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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yatsu, Naoya Shiraishi, Hiroki Sato, Hiroyuki Takadama, Keiki The University of Electro-Communications Department of Informatics Tokyo182-8585 Japan Yokohama National University Department of Electrical Engineering and Computer Science Yokohama240-8501 Japan University of Tokyo Interdisciplinary Information Science Research Division Chiba277-0882 Japan

ISBN: (纸本)9798350308365

This paper focuses on 'prototypes' as representative samples that can serve as a summary of the original dataset and proposes a novel Learning Classifier System (LCS) to generate a small number of prototypes that can achieve high classification accuracy in class classification. Concretely kNNUCS-PG is proposed to improve the sUpervised Classifier System (UCS) as one of LCSs by changing from the interval (rule) representation to the point representation to represent a prototype by employing k-nearest neighbors (kNN) to match the neighborhood prototypes. For a high classification accuracy with data reduction, selecting and generating prototypes that can maintain decision boundaries is necessary. Since UCS has generalization mechanisms, including rule merging, unnecessary rule deletion, and new rule discovery using niche Genetic Algorithm (GA), such techniques are highly useful for evolving indispensable prototypes as generalized prototypes. This paper compared conventional prototype-based methods with kNNUCS-PG in experiments using three benchmark problems and eight table datasets. kNNU CS-PG can maintain high classification accuracy even for complex problems with much overlap in the datasets. The results show that kNNUCS-PG is a robust PG that can maintain high classification accuracy while reducing data for various complex problems. © 2024 IEEE.

关键词： Self-supervised learning

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Design and Analysis of Compact UWB Circular Ring Planar Antenna for mmWave FR-II NR Bands n259, n262, n263 (V-Band), and n260 (Ka-Band) for 5G Applications 6th

Design and Analysis of Compact UWB Circular Ring Planar Ante...

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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Kumar, Pradeep Raj, Tej Kapoor, Ankush Krishna, Nippani Satya Discipline of Electrical Electronic and Computer Engineering University of KwaZulu-Natal Durban4041 South Africa Electrical Cluster SoAE UPES Uttarakhand Dehradun India Department of ECE J. N. Government Engineering College Sundernagar India Applied Science Cluster SoAE UPES Uttarakhand Dehradun India

ISBN: (纸本)9789819783281

This manuscript proposes an ultra-wideband circular ring-type planar microstrip patch antenna with a partial ground plane. The antenna achieves an impedance bandwidth of approximately 38.1 GHz, encompassing a frequency range of 36.517–74.624 GHz. The presented radiator attains a fractional bandwidth of 68.57%, with a peak gain of 5.64 dBi, cover FR-II NR frequency bands n259 (39.50–43.50 GHz), n262 (47.20–48.20 GHz), n263 (57–71 GHz), and n260 (37–40 GHz). The proposed antenna is modeled on dielectric material Taconic (TLY-5) with a dielectric constant (Εr) 2.2, a substrate width of 0.51 mm, and a loss tangent of 0.0009. The suggested element features a compact size with substrate dimensions of 10 × 6.30 mm2, exhibiting a quasi-omnidirectional radiation pattern with a maximum radiation efficiency of 96%. The simulation results, including return loss and VSWR, have been validated using CST Microwave Studio and Ansys HFSS, showing close agreement. This indicates that the proposed antenna is a strong candidate for 5G mmWave FR-II NR V-band and Ka-band wireless applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slot antennas

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DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying 31

DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Guanghui Liu, Dexi Nie, Jian-Yun Wan, Qizhi Hu, Rong Liu, Xiping Liu, Wanlong Liu, Jiaming School of Information Management Jiangxi University of Finance and Economics China Canada School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Statistics Division of the Physical Sciences The University of Chicago United States

