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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Angi, Antonino Sacco, Alessio Alberti, Enrico Marchetto, Guido Esposito, Flavio Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350333732

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms. © 2023 IEEE.

关键词： Network management

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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Efficient Tool Condition Monitoring Using SOM-Hebb Classifier on FPGA 6

Efficient Tool Condition Monitoring Using SOM-Hebb Classifie...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Akanksha, A. Somani Kokate, R.D. Abhilasha, Mishra Maharashtra Institute of Technology Department of Electronics & Computer Engineering Aurangabad India College of Engineering Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350304480

Tool condition monitoring is vital for enhancing productivity, reducing costs, and improving product quality in manufacturing industries. Existing approaches, such as threshold-based methods, sensor-based methods, and machine learning-based techniques, have limitations in terms of accuracy, adaptability, interpretability, and computational costs. This research proposes a tool condition monitoring system based on the Self-Organizing Map (SOM) algorithm and the Hebbian learning algorithm. The system is implemented on a Field-Programmable Gate Array (FPGA) platform for real-time processing in manufacturing environments. A dataset is acquired using sensors, pre-processed, and divided into training and testing sets. The SOM-Hebb classifier is designed with appropriate parameters, and the tool condition monitoring algorithm involves projection histograms, discrete Fourier transform (DFT), and the SOM-Hebb classifier. The FPGA implementation achieves high-speed processing and meets timing constraints. Simulation results demonstrate the system's functionality and accuracy, with a classification accuracy of 99.22%. The system exhibits a processing time of 35 clock cycles. This research provides a promising approach for efficient and accurate tool condition monitoring in manufacturing industries. © 2023 IEEE.

关键词： Field-Programmable Gate Array (FPGA) Hebbian learning algorithm Real-time processing Self-Organizing Map (SOM) Tool condition monitoring

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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning

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Neural Computing and Applications 2024年第25期36卷 15545-15565页

作者： Gong, Yiyun Yu, Haibo Kang, Li Sun, Chaoli Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Computer Science and Technology North University of China Taiyuan030051 China School of Environment and Safety Engineering North University of China Taiyuan030051 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China

This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance surrogate-assisted evolutionary optimization for medium-scale expensive problems. The method attempts to address the challenges posed by multi-polar and multi-variable coupling properties in such problems. During the iteration, TSLL splits the optimization process into two distinct stages. First, two unsupervised feature learning techniques: principal component analysis (PCA) and Autoencoder, are utilized to improve the representation of potential optimal samples in the solution space. PCA is used for feature reduction, extracting the most important features. On the other hand, Autoencoder focuses on reconstructing features within the medium-scale solution space. To ensure comprehensive exploration of the entire solution space, TSLL employs two distinct surrogate modeling approaches along with Q-learning in the second stage to dynamically select the mutation strategy for the differential evolution operator. Numerous experiments demonstrate the superiority of TSLL over five state-of-the-art surrogate-assisted approaches and two sophisticated evolutionary algorithms including the winner of CEC 2017 on medium-scale benchmark problems and a wind farm layout problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and Secure Data Using Li-Fi Technology 13

An Advanced Rivest Cipher 4 Algorithm to Transfer Fast and S...

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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Msallam, Mohammed Majid Samet, Refik Ankara University Computer Engineering Department Ankara Turkey University of Technology Control and Systems Engineering Department Baghdad Iraq

ISBN: (纸本)9798350340891

The Light-Fidelity (Li-Fi) is a wireless communication technology that is light-based and can complete wireless fidelity (Wi-Fi) technologies for many applications. Li-Fi technology which uses light spectrum is a technology for solving the lack of radio spectrum at Wi-Fi. One of the main benefits of Li-Fi technology is the ability to communicate by light with a high level of safety. However, the signal of Li-Fi technology is the same as other signals that are susceptible to eavesdropping because no security system is infallible. The objective of this paper is to decrease the computational cost of encryption and decryption and keep the security of transformed data when transmitted via Li-Fi technology. This study proposes the advanced Rivest Cipher 4 (ARC4) algorithm that will be used in the physical layer of Li-Fi applications. The novelty will be in the pseudo-random generation algorithm (PRGA) which is part of Rivest Cipher 4 (RC4). The data will become an index of the s-array and get the value of that index in the s-array instead of using the XOR operation. The proposed ARC4 was evaluated by the National Institute of Science and Technology (NIST) test. The obtained results illustrate that ARC4 is more robust and effective than RC4 when the data is transformed by Li-Fi communication. © 2023 IEEE.

关键词： Wi-Fi

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Consistent Counterfactual Explanations via Anomaly control and Data Coherence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 794-804页

作者： Movin, Maria Siciliano, Federico Ferreira, Rui Silvestri, Fabrizio Tolomei, Gabriele Spotify StockholmSE-111 Sweden Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Spotify LondonWC2N 6AT United Kingdom Sapienza University of Rome Department of Computer Science Rome00161 Italy

Algorithmic recourses are popular methods to provide individuals impacted by machine learning models with recommendations on feasible actions for a more favorable prediction. Most of the previous algorithmic recourse methods work under the assumption that the predictive model does not change over time. However, in reality, models in deployment may both be periodically retrained and have their architecture changed. Therefore, it is desirable that the recourse should remain valid when such a model update occurs, unless new evidence arises. We call this feature consistency. This article presents anomaly control and data coherence (ACDC), a novel model-agnostic recourse method that generates counterfactual explanations, i.e., instance-level recourses. ACDC is inspired by anomaly detection methods and uses a one-class classifier to aid the search for valid, consistent, and feasible counterfactual explanations. The one-class classifier asserts that the generated counterfactual explanations lie on the data manifold and are not outliers of the target class. We compare ACDC against several state-of-the-art recourse methods across four datasets. Our experiments show that ACDC outperforms baselines both in generating consistent counterfactual explanations, and in generating feasible and plausible counterfactual explanations, while still having proximity measures similar to the baseline methods targeting the data manifold. © 2024 IEEE.

关键词： Machine learning

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Decomposition frequency optimization in wavelet-based template matching algorithms to manage P300 latency jitter 46

Decomposition frequency optimization in wavelet-based templa...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Quattrociocchi, Ilaria Caracci, Valentina Riccio, Angela Galiotta, Valentina D'Ippolito, Mariagrazia Cincotti, Febo Toppi, Jlenia Astolfi, Laura Sapienza University of Rome Department of Computer Control and Management Engineering Italy Fondazione Santa Lucia Irccs Neuroelectrical Imaging and Brain Computer Interface Laboratory Rome Italy

ISBN: (纸本)9798350371499

Event-Related Potentials (ERPs) are modifications of the brain activity in response to external sensory stimulation. P300 is a positive ERP component that occurs 300 ms after the presentation of a rare stimulus and indicates conscious perception of an unexpected change in sensory stimulation. Since the amplitude of ERPs is comparable to that of background electroencephalography, the most widespread approach to render ERPs correctly detectable is time-domain averaging. ERPs are physiologically affected by variations in latency, referred to as latency jitter. The most promising techniques to address latency jitter are cross-correlation template matching algorithms, which estimate single-trial latencies and resynchronize them according to a template that best describes the ERP component. Adaptive Wavelet Filtering (AWF) is an algorithm applied in the wavelet domain to provide the optimal compromise of time-frequency resolution in detecting ERPs. The choice of an appropriate decomposition frequency is crucial in the application of AWF, as it is essential for identifying the correct template and estimating single-trial latencies. In this study, we investigated the influence of frequency on the performance of AWF to optimize it. The algorithm was tested on simulated data with varying latency shifts and signal-to-noise ratios as well as for data recorded from 11 healthy subjects during an auditory oddball paradigm. Results on both simulated and real EEG data showed how frequency decomposition of 3 Hz is the most appropriate for P300 waveform detection. © 2024 IEEE.

关键词： Template matching

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Analysis of the External Characteristics and Eddy Current Loss of PMSG-Based WECS Relying on a Multi-Domain Coupling Model 19th

Analysis of the External Characteristics and Eddy Current Lo...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zuo, Peng Li, Weili Liu, Xiaoke Liu, Wenmao Li, Dan Qiao, Tianhuai School of Electrical Engineering Beijing Jiaotong University Beijing China Department of Electrical Engineering Tsinghua University Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9789819613861

With the trend towards larger-scale wind generators, the internal physical field and control strategy of high-capacity generator is becoming increasingly complex. The physical field change law inside the generator is often overlooked by the conventional wind energy conversion system (WECS) model. Therefore, developing a comprehensive and precise WECS model is essential for the safe operating of wind generators. Aiming at the above problems, this paper takes a 5 MW direct-drive permanent magnet synchronous generator (PMSG) as an example, and establishes a multi-domain coupling model. This approach can be used to investigate how the external characteristics of PMSG change under different operating conditions. Additionally, it has the ability to monitor how eddy currents are distributed in permanent magnets (PM), supporting generator design theoretically. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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