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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

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. © 2024 IEEE.

关键词： Accident prevention

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Near-Far Field Boundary Analysis and Transmit Covariance Optimization for Dual-Polarized XL-MIMO Communications

Near-Far Field Boundary Analysis and Transmit Covariance Opt...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zeng, Shuhao Di, Boya Zhang, Hongliang Han, Zhu Poor, H. Vincent School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Princeton University Department of Electrical and Computer Engineering United States School of Electronics Peking University Beijing China University of Houston Electrical and Computer Engineering Department United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798350368369

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to play an important role in future sixth generation (6G) networks. Most existing works in this area focus on single-polarized XL-MIMO, where transceivers transmit and receive signals in only one polarization direction, leading to degraded data rates. To improve multiplexing performance, in this paper, we investigate downlink XL-MIMO networks with dual-polarized antennas. However, unlike conventional dual-polarized massive MIMO, the cross-polarization discrimination (XPD) of channels vary across base station antennas in dual-polarized XL-MIMO due to the enlarged antenna aperture, leading to following two challenges. First, conventional near-far field boundary is insufficient as it only accounts for phase differences across array elements while irrespective of XPD differences. Second, existing transmit covariance optimization methods developed for dual-polarized massive MIMO cannot be directly utilized, since they are developed based on uniform XPD and pathloss assumptions. To address these challenges, we model the variations of XPD across antennas, based on which a non-uniform XPD distance is introduced to complement existing near-far field boundary. Based on the new distance criterion, we propose an efficient scheme for optimizing the transmit covariance, which considers the non-uniform XPD and pathloss. Numerical results validate our analysis and demonstrate the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： Carrier communication

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Informative and Long-Term Response Generation using Multiple Suggestions and User Persona Retrieval in a Dialogue System

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APSIPA Transactions on Signal and Information Processing 2024年第2期13卷

作者： Hsu, Jia-Hao Chen, Tsai-Yi Wu, Chung-Hsien Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Artificial Intelligence National Cheng Kung University Tainan Taiwan

Enhancing user satisfaction in dialogue systems relies on their ability to understand users and generate responses that meet their expectations. This study proposes a dialogue system that incorporates the Multi-Suggestions Transformer (MST) to generate informative and long-term responses. The MST combines empathy suggestions, system persona suggestions, and knowledge suggestions to produce comprehensive and informative responses. Additionally, the system employs a persona detection model and a persona extraction model to extract the user persona from current sentences and retrieve the most suitable user persona from the dialogue history. This facilitates long-term conversations by enabling the system to remember and respond to sentences relevant to the user persona. The proposed MST-based dialogue system outperforms the baseline in terms of informativeness, as evidenced by higher scores in BLEU, BERT-score, Distinct-n, and Perplexity on the Blended Skill Talk and Multi Session Chat datasets. Furthermore, two novel evaluation metrics, PerP and PerB, introduced in this study demonstrate the system’s effective utilization of the user persona for achieving long-term dialogue. Human subjective evaluation indicates that our model consistently outperforms the baseline, achieving superior scores of 68%, 56%, 52%, and 64% in the four subjective metrics. © 2024 J.-H. Hsu, T.-Y. Chen and C.-H. Wu.

关键词： Speech processing

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Hybrid BiLSTM-PSO Approach for Multi-Metering Point Day-Ahead electrical Load Forecasting

Hybrid BiLSTM-PSO Approach for Multi-Metering Point Day-Ahea...

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Power Energy Systems and Applications (ICoPESA), International Conference on

作者： Noelyn M. De Jesus Enrique D. Festijo Gerard Francesco DG. Apolinario Dylan Josh Domingo Lopez Graduate School Technological Institute of the Philippines Manila Philippines Electrical Engineering Technological Institute of the Philippines Manila Philippines Electrical Engineering and Computer Science Chung Yuan Christian University Taoyuan Taiwan

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

Accurate individual load profile forecasting is essential for utilities to optimize resource allocation. A novel BiLSTM-PSO approach is proposed to address this challenge. By capturing short-term dependencies in load patterns, it significantly improves accuracy for each metering point. Historical data (BATELEC I, 2018–2023) is analyzed using statistical methods like Pearson correlation, cointegration, and Granger causality to identify these dependencies. A Bi-directional Long Short-Term Memory (BiLSTM) network is then optimized by Particle Swarm Optimization (PSO) for more accurate daily load profile forecasting. The BiLSTM-PSO approach achieves significant reductions in error metrics compared to the baseline model: 49.72% (R-squared), 17.95% (MAE), 17.42% (RMSE), and 30.88% (MSE). Outperforming alternatives, the Bi-LSTM approach offers superior forecasting accuracy. The results recommend integrating a BiLSTM-PSO model into information systems for real-time load forecasting, improving procurement planning and grid management efficiency for electric utilities.

