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2023 International Conference on Automation in Manufacturing, Transportation and Logistics, iCaMaL 2023

作者： Su, Yue Qi, Liang Zhou, MengChu Wiśniewski, Remigiusz Faculty of Innovation Engineering Macau University of Science and Technology China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark United States Institute of Control and Computation Engineering University of Zielona GÓra Poland

ISBN: (纸本)9781839539954

Petri nets (PNs) are graphical and mathematical tools used to model various discrete event systems and analyze their properties. Reachability is their fundamental property. When we use a state equation to determine a state's (marking's) reachability, the existence of legal firing sequences (LFSs) corresponding to nonnegative integer solutions (NISs) of the state equation needs to be determined. Our previous work has proposed an algorithm to determine the LFSs' existence in polynomial time. However, when a state equation has an infinite number of NISs, we cannot check all NISs to determine a marking's reachability. Hence, this work studies the relationship between the structure of an ordinary PN and the number of NISs of its state equation. Then, a method is proposed to modify the structure of the PN such that the state equation has no more than one NIS. The modified PN is used as the final model of the system, which maintains the functions of its modeled system. Its reachability can be determined in polynomial time. This work shows the application of the proposed method to a flexible manufacturing system's modeling and analysis. © The Institution of engineering & Technology 2023.

关键词： Polynomial approximation

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A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction

arXiv

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arXiv 2024年

作者： Sun, Jingyun Wei, Chi Li, Yang College of Computer and Control Engineering Northeast Forestry University Harbin China. College of Computer Science and Technology Harbin Engineering University Harbin China.

Legal charge prediction, an essential task in legal AI, seeks to assign accurate charge labels to case descriptions, attracting significant recent interest. Existing methods primarily employ diverse neural network structures for modeling case descriptions directly, failing to effectively leverage multi-source external knowledge. We propose a prompt learning framework-based method that simultaneously leverages multi-source heterogeneous external knowledge from a legal knowledge base, a conversational LLM, and related legal articles. Specifically, we match knowledge snippets in case descriptions via the legal knowledge base and encapsulate them into the input through a hard prompt template. Additionally, we retrieve legal articles related to a given case description through contrastive learning, and then obtain factual elements within the case description through a conversational LLM. We fuse the embedding vectors of soft prompt tokens with the encoding vector of factual elements to achieve knowledge-enhanced model forward inference. Experimental results show that our method achieved state-of-the-art results on CAIL-2018, the largest legal charge prediction dataset, and our method has lower data dependency. Case studies also demonstrate our method’s strong interpretability. © 2024, CC BY.

关键词： Contrastive Learning

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ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-term Load Forecasting 2

ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-...

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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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Evaluating the Performance of the OWC Satellite System Under Different Parameters Using the Optisystem Program

Evaluating the Performance of the OWC Satellite System Under...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Abood, Layla H. Al-Hilali, Aqeel Ali Galeb, Basim Saber, Ali Ali Bashar, Mustafa Department of Control and Systems Engineering University of Technology Baghdad Iraq Medical Instrumentation Engineering Department Al-Farahidi University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Cybersecurity Technology Engineering Department Al-Kitab University Kirkuk Iraq Medical Instrumentation Engineering Department Uruk University Baghdad Iraq

ISBN: (纸本)9783031828805

The potential of optical wireless communication (OWC) systems for high-speed data transfer, especially over extended distances, is being investigated more and more. However, problems including signal attenuation, dispersion, and noise pose a serious challenge to these systems’ effectiveness as transmission lengths increase. This research examines how an OWC system built using OptiSystem software is affected by different transmission lengths, ranging from 1000 to 3000 km. The results of the investigation show that the Quality Factor (QF) and the Bit Error Rate (BER) have a significant negative connection, with more signal distortion and attenuation occurring at greater distances. While rising noise power—due to attenuation and compensatory noise from optical amplifiers—further degrades signal quality by reducing the Signal-To-Noise Ratio (SNR) and Optical Signal-To-Noise Ratio (OSNR), the eye diagram's narrowing indicates increased pulse broadening and intersymbol interference. Furthermore, timing irregularities, as measured by RMS jitter and peak-to-peak jitter, became larger at greater distances, indicating the detrimental effects of attenuation on signal accuracy. The exponential increase in BER logarithm with distance highlights the increasing error rates. These results highlight the serious problems with long-distance transmission and the need for creative solutions. To further enhance the performance and dependability of high-speed OWC systems over long distances, future research should concentrate on creating sophisticated dispersion compensation algorithms, improving noise control tactics, and investigating adaptive modulation schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Timing jitter

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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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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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