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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Hardware Resource Reliability Analysis based on Deep Learning for Virtual Machine Deployment Optimization 9

Hardware Resource Reliability Analysis based on Deep Learnin...

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9th IEEE Global Conference on Consumer Electronics, GCCE 2020

作者： Huang, Chih-Hao Huang, Bo-Hong Wu, Tin-Yu National Ilan University. Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan Taiwan

This paper proposes a new method based on the Apriori algorithm and deep learning technology to improve the flexibility of hardware deployment and to analyze hardware usage reliability for VM. This method can signific... 详细信息

ISBN: (纸本)9781728198026

关键词： Reliability analysis

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Predicting Metabolic Syndrome With Machine Learning Models of Hybrid Analysis

Predicting Metabolic Syndrome With Machine Learning Models o...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wu, Yi-Syuan Wang, Wei-Yun Wu, Hao-Yi Chen, Chao-Wen Chu, Chi-Ming Wu, Cheng-Wei Division of Trauma and Surgical Critical Care Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Master Program of E-Learning for Multimedia and Network Communications National I-Lan University Yilan Taiwan National Defense Medical Center Tri-Service General Hospital Songshan Branch School of Nursing Department of Nursing Taipei Taiwan National Defense Medical Center Department of Nursing Tri-Service General Hospital Taipei Taiwan Kaohsiung Medical University Hospital Kaohsiung Medical University Trauma and Critical Care Service Department of Surgery Kaohsiung Taiwan National Defense Medical Center School of Public Health Taipei Taiwan National I-Lan University Department of Computer Science and Information Engineering Yilan Taiwan

ISBN: (纸本)9798350324174

The study uses machine learning to determine the accuracy of different algorithms for predicting MetS. Early prediction of MetS through routine health screening is an important goal for preventive medicine. The subjects in this study were employees aged 20 to 65 years old who signed a consent form for in-hospital health screening data were collected from 2015 to 2020. Total of 3538 individuals who underwent health screening were included in this study. Baseline characteristics of the 315 participants diagnosed with metabolic syndrome and 3222 participants without metabolic syndrome. This is a machine learning approach to identify metabolic syndrome using biochemical values and lifestyle habits. We found that the F1 score for detection of metabolic syndrome by neural network is the most accurate and higher compared to other models. Our study also identified some new indicators, including heart rhythm, RBC, WBC, Hb, and uric acid, as candidates for better detection of metabolic syndrome. © 2023 IEEE.

关键词： Forecasting

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County 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 Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Resilience test case automation for LTE femtocell networks 5

Resilience test case automation for LTE femtocell networks

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Sudarsanam, P. Anand, R. Banerjee, Sumanta Hemanth, R. BMS Institute of Technology and Management Bangalore Department of Master of Computer Applications Bengaluru India CMR Institute of Technology Bengaluru Department of Information Science and Engineering Bengaluru India Nokia Solutions and Network SQUAD LEAD Bengaluru India BMS Institute of Technology and Management Bangalore Department of MCA Bengaluru India

ISBN: (纸本)9781665412728

The automated test case generation includes the manual test cases, which are converted to automated test cases through executable test scripts. It is more essential to bring out an effective resilience testing for network gateways. The automation testing plays a major role in performing time constrained and skillful work. Moreover, the network gateways will not take part in onetime investment, even when the technology or environment evolves, the test scripts are continuously patched based on the market demands. Thus, the automation testing is remaining as a challenging task to define and test automatically in a cost-effective mode. The developed device is called as femtocell, which helps to effectively densify the network and deliver a great customer experience in small indoor environments. It is checked to meet the real world scenarios under resilience test cases. It is performed by various manual test cases. Moreover, this project automates the manual test cases. © 2021 IEEE.

关键词： Automation

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction 2

Alert and Response of Earthquake and Tsunami for Community B...

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2nd International Conference on Sciences and Technology Applications, ICOSTA 2020

作者： Retno Susilorini, Rr M. I. Pamudji, Andre Kurniawan Sanjaya, Ridwan Ismail, Amrizarois Amasto, Ardhito Hayyu Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang Indonesia

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that "Smart Resilient City"model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that "Smart Resilient City"model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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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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Front-end circuit design for electroencephalography (EEG) signal 2

Front-end circuit design for electroencephalography (EEG) si...

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Indo - Taiwan 2nd International Conference on Computing, Analytics and networks, Indo-Taiwan ICAN 2020

作者： Salahuddin Morsalin, S.M. Lai, Shin-Chi Master's Program of Green Technology for Sustainability Nanhua University Chiayi County Taiwan Nanhua University Department of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781728149998

Electroencephalography (EEG) signals are recorded for knowing the electrical activity of the brain from the scalp, and the recorded waveform provides acquits into the dynamic aspects of brain activity. This study incorporates the circuit design and device development to achieve the Electroencephalography signal acquisition front-end circuit design for future Brain-computer Interface (BCI) applications. The amplitude of acquired signals should be strong enough and is usually expressed in the unit of millivolts. The data acquisition procedure consists three stages: 1) The acquisition of original EEG signal can be done by the active electrode and an instrumentation amplifier with a smaller gain;2) Improves the signal quality by using band-pass filter and band-stop filter;3) Those EEG signals convert into the digital code through the analog-to-digital converter (ADC) that will be integrated into a micro-controller. The digital code is stored into an embedded memory and is further transmitted via the Bluetooth module. The experimental results show that the system could implement the acquisition and storage of the EEG signals efficiently. The size of the printed circuit board (PCB) for the proposed design is smaller than 5.5 cm2. This system would be a benefit to all involved in the use of EEG for clinical diagnosis and monitoring, or even for the braincomputer interface. © 2020 IEEE.

关键词： Electrodes

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural networks, computer Retrospective Study

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