ISBN: (纸本)9798891761964

Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and event templates. These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering their possible relationship. In this work, we propose DEGAP to address these challenges through a simple yet effective components: dual prefixes, i.e. learnable prompt vectors, where the instance-oriented prefix and template-oriented prefix are trained to learn information from different event instances and templates. Additionally, we propose an event-guided adaptive gating mechanism, which can adaptively leverage possible connections between different events and thus capture relevant information from the prefix. Finally, these event-guided prefixes provide relevant information as cues to EAE model without retrieval. Extensive experiments demonstrate that our method achieves new state-of-the-art performance on four datasets (ACE05, RAMS, WIKIEVENTS, and MLEE). Further analysis shows the impact of different components. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Application of Independent Component Analysis and Nelder–Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation

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Sensors 2024年第11期24卷 3544页

作者： Su, Te-Jen Lin, Wei-Hong Zhuang, Qian-Yi Hung, Ya-Chung Yang, Wen-Rong He, Bo-Jun Wang, Shih-Ming Department of Electronic Engineering National Kaohsiung University of Sciences and Technology Kaohsiung80782 Taiwan Department of Computer Science and Information Engineering Cheng Shiu University Kaohsiung833 Taiwan

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder–Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature. © 2024 by the authors.

关键词： Independent component analysis

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Yogi, Kottala Sri Sharma, Avinash Dankan Gowda, V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Dept of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune Telangana Hyderabad India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area. © 2024 IEEE.

关键词： Internet of things

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Black-hole-inspired thermal trapping with graded heat-conduction metadevices

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National Science Review 2023年第2期10卷 131-138页

作者： Liujun Xu Jinrong Liu Peng Jin Guoqiang Xu Jiaxin Li Xiaoping Ouyang Ying Li Cheng-Wei Qiu Jiping Huang Department of Physics State Key Laboratory of Surface Physics and Key Laboratory of Micro and Nano Photonic Structures (MOE) Fudan University Department of Electrical and Computer Engineering National University of Singapore Graduate School of China Academy of Engineering Physics School of Materials Science and Engineering Xiangtan University Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang University International Joint Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University

The curved space-time produced by black holes leads to the intriguing trapping *** far,metadevices have enabled analogous black holes to trap light or sound in laboratory ***,trapping heat in a conductive environment is still challenging because diffusive behaviors are *** by black holes,we construct graded heat-conduction metadevices to achieve thermal trapping,resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal *** experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the *** heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy *** results also provide an insight into correlating transformation thermotics with other disciplines,such as cosmology,for emerging heat control schemes.

关键词： thermal trapping graded metadevices imitated advection

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Enhanced Knee Osteoarthritis Classification using a Modified EfficientNetB5 Architecture with Self-Attention Mechanism

Enhanced Knee Osteoarthritis Classification using a Modified...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Minhazul Islam Md. Rakib Hossain Md. Musab Us Saber Shakin S. M. Mahedy Hasan Farzana Akter Azmain Yakin Srizon Md. Farukuzzaman Faruk Md. Faruk Hossain Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Knee osteoarthritis, a debilitating and threatening musculoskeletal condition, underscores the critical importance of early detection for timely treatment. Given the seriousness of this health issue, this study explores the use of deep learning as an effective diagnostic tool. The study introduces an innovative method for diagnosing and classifying knee osteoarthritis, achieved through the modification of an EfficientNetB5 architecture and the integration of the Self Attention mechanism, addressing current limitations. The attention mechanism selectively emphasizes crucial features, enabling the model to prioritize and weigh pertinent information in the classification process. To ensure a strong approach, a custom callback is used in training coupled with strategies to solve issues of class imbalance. This approach greatly enhances the diagnosis of musculoskeletal health, meeting the critical need for early intervention with an exceptional 90.86% accuracy with a comparatively smaller dataset. The intentional alteration of the model, along with an emphasis on the Self Attention mechanism, signifies a break from custom and a sincere attempt to improve medical image analysis. The strategy’s proven effectiveness not only supports it but also points out areas for improvement, perhaps leading to more accurate diagnostic tools. This technique represents a significant advancement in the treatment of osteoarthritis in the knee, a condition that may have fatal implications.

关键词： Training Musculoskeletal system Attention mechanisms Accuracy Instruments computer architecture Osteoarthritis Information technology Optimization Medical diagnostic imaging

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Beamforming Design for Multi-Sector BD-RIS Assisted FL with AirComp

Beamforming Design for Multi-Sector BD-RIS Assisted FL with ...

