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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Naphtha’s price forecasting using Neuro-fuzzy system

Naphtha’s price forecasting using Neuro-fuzzy system

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SICE Annual Conference

作者： Porntip Visetsripong Pitikhate Sooraksa Pramote Luenam Watchareeporn Chaimongkol Joint Master of Science Program in Information Technology Management National Institute of Development Administration Bangkok Thailand Faculty of Engineering ing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Information Systems Education Center National Institute of Development Administration Bangkok Thailand School of Applied Statistics National Institute of Development Administration Bangkok Thailand

Naphthapsilas price forecasting using Neuro-fuzzy system is a forecasting technique that applied information technology with statistics. 1950 daily prices were collected as a time-series data with trend component. The research found that Neuro-Fuzzy system is more accurate and more reliable than a statistical method; it also works well with continuous data and performs better with more training data. Neuro-Fuzzy system might be used with different data type, but, it might come across with other factors, e.g. seasonal or irregular event. The research also illustrates the multidisciplinary nature in todaypsilas world of works in the era of merging among many disciplines.

关键词： Forecasting Predictive models Smoothing methods Neurons Market research Time series analysis Data models

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

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24th International Arab Conference on information technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

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Journal of Network and Computer Applications 2024年 227卷

作者： Lin, Chun-Cheng Deng, Der-Jiunn Hsieh, Li-Tsung Pan, Pei-Tzu Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua500 Taiwan

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities. © 2024 Elsevier Ltd

关键词： 5G mobile communication systems

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia Computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Portable Howell heterophoria measurement device

Portable Howell heterophoria measurement device

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Chen, Der-Chin Yeh, Feng-Ming Lin, Shyang Liu, Yung-Fu Hsieh, Ya-Hui Chen, Wei-Hsin Feng Chia University Department of Electrical Engineering Taichung Taiwan Department of Optometry Yuanpei University of Medical Technology Hsinchu Taiwan Professional Master s Program of Optometry Science and Technology Feng Chia University Taichung Taiwan Department of Veterinary Medicine National Chung Hsing University Taichung Taiwan Department of Healthcare Information and Management Ming Chuan University Taoyuan Taiwan Department of Optometry Hsin Sheng Junior College of Medical Care and Management Taoyuan Taiwan

ISBN: (纸本)9781665419512

In this study, we used a compound vision card consisting of Howell's and Thorington's vision card on both sides, and placed a prism or a Maddox 's lens in the left eye of the subjects to establish the subjective measurement technique of heterophoria. When a prism with 6 prism diopters and the base upward was placed in front of the left eye of the subject to look at the front of the compound vision card at 45 cm, it was observed that the Howell vision card was separated into two vision cards. After confirming the relative position of the point of the upper and lower vision cards, the subject move the paddle on the measuring device to make the paddle overlap the point of the upper vision card, judge the left and right directions of the point and read number of the horizontal base line, and then measure the line of sight deviation of the near horizontal heterophoria. In this study, 5 subjects with normal vision were tested with von Graefe, Howell and This innovative device for near distance horizontal esotropia. The test results showed that 85% of the subjects were exotropia, and the difference of the three measurements was within one prism diopter (±1Δ). It is proved that the reliability of the measuring device is excellent. © 2021 IEEE.

关键词： Prisms

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The Role of Positive Emotion in Trust Repair

The Role of Positive Emotion in Trust Repair

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2011 International Conference on Financial management and Economics（ICFME 2011）

作者： Jyh-Jeng Wu Shu-Hua Chien Ying-Hueih Chen Hsin-Tzu Chang Department of Business Management National United UniversityTaiwan Department of Insurance and Finance National Taichung Institute of TechnologyTaiwan Department of Computer Science and Information Management Providence UniversityTaiwan Master Program in Business Administration National United UniversityTaiwan

This paper discusses the role of casual attributions and positive emotion in trust repair of *** equation modeling(SEM) was used in our study to analyze 517 valid online *** empirical results indicate that casual attributions(locus and stability) have a negative impact on positive emotion;however,controllability opposing our initial expectation has a positive impact on positive *** the same time,positive emotion not only influences trust but also mediates the effect of casual attributions on trust.

关键词： casual attributions,positive emotion,trust repair,e-commerce

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction

Using LSTM and Regression Analysis for Railway Passenger Tra...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Fu Jie Tey Chih-Chi Tuan Cheng-En Cai Ming-Xuan Wu Yun-Sheng Wu Tin-Yu Wu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan

ISBN: (纸本)9781665492331

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data.

关键词： Temperature distribution Solid modeling Government Transportation Training data Predictive models Market research

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Sport science Model to Support the Professional Sports Organization Decision Making

Sport Science Model to Support the Professional Sports Organ...

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International Conference on information management and technology (ICIMTech)

作者： Tanty Oktavia Ford Lumban Gaol Takaaki Hosoda Arsyan Syahir School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

The role of information technology in sports organizations is important to analyze all aspects including how to win in the competition based on various internal and external factors. The approach that used to enhance the sports organizations is sports science, which is a concept that contributes to the development of athletes by collaborating in many fields and studies, such as anatomy, physiology, psychology, engineering, chemistry, etc. The approach of Sport science articulates the technology that blended on all of the approached. Currently, the use of sports technology or science in Indonesia is also very low in all sports categories. Usually, the management and coaches look at player statistics based on manual data or reports to view player statistics periodically. There is still a lack of exploration to use player statistics to be used by coaches to improve the ability of the players and the organization. This research will propose how to develop sports science with a business intelligence approach that used the Kimball data mart approach as decision support in the field of sports so that management and coaches can apply the correct approach to improve the capacity of players and organizational development. The result of this study will provide the model of sports science that can be implemented for a professional sports organization in Indonesia.

关键词： Business intelligence Decision making Tools Companies

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