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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Dong, Enchong Luan, Wenting School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China

Given the uncertain, random, and volatile characteristics of power load, accurate forecasting of power load becomes challenging. To address this issue, this paper proposes an optimized prediction model by using a modified Adaptive White Noise Complete Integrated Empirical Modal Decomposition Algorithm (ICEEMDAN) and the improved Whale Optimization Algorithm (IWOA) for bidirectional short-term and long-term memory networks (BiLSTM). First of all, Australian power load data is preprocessed in this study. Then, the load data is decomposed into a series of sequences by using ICEEMDAN. After that, the improved whale algorithm is employed to analyze the BiLSTM super parameter optimization. Finally, each decomposed component is input into the IWOA-BiLSTM model for load forecasting. The results demonstrate that the proposed algorithm achieves higher accuracy in power load forecasting, compared to individual benchmark models and most portfolio models, indicating its applicability and practical value. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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Design of an Optimization Scheme for Compressed Circuits in Hardware Implementation of Large Number Multiplication

Design of an Optimization Scheme for Compressed Circuits in ...

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2023 International Conference on Electronic Materials and Information engineering, EMIE 2023

作者： Su, Cheng Xia, Hong Zhao, Pan Lei, Wenyuan College of Control and Computer Engineering North China Electric Power University No. 2 Beinong Road Huilongguan Street Changping District Beijing010-80798509 China

ISBN: (纸本)9781510671331

The frequent use of 1024-bit large number multiplication in encryption algorithms like SM9 often involves parallel execution of multiple small-bit Booth multipliers. However, this design approach leads to significant area consumption in the compression circuit and intermediate registers. In this paper, we propose a novel reconfigurable and reusable compressed circuit design that reduces the area of the compression circuit without compromising speed, thus minimizing the overall area of the large number multiplication circuit. The proposed design is simulated using Design Compiler in the SMIC0.18 technology library under the SSG process corner. The results demonstrate a 21% reduction in circuit area and a 2% decrease in power consumption. © 2023 SPIE · 0277-786X ·

关键词： Timing circuits

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Integrated Energy-Efficient Distributed Link Stability Algorithm for UAV Networks

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computers, Materials & Continua 2024年第11期81卷 2357-2394页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Razaz Waheeb Attar Farman Ali Ahmed Alhomoud Babar Shah School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia College of Technological Innovation Zayed UniversityDubai19282United Arab Emirates

Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control *** these networks,nodes function as senders,receivers,and *** such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely *** the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power *** optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in *** paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in *** energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient *** link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of *** simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing *** evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.

关键词： FANET UAV internet of UAVs relay mechanism energy consumption routing IEE-DLSA

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Orthogonal Dual-channel based Wireless Charging System with Free Rotation and Simultaneous Data Transmission

Orthogonal Dual-channel based Wireless Charging System with ...

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China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Hao Liu Junhao Chang Zhenjie Li Henglin Chen College of Electrical Engineering Zhejiang University Hangzhou China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

An orthogonal dual-channel-based wireless charging system is designed to desensitize the wireless charging system to free rotation and transmit energy and data simultaneously. The radial magnetic flux and rectangular coil make the power transmission channel insensitive to rotation and have a certain degree of anti-misalignment ability and the spiral tube-shaped magnetic coupling structure also has anti offset ability when decoupled from the power channel. In addition, the channel is rotationally symmetric. A simple decoding circuit is designed to achieve data extraction. Simulation results based on finite element and PLECS software verified the decoupling and anti-misalignment performance, verifying the proposed WCS’s feasibility.