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IEEE Conference on Wireless Communications and Networking

作者： Xiaolong Xu Ying Ju Zhenghao Li Xiangwang Hou Lei Liu Shahid Mumtaz Celimuge Wu School of Telecommunications Engineering Xidian University Xi'an China Department of Electronic Engineering Tsinghua University Beijing China Department of Engineering Nottingham Trent University Nottingham UK Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Federated learning (FL) is a promising approach that effectively and securely harnesses the vast amounts of data generated by the rapid proliferation of internet-connected devices. In FL, the transmission of model parameters over wireless channels plays a pivotal role in determining system performance. To optimize the wireless environment and boost communication efficiency, we present a novel FL beamforming design scheme that integrates multi-sector beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with over-the-air computation (AirComp). The scheme leverages the waveform superposition property of wireless signals, using AirComp to rapidly aggregate the global model in FL. Additionally, the scheme utilizes BD- RIS to flexibly manip-ulate communication beams, improving user channel conditions and further reducing model aggregation errors. Specifically, we evaluate the impact of this design on FL systems and derive an upper limit on the gap between training loss and optimal loss. To minimize this gap, we formulate a joint optimization problem of BD- RIS passive beamforming and base station receive beamforming, and we propose an optimization algorithm based on successive convex approximation (SCA) and block coordinate descent (BCD) to solve it. Simulation results confirm that our de-sign significantly enhances user channel conditions and improves FL performance, with the benefits becoming more pronounced as the number of BD- RIS reflecting elements increases.

关键词： Training Array signal processing Federated learning Computational modeling Atmospheric modeling Wireless networks System performance Reconfigurable intelligent surfaces Communication system security Optimization

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An efficient data aggregation scheme with local differential privacy in smart grid

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Digital Communications and Networks 2022年第3期8卷 333-342页

作者： Na Gai Kaiping Xue Bin Zhu Jiayu Yang Jianqing Liu Debiao He School of Cyber Science and Technology University of Science and Technology of ChinaHefeiAnhui230027China Department of Electronic Engineering and Information Science University of Science and Technology of ChinaHefeiAnhui230027China Department of Electrical and Computer Engineering University of Alabama in HuntsvilleHuntsvilleAL35899USA Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education School of Cyber Science and EngineeringWuhan UniversityWuhanBeihu430072China

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

关键词： Local differential privacy Data aggregation Smart grid Privacy preserving

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Enhancing Time Series Prediction in Industrial Processes: A Self-Attention BiLSTM Approach

Enhancing Time Series Prediction in Industrial Processes: A ...

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Robotics, Control and Automation engineering (RCAE), International Conference on

作者： Sicheng Wen Jiajun Xu Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Department of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

The development of data-driven soft sensors for modeling complex data, particularly in scenarios characterized by strong nonlinearity, high dimensionality, cross-correlation and autocorrelation, remains a significant challenge in process data analysis. Bidirectional Long Short-Term Memory (BiLSTM) networks are highly effective in capturing both forward and backward dependencies within sequences, thereby enhancing the extraction of temporal features from time series data. However, BiLSTM networks may struggle to capture global dependencies, especially in long sequences. To address this limitation, the self-attention mechanism, which excels in modeling global relationships across all positions in the input sequence, can be integrated with BiLSTM by applying weighted attention to the two different directions of BiLSTM, thereby enhancing its performance. This study proposes a novel method called SA-BiLSTM, which combines the strengths of the BiLSTM network and the self- attention mechanism. The self-attention layer captures global dependencies, while the BiLSTM layer extracts comprehensive temporal features in both directions, which are then passed to a fully connected layer for final prediction. Extensive comparisons with traditional methods such as BiLSTM, LSTM with added self-attention mechanism, LSTM, RNN, and SVM validate the superior performance of the proposed SA-BiLSTM method in handling complex time series data.

关键词： Support vector machines Sensitivity Soft sensors Impurities Time series analysis Bidirectional long short term memory Feature extraction Prediction algorithms Robot sensing systems Real-time systems

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