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2024 IEEE international conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Liao, Kuo-Cheng Wu, Ze-We Chen, Shih-Hung Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a system of multi-point capacitive sensing smart garment designed for posture detection. This smart garment utilized multiple capacitive sensors placed at different positions to monitor changes in body posture movements. These sensors were capable of detecting subtle variations between the garment and the wearer's body and translating this data into understandable information. By analyzing the collected data, this smart garment can provide real-Time monitoring of the user's posture, such as standing, sitting, or lying down. © 2024 IEEE.

关键词： garment multi-point capacitive sensing posture respiration

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multi-objective Model and Genetic Algorithm for multisource multicast VNF Service Chain Deployment Problem

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2024年第6期51卷 562-571页

作者： Liu, Jintao Zhao, Xuelin Ma, Ruiyan Xuan, Hejun Guo, Xinming Zhai, Wenjie School of Architecture and Civil Engineering Xinyang Normal University Henan Xinyang46400 China School of Computer and Information Technology Xinyang Normal University Henan Xinyang46400 China

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a software-defined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%–15.61%, 11.2%–26.4%, 8.3%–17.6% and 7.8%–14.4% than that of the compared algorithm, respectively. © (2024), (international Association of Engineers). All rights reserved.

关键词： Genetic algorithms

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Proposed multi-Pathization Method with SDN for multiple Paths for TCP Throughput 11

Proposed Multi-Pathization Method with SDN for Multiple Path...

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11th international conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Kobayashi, Yuuki Ito, Yoshihiro Fujikami, Ryotaro Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9798350355314

In this paper, we propose an innovative method to control the increasing traffic volume caused by the proliferation of IoT devices, utilizing Software Defined Networking (SDN). Our method leverages a genetic algorithm, a powerful numerical optimization technique, to determine the optimal forwarding probability that maximizes Quality of Service (QoS) across the network. We experimentally evaluate the TCP throughput of the proposed method under various conditions in a multi-route network topology. The results of these experiments confirm the effectiveness and robustness of our scheme, showing significant improvements in network performance compared to traditional methods. This approach is particularly valuable for future network environments where IoT devices are expected to dominate, providing a scalable solution to manage network congestion and maintain high QoS standards. The potential applications of our method extend to various IoT-driven industries, making it a crucial tool for network administrators in the era of ubiquitous connectivity. © 2024 IEEE.

关键词： Genetic algorithms

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Design and implementation of a multi-point temperature acquisition system based on WiFi 3

Design and implementation of a multi-point temperature acqui...

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3rd international conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Qin, Yujuan School of Electronic Information Engineering Lanzhou Institute of Technology Lanzhou China

ISBN: (纸本)9781510680531

This article designs a multi-point temperature acquisition system based on WiFi, which includes two parts: a lower computer and an upper computer. The lower computer is divided into a collection part and a terminal part. The collection part uses a microcontroller as the main control chip, which sends the data collected by the temperature sensor to the terminal through a wireless transmission module. The terminal part uses an LCD display module to display the temperature of each collection point. When the temperature exceeds the threshold set by the upper computer, the microcontroller sends an alarm through a buzzer. At the same time, the terminal device can transmit all information to the upper computer through serial communication, and the upper computer is responsible for displaying and processing the temperature value, The upper computer can set the temperature threshold for the operation of the collection point downwards. © 2024 SPIE.

关键词： Microcontrollers

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Residual-Based Convolutional Correlation Analysis for Enhanced Models in SSVEP Classification 4

Residual-Based Convolutional Correlation Analysis for Enhanc...

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4th international conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Nie, Zihao Wang, Raofen School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9798350384062

Steady-state visual evoked potentials (SSVEP) systems are considered ideal for practical brain-computer interfaces, with a focus on precise decoding of multi-class datasets in short time. Convolutional Correlation Analysis (Conv-CA) combines convolutional neural networks with canonical correlation analysis, extracting features from electroencephalography signals and template signals in two branches. These features are then classified using a correlation layer. This paper introduces residual connections to the network, proposing the improved Residual-based Convolutional Correlation Analysis (ResConv-CA) model. All experiments were validated on a 40-class public dataset, and considering the 140ms visual delay of SSVEP, we selected an effective data length of [0.64s, 5.64s]. In experiments, the ResConv-CA model showed increased average classification accuracy and improved accuracy for challenging subjects. This confirms the improved stability and accuracy of the enhanced model with the addition of residual connections. © 2024 IEEE.