关键词： Wireless communication Magnetic flux Spirals Simulation Inductive charging Power transmission Software Decoding Data mining Magnetic circuits

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Multi-object Crowd Real-time Tracking in Dynamic Environment Based on Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 386-394页

作者： Zhang, Fu-Quan Ma, Lin-Juan College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In this paper, it proposes a new lightweight neural network detection model and tracking algorithm that enables us to perform multi-object crowd tracking. The technique applied in our study is known as object detection and tracking which is based on neural network and can be calculated and reproduced intelligently according to the movement and behavior of the tracked crowd. Also, a number of experiments have been carried out to test the validity of the accuracy in the algorithm. And it is indicated that the results of the experiments have better performance and can contribute to multi-object crowd real-time tracking with high accuracy, which is similar to the state-of-the-art. All our preliminary results throw light on the nature of detection and tracking and result in a solution of multi-object crowd tracking, and it proves to be fruitful and encouraging. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Object detection

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End-to-end Flow Scheduling Optimization for Industrial 5G and TSN Integrated Networks

End-to-end Flow Scheduling Optimization for Industrial 5G an...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Houling Li, Mingyan Gu, Fuqiang Li, Qihao Zhang, Weiting Guo, Songtao Chongqing University College of Computer Science Chongqing400044 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Jilin University College of Communication Engineering Changchun130015 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100091 China

ISBN: (纸本)9798350351255

The integrated of the fifth generation (5G) and time-sensitive networking (TSN) is a promising approach to meet the requirements of deterministic forwarding with extremely low latency and high flexibility for the Industrial Internet of Things (IIoT). However, due to the large dissimilarity of protocol stacks, the 5G system normally serves as a logical bridge for TSN, performing hold-and-forward operations in Base Stations (BSs) under the current 5G-TSN frameworks. To provide ubiquitous and seamless connectivity for IIoT devices, this paper focuses on the integrated enhancement of 5G and TSN by optimizing the scheduling of time-sensitive flows with end-to-end latency of 5G-TSN transmission taken into consideration. Specifically, we propose a novel architecture named GF-CQF, which combines 5G Grant-free (GF) access with TSN Cyclic Queuing and Forwarding (CQF). Subsequently, the distributed flow scheduling problem based on GF-CQF architecture is established due to the high timeliness demands. To alleviate the impact of the uncertainty inherent in 5G channels on end-to-end deterministic transmission, a feature-aware decentralized real-time scheduling (FDRS) policy based on Multi-agent Reinforcement Learning is proposed. FDRS allows each agent at BS to adaptively allocate TSN injection slots for flows mainly based on dynamic 5G transmission performance and TSN network resource state so that TSN queue overflow can be avoided and flow delay constraints can be guaranteed. Simulations show that FDRS offers superior scheduling capabilities under the limited TSN queue resources. © 2024 IEEE.

关键词： 5G mobile communication systems

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Deep Learning-based Source Code Classification and Visualization of Decision Rationales 28th

Deep Learning-based Source Code Classification and Visualiza...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Oeda, Shinichi Mitsumori, Seiya Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students' proficiency levels. For this purpose, an objective evaluation of each student's coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users. © 2024 The Authors.

关键词： Adversarial machine learning

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NEW TOOL FOR SYSTEM IDENTIFICATION AND PREDICTIVE control BASED ON NEURAL NETWORKS USING LABVIEW SOFTWARE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2022年第2期84卷 91-110页

作者： Alshareefi, Hamid Lupu, Ciprian Alboudkhal, Hayder Ismail, Laith Dept. of Automatic Control and Computers University POLITEHNICA of Bucharest Romania Ministry of Electricity Iraq Dept. of Computer Technology Engineering AL-TURATH University College Baghdad Iraq

In this paper, a new tool for system identification and model predictive control (MPC) has been developed. The mathematical approximation of the model identification was derived using the neural network theory. The emphasis of this paper is to employ a multi-layer recurrent neural network with three layers to match an autoregressive-moving average (ARMA) model in the identification stage to provide the mathematical model for the model predictive controller in the control stage. The tool was designed using LabVIEW software and the MathScript facility. The tool can be used to identify and control simulated or real systems using the stored input/output data of the system. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Recurrent neural networks

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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