicle Platooning at a Signalized Intersection in Mixed Traffic with Partial Detection

by Hung Tuan Trinh, Sang-Hoon Bae and Duy Quang Tran

Appl. Sci. 2022, 12(19), 10145; https://doi.org/10.3390/app121910145 - 9 Oct 2022

Cited by 6 | Viewed by 3777

Abstract

The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning [...] Read more.

The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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11 pages, 4373 KiB  

Open AccessArticle

Geometry-Based Radiation Prediction of Laser Exposure Area for Laser Powder Bed Fusion Using Deep Learning

by Song Zhang, Anne Jahn, Lucas Jauer and Johannes Henrich Schleifenbaum

Appl. Sci. 2022, 12(17), 8854; https://doi.org/10.3390/app12178854 - 2 Sep 2022

Cited by 5 | Viewed by 2344

Abstract

Laser powder bed fusion (LPBF) is a promising technique used to manufacture complex geometries in a layer-wised manner. Radiation during the LPBF process is influenced by the part geometry, e.g., the overhang angle and the wall thickness. Locally varying radiation can cause deformation [...] Read more.

Laser powder bed fusion (LPBF) is a promising technique used to manufacture complex geometries in a layer-wised manner. Radiation during the LPBF process is influenced by the part geometry, e.g., the overhang angle and the wall thickness. Locally varying radiation can cause deformation of the product after manufacturing. Thus, the prediction of the geometry-caused radiation before the manufacturing can support the evaluation of the design printability to achieve first-time-right printing. In this paper, we present a framework to predict the geometry-based radiation information using a deep learning (DL) algorithm based on the part geometry from computer-aided design (CAD). The algorithm was trained using data from an LPBF-print job consisting of parts with varying overhang angles. Image data, which include the information of radiation, were captured with an optical tomography (OT) camera system that was installed on a LPBF machine used in a laboratory environment. For the DL algorithm, a U-Net based network with mean absolute error (MAE) loss was applied. The training input was binarized OT data representing the contour of the designed geometry. Complementary, the OT data were used as ground truth for the model training. For the application, the design contours of multiple layers were extracted from the CAD file. The result shows the applicability to predict the OT-like radiation by its contour, which has the possibility to show the anomaly due to the part geometry. Full article

(This article belongs to the Topic Application of Big Data and Deep Learning in Engineering Analysis and Design)

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