关键词： Procurement Accuracy Recurrent neural networks Load forecasting Predictive models Forecasting Particle swarm optimization Long short term memory Optimization Load modeling

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Ti02-based Flexible Integrated Photonics for High-sensitivity Temperature Sensing

Ti02-based Flexible Integrated Photonics for High-sensitivit...

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2022 Conference on Lasers and Electro-Optics, CLEO 2022

作者： Zhao, Xuan Fan, Bo Veeraraghavan, Ashok Robinson, Jacob Applied Physics Graduate Program Smattey-Curl Institute Rice University HoustonTX77005 United States Electrical & Computer Engineering Rice University HoustonTX77005 United States

We report a flexible temperature sensor based on Ti02 photonics that shows double the sensitivity compared to silicon photonics. This high sensitivity and biocompatibility pave the way towards point-of-care temperatur... 详细信息

ISBN: (纸本)9781957171050

关键词： Biocompatibility

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Light-induced tunable optical properties of 1T-TaS2 at different temperatures

Light-induced tunable optical properties of 1T-TaS2 at diffe...

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CLEO: QELS_Fundamental science, QELS 2022

作者： Li, Weijian Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Electrical and Computer Engineering Rice University HoustonTX77005 United States

The c-axis permittivity of 1T-TaS2 - a quasi-2D charge-density-wave material - changes upon illumination due to light-induced reorganization of CDW stacking. Here we probe the mechanism of this reorganization and find... 详细信息

ISBN: (纸本)9781557528209

关键词： Charge density

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Application of Artificial Neural Network Method for Forecasting Weekly Electricity Load Based on Daily Pattern Classification

Application of Artificial Neural Network Method for Forecast...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Husni Ramzi Adriman Ira Devi Sara Rio Syahputra Graduate program of Electrical Engineering Faculty of Engineering Universitas Syiah Kuala Banda Aceh Aceh Department of Electrical and Computer Engineering Faculty of Engineering Universitas Syiah Kuala Banda Aceh Aceh

Accurate weekly electricity load prediction is of utmost importance for electricity providers to ensure uninterrupted power supply to customers. This study applies an Artificial Neural Network (ANN) to achieve precise weekly electricity load prediction. The dataset used for the ANN model consists of three months’ worth of data, including daily workload profiles, holiday work profiles, temperature, and humidity. For model training, 90% of the data is utilized with the Levenberg-Marquardt algorithm, while the remaining 10% is used for testing. The Mean Average Percentage Error (MAPE) is employed as the error metric. Based on the test results, the weekly load prediction error rate using ANN is determined to be 1.78% based on the MAPE value.

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Grating Coupled Attachment of Optical Fiber Arrays for in Situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in Si...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joel B. Slaby Hannah M. Dattilo C. Alex Kaylor Ronald D. Schrimpf Daniel M. Fleetwood Stephen E. Ralph Robert Reed Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fibers Radiation effects X-ray lasers Waveguide lasers Fiber gratings Robustness Optical fiber devices Arrayed waveguide gratings Photonics Optical arrays

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NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improve the Performance of Pretrained Language Model in BioASQ 10b Phase B

NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improv...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Ting, Hao-Hsuan Zhang, Yu Han, Jen-Chieh Tsai, Richard Tzong-Han Interdisciplinary Program of Electrical Engineering and Computer Science National Central University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan IoX Center National Taiwan University Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taiwan

This paper presents our system for the BioASQ10b Phase B task. For ideal answers, we used the fine-tuned BioBERT model on the MNLI dataset to construct sentence embeddings and combined it with BERTScore to select sentences from the provided Snippets as answers. For the exact answers, we also used the BioBERT model and used the snippet scores generated from the ideal answer selection model to predict the exact answers for factoid and list questions. The exact answers of our fifth test batch ranked second place. In addition, the ideal answers we submitted achieved first place in the ROUGE score in all test batches from batch second to fifth. © 2022 Copyright for this paper by its authors.

关键词： Computational linguistics

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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