关键词： Brain computer interface

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Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications 15

Human Activity Recognition Using Deep Learning Techniques fo...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shekhar, Raj Tomar, Deepak Singh Sharan, Bhagwati Pateriya, R.K. Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350370249

The idea of smart healthcare is gradually gaining traction with the rapid advancement in information technologies. Smart healthcare is the intelligent transformation of the current medical system to make it more reliable, efficient, and individualized through the intelligent use of next-generation technologies like artificial intelligence, and the Internet of Things (IoT). This study offered the recent advancement of Deep learning (DL) techniques for healthcare systems, and the use of DL techniques to identify human physical activity with wearable sensors. In this paper, convolutional neural networks (CNN), CNN-LSTM, multi Headed CNN-LSTM, and multi Headed CNNBiLSTM are used. The obtained outcomes of these Deep Learning models are compared in terms of accuracy, precision, recall and F1 score. The multi-Headed CNN-BiLSTM model exhibits better performance than the rest of the models when applied to the UCI HAR dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Extreme Learning Machine Soft-sensing Model of SMB Chromatographic Separation Process Based on multi-strategy Fusion Coati Optimization Algorithm

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第2期52卷 447-463页

作者： Xing, Cheng Yang, Qing-Da Wang, Jie-Sheng ShuangGuan, Yi-Peng Sun, Yong-Cheng School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114044 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

Simulated Moving Bed (SMB) chromatographic separation technology is an innovative method that combines traditional fixed-bed adsorption operation with real moving bed (TMB) chromatographic separation technology. In order to accurately evaluate the purity and yield of extracted and residual in the SMB chromatography separation process, a extreme learning machine soft-sensing model based on multi-strategy fusion coati optimization algorithm (COA) was proposed. This model selects auxiliary variables based on the analysis of the SMB chromatographic separation process. The proposed multi-strategy fusion COA includes logistic mapping, prey position vector and meme grouping to enhance the randomness and diversity of COA. Simulation results demonstrate that the optimized ELM can proficiently predict the key pecuniary and technical index of the SMB chromatography separation process. © (2025), (international Association of Engineers). All rights reserved.

关键词： Machine learning

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A Lightweight multi-Vehicle Detection Method Based on multi-Scale Feature Fusion and multi-Head Self-Attention 7

A Lightweight Multi-Vehicle Detection Method Based on Multi-...

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7th IEEE international conference on computer and Communication engineering Technology, CCET 2024

作者： Zhang, Yong-Mei Ao-Guo Du, Zhi-Rong Oi, Hao-Yu School of Information Science and Technology North China University of Technology Beijing100144 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9798350355673

Aiming at the problems of high computational complexity, low recognition accuracy, and poor real-time performance of vehicle detection methods based on deep learning, this paper proposes a lightweight multi-vehicle detection method based on multi-scale feature fusion and multi-head self-attention. By adopting the FasterNet to replace the original CSPDarkNet53 backbone network of YOLOv8, and combining the idea of partial convolution, the C2f module in the neck is reconstructed to achieve efficient fusion of multi-scale features. After the last layer of the backbone network, a multi-head self-attention mechanism is introduced to enhance the ability of the model to understand complex spatial relationships and contextual information in images. Furthermore, the Focal-EIoU loss function is used to replace the CIoU loss function, accurately defining the aspect ratio of the predicted bounding box and alleviating the issue of imbalance between positive and negative samples. Experiment results show that the proposed method achieves 0.1% higher accuracy, 3.08% higher mean average precision, and 1.95 higher frames per second compared with the original model on the UA-DETRAC dataset. © 2024 IEEE.

关键词： Deep learning

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Distributed Computation of Source, Target, and Mixed Strongly Connected Components in multi-agent Systems 16th

Distributed Computation of Source, Target, and Mixed Stron...

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16th APCA international conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Ramos, Guilherme Pequito, Sérgio Department of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Lisbon1049-001 Portugal

ISBN: (纸本)9783031817236

Strongly Connected Components (SCCs) play a crucial role in understanding the structural properties of directed graphs, with a plethora of applications across various domains including engineering, computer science and social sciences. In this paper, we propose a method for computing both the source SCCs and target SCCs in a distributed fashion. Our method leverages the decentralized nature of distributed systems to efficiently identify SCCs within large-scale graphs. We illustrate the proposed methods with examples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed computer systems

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Accurate Photovoltaic Forecasting through a multi-connected Structural Network 4

Accurate Photovoltaic Forecasting through a Multi-connected ...

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4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xie, Jiangshan Hu, Yanming Rong, Fangying International Business School Henan University Zhengzhou China School of Information Communications Engineering Beijing Information Science & Technology University Beijing China School of Information Science and Engineering Shandong Agricultural University Tai'an China

ISBN: (纸本)9798350359589

With the growing demand for renewable energy sources, the importance of accurate photovoltaic (PV) forecasting has become crucial for effective energy management. Predicting PV output remains a challenge due to the need for high accuracy and low computational complexity. This paper introduces a novel method for general PV forecasting through a multi-connected structural network to pursue accurate PV generation. We explore the impact of different neural network interactions on actual power data within the network layer and test its relevance by constantly changing parameters. Stacked gate recurrent unit (GRU) and bidirectional long short-term memory (BiLSTM) networks are used to learn universal meteorological data feature as input for network layers, and after incorporating an attention mechanism, they are trained in a hierarchical manner of multi model fusion, interaction, and infiltration. Experimental results indicate that the proposed method can achieve competitive performance when compared to traditional single approaches. © 2024 IEEE.

关键词： Attention CNN Feature fusion PV forecasting

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