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Telecommunications Forum (TELFOR)

作者： Dimitrije Stojanović Bogdan Pavković Nenad Četić Momčilo Krunić Luka Vidaković Departmant of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia LabSoft Novi Sad Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

Software development necessitates a robust testing plan though test development can be laborious and nonappealing task. We explore the utilization of the application artificial intelligence agents for generating and executing unit tests, enhancing the “Mostly Basic Python Problems” dataset. We employ behavior-driven development within a three-agent system to generate user stories and unit tests. Empirical results indicate improvements in branch coverage, illustrating the effective utilization of large language models in software testing and development processes.

关键词： Software testing Codes Large language models Documentation Telecommunications Test pattern generators Software development management Python

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Prototype Guided Personalized Federated Intrusion Detection System 10

Prototype Guided Personalized Federated Intrusion Detection ...

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10th IEEE Smart World Congress, SWC 2024

作者： Cheng, Long Yan, Huiru Zhou, Hanlin Wang, Ying Tang, Haichuan Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Ai Lab of Crrc Academy Beijing China

ISBN: (纸本)9798331520861

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines. © 2024 IEEE.

关键词： Federated learning

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Research on Heterogeneity Problem of Federated Learning Based on Knowledge Distillation-Translate 4

Research on Heterogeneity Problem of Federated Learning Base...

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4th International Conference on New Energy and Power engineering, ICNEPE 2024

作者： Liu, Xunjie Wang, Haotian Guo, Binghui Wang, Yutian Gao, Haiwei Beihang University School of Mathematical Sciences Center for Future Blockchain and Privacy Computing Beijing China Beihang University School of Software Beijing China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China Beijing Wuzi University School of Economics Beijing China Beijing Jiaotong University School of Mechanical Electronic and Control Engineering Beijing China

ISBN: (纸本)9798331516086

With rising computing power, data-driven AI algorithms have become essential in fields like image processing, with significant applications in electricity inspection image detection. However, competition and privacy concerns prevent data sharing between electricity companies, limiting the development of smart inspection technologies. Federated learning offers a solution, enabling companies to collaboratively train a global model without sharing raw data, thus enhancing model performance and data efficiency. When data distribution is inconsistent (non-IID), traditional federated learning faces challenges that affect training efficiency and accuracy. To address this, we propose FedTSD, a federated learning algorithm designed for electricity inspection tasks. By dynamically exchanging teacher-student roles between the global and local models, FedTSD uses a central server to transfer additional knowledge, boosting generalization and reducing overfitting. Experiments on CIFAR-10 and SVHN datasets show that FedTSD outperforms traditional methods, achieving higher accuracy and smoother training, with faster convergence, which is crucial for efficient electricity inspections. © 2024 IEEE.

关键词： Federated learning

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Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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On PID controllers for a Complex-Order Fractional Model of an Automotive Injection System

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IFAC-PapersOnLine 2024年第12期58卷 119-124页

作者： A. Altamura P. Lino G. Maione M. Kapetina M.R. Rapaić Z.D. Jeličić Department of Electrical and Information Engineering Polytechnic University of Bari Bari 70125 Italy Computing and Control Department Faculty of Technical Sciences University of Novi Sad Novi Sad 21102 Serbia

Recent studies have shown that complex-order fractional operators allow for compact modeling with simpler structures and less parameters than models based on integer-order operators and even non-integer real-order operators. This work verifies the usefulness and potential of a complex-order fractional model when used to set parameters of a PID controller regulating the common-rail pressure in the injection system of a compressed natural gas engine. Parameters are derived by a general particle swarm optimization that considers a cost function weighting the performance and robustness measures. Simulation results show the effectiveness of the complex-order fractional model-based PID controller through the response to step references, the rejection of disturbances, the low sensitivity to variations of the injection system parameters.

关键词： Complex orders complex-order fractional model PID controllers parameter optimization automotive engine injection systems

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Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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MFANet: Mixed Feature Attention Network for Port Lane Detection 42

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational efficiency

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Optimal Tracking Performance of Networked control Systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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Techno-Economic Analysis of 2nd Life Lithium-Ion Batteries Applied to Street Lighting in Uruguay

Techno-Economic Analysis of 2nd Life Lithium-Ion Batteries A...

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IEEE URUCON

作者： Mauricio Mendes Da Silva Vinicius Marins Cleff Conrado Fleck Dos Santos Emanuel Antunes Allan Braz Sérgio Frederico Pires Dias Iván Principi Control and Automation Engineering Uruguay Technological University Rivera Uruguay Eletronic and Computing Engineering Catolic University Pelotas Pelotas Brasil Renewable Energy Engineering Uruguay Technological University Durazno Uruguay

ISBN: (数字)9798350355383

ISBN: (纸本)9798350355390

This study explores the reuse of lithium-ion batteries for street lighting in Uruguay, offering a sustainable solution to battery disposal by extending their lifespan through solar-powered recharging. It assesses the feasibility, benefits, and challenges of implementing second-life batteries in public lighting, using solar irradiation data to evaluate system performance over a Typical Meteorological Year (TMY). The findings highlight the potential for reducing waste, conserving resources, and enhancing grid stability.

关键词： Lithium-ion batteries Radiation effects System performance Lighting Focusing Power system stability Lithium batteries Stability analysis Batteries System implementation

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Algorithms to Calculate the Most Reliable Maximum Flow in Content Delivery Network

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Computer Systems Science & engineering 2022年第5期41卷 699-715页

作者： Baili Zhang Keke Ling Pei Zhang Zhao Zhang Mingjun Zhong School of Computer Science and Engineering Southeast UniversityNanjing211189China State Key Laboratory of Smart Grid Protection and Control Nanjing211106China Nari Group Corporation Nanjing211106China Department of Computing Science University of AberdeenAberdeenAB243UEUK

Calculating the most reliable maximum flow(MRMF)from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network(CDN).However,SDBA,as the current state-of-the-art MRMF algorithm,is too complex to meet real-time computing *** paper proposes a set of MRMF algorithms:NWCD(Negative Weight Community Deletion),SCPDAT(Single-Cycle Preference Deletion Approximation algorithm with Time constraint)and SCPDAP(Single-Cycle Preference Deletion Approximation algorithm with Probability constraint).NWCD draws on the“flow-shifting”algorithm of minimum cost and maximum flow,and further defines the concept of negative weight *** algorithm continuously deletes the negative weight communities,which can increase reliability while keeping the flow constant in the residual *** is proven that when all negative weight communities are deleted,the corresponding maximum flow is the *** tries to approach the optimal solution to the greatest extent possible within the limited time,while SCPDAP tries to reach the probability threshold in the shortest amount of *** of these adopt the strategy of first deleting single-cycle communities(which contribute more to the reliability with lower time cost).Experiments show that,compared with SDBA,NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost,while SCPDAT and SCPDAP demonstrate better time performance and increased applicability.

关键词： Content delivery network uncertain graph maximum flow flow reliability

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