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3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Naderi, Ehsan Asrari, Arash College of Engineering and Computer Science Arkansas State University Department of Electrical Engineering JonesboroAR United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States

ISBN: (纸本)9798350349207

In the process of protecting power systems against different types of cyberattacks, the primary step is to precisely model such frameworks from attacker's perspective. This paper investigates a false data injection (FDI) attack framework, which can target under-load tap changing (ULTC) transformers, resulting in manipulated voltage profile in radial smart distribution networks. The developed FDI model compromises the voltage profile of a distribution feeder through misleading the volt/var optimization, that optimally manages system-wide voltage profile and flow of reactive power. The presented attack model is formulated as a bi-objective optimization problem. The objective functions from the attacker's point of view are 1) minimizing the level of false data to be injected into the smart meters associated with load data and 2) maximizing the voltage deviation of the distribution grid. Negative impacts of such a cyberattack model have been validated and discussed in this work on an IEEE distribution test system, necessitating proper remedial actions, which will be elaborated in the next step of this research. © 2024 IEEE.

关键词： Reactive power

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Unsupervised Action Recognition Using Spatiotemporal, Adaptive, and Attention-Guided Refining-Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1460-1471页

作者： Yin, Xinpeng Zhang, Cheng Huang, ZiXu He, Zhihai Cao, Wenming Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Department of Electronics and Information Engineering Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China

Previous articles on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. However, designing application-level strategies poses challenges. This article shifts the focus to the generation-level modelings and introduces the spatiotemporal adaptively attentions-guided refining network (AgRNet). AgRNet approaches the reduction of costs and enhancement of efficiency by constructing the adaptive activity-guided attention (AAGA) and adaptive dominant-guided attenuation (ADGA) modules. The AAGA leverages the sparsity of the correlation matrix in the attention mechanism to adaptively filter and retain the active components of the sequence during the modeling process. The ADGA embeds the local dominant features of the sequence, obtained through convolutional distillation, into the globally dominant features under the attention mechanism, guided by the defined attenuation factor. Additionally, the progressive feature modeling (PFM) module is introduced to complement the progressive features in motion sequences that were overlooked by AAGA and ADGA. AgRNet shows efficiency on three public datasets, NTU-RGBD 60, NTU-RGBD 120, and UWA3D. © 2020 IEEE.

关键词： Time series analysis

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Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique

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Health and Technology 2024年第1期14卷 109-118页

作者： Alizargar, Azadeh Chang, Yang-Lang Tan, Tan-Hsu Liu, Tsung-Yu Department of Electrical Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei 10608 Taiwan Department of Multimedia and Game Science Lunghwa University of Science and Technology Taoyuan 333326 Taiwan

Purpose: Hepatitis B, caused by the Hepatitis B virus (HBV), can harm the liver without noticeable symptoms. Early detection is crucial to prevent transmission and enhance recovery. The main goal is to predict Hepatitis B through cost-effective lab test data, by utilizing machine learning. The primary focus is on evaluating the effectiveness of various algorithms in predicting the disease and their potential to enhance early diagnosis capabilities. Methods: Six distinct algorithms (Support Vector Machine, K-nearest Neighbors, Logistic Regression, decision tree, extreme gradient boosting, random forest) were employed alongside an ensemble model. Analysis involved two rounds: considering all features and key attributes. The Synthetic Minority Oversampling Technique (SMOTE) was employed for data imbalance. Various metrics, including the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC) curve, area under the curve (AUC), and mean absolute error (MAE), were utilized to assess the efficacy of each predictive technique. The National Health and Nutrition Examination Survey (NHANES) dataset was employed. Results: The experimental results demonstrate that the ensemble model attained the highest accuracy (97%) and AUC (0.997) in comparison to existing models. The analysis revealed that specific crucial features possess substantial predictive significance within this model. Conclusion: The study underscores the potential of the ensemble model as a valuable tool for medical practitioners, leveraging cost-effective and readily obtainable laboratory test data to predict Hepatitis B with remarkable accuracy. By facilitating early diagnosis and intervention, this research presents a promising avenue to enhance patient outcomes in the context of Hepatitis B. © 2023, The Author(s) under exclusive licence to International Union for Physical and engineering sciences in Medicine (IUPESM).

关键词： Early detection Ensemble model Index terms— Hepatitis B Liver damage Machine learning SMOTE

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Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithm

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Neural Computing and Applications 2024年第31期36卷 19549-19583页

作者： Mohammadpour, Majid Mostafavi, Seyedakbar Mirjalili, Seyedali Department of Computer Engineering Yazd University Yazd Iran Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm’s performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity;while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This n

关键词： Chaos theory

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Unknown-input-observer-based approach to dynamic event-triggered fault estimation for Markovian jump systems with time-varying delays

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science China(Information sciences) 2022年第3期65卷 138-151页

作者： Xiaoting DU Lei ZOU Zhongyi ZHAO Yezheng WANG Maiying ZHONG College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London

This paper investigates the unknown-input-observer-based fault estimation problem for a class of discrete-time-delay Markovian jump systems under the dynamic event-triggered transmission scheme. The dynamic event-triggered mechanism is used to decide whether the current information should be transmitted to the estimator or not to save the limited communication resources. This study aims to design an event-based fault estimator such that the estimation error is exponentially ultimately bounded in the mean square *** adopting the Lyapunov-Krasovskii functional approach, sufficient conditions are obtained to guarantee the existence of the desired estimator to achieve the prescribed performance requirement. The estimator gains are derived based on the convex optimization technique. A numerical example is provided to illustrate the effectiveness of the developed estimator design scheme.

关键词： fault estimation unknown input observer Markovian jump systems dynamic event-triggered mechanism time-varying delays

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Privacy Comparison for Bitcoin Light Client Implementations 6

Privacy Comparison for Bitcoin Light Client Implementations

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6th Conference on Advances in Financial Technologies, AFT 2024

作者： Kotzer, Arad Rottenstreich, Ori The Department of Computer Science Technion Haifa Israel The Department of Computer Science The Department of Electrical and Computer Engineering Technion Haifa Israel

ISBN: (纸本)9783959773454

Light clients implement a simple solution for Bitcoin’s scalability problem, as they do not store the entire blockchain but only the state of particular addresses of interest. To be able to keep track of the updated state of their addresses, light clients rely on full nodes to provide them with the required information. To do so, they must reveal information about the addresses they are interested in. This paper studies the two most common light client implementations, SPV and Neutrino with regards to their privacy. We define privacy metrics for comparing the privacy of the different implementations. We evaluate and compare the privacy of the implementations over time on real Bitcoin data and discuss the inherent privacy-communication tradeoff. In addition, we propose general techniques to enhance light client privacy in the existing implementations. Finally, we propose a new SPV-based light client model, the aggregation model, evaluate it, and show it can achieve enhanced privacy than in the existing light client implementations. © Arad Kotzer and Ori Rottenstreich.

关键词： Differential privacy

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Efficient Classification of Rice Varieties Using Optimized Convolutional Neural Networks 8

Efficient Classification of Rice Varieties Using Optimized C...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Pathak, Abhishek Kumar Singh, Ankit Kumar Bhatia, Vimal Singh, Puneet Indian Institute of Technology Department of Electrical Engineering Indore India Motihari College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Rice type classification is a crucial task in agri-cultural automation, aimed at improving quality control and ensuring market standards. This study presents a deep learning-based approach using a optimized Convolutional Neural Network (CNN) model implemented with PyTorch to classify five rice varieties: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. Our proposed model diverges from conventional methods that rely heavily on complex machine learning algorithms or pretrained CNN architectures like MobileNetV2, VGG16, EfficientNetB0 etc., which require a large number of parameters and layers, re-sulting in increased computational complexity. Through proposed approach, we achieved a remarkable 99.81% validation accuracy and a minimal validation loss of 0.01 % with a significantly reduced model size 30.76 MB. The simplicity of the proposed CNN architecture not only enhances computational efficiency but also maintains high accuracy, making it a viable solution for rice type classification in practical applications. This study demonstrates that a fine-tuned CNN model can outperform more complex models, providing an efficient and effective alternative for agricultural classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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computer-aided visual communication design based on image detail enhancement algorithm

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Optical and Quantum Electronics 2024年第4期56卷 1-17页

作者： Jiang, Jian School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan ChangDe 415000 China

There is a problem of unclear image details in traditional visual communication design, which can affect the effectiveness of the design work. This article proposes a computer-aided design method based on image detail enhancement algorithms. The core idea of this method is to improve the detail representation of images by using image detail enhancement algorithms, thereby making the design work clearer and more vivid. Firstly, preprocess the image to improve its quality and clarity. Then, an enhancement algorithm is used to enhance the details of the image, highlighting the details in the image to make it more distinct. Finally, post processing is performed on the enhanced image to make the image details more prominent and meet the design requirements. By analyzing the experimental results of computer-aided visual communication design using image detail enhancement algorithms, this article found that this algorithm can significantly improve the detail representation of design works and enhance the visual communication effect. Compared with traditional design methods, design works using image detail enhancement algorithms are clearer and more vivid, which can better attract the attention of the audience and convey design intentions. This verifies the effectiveness and practicality of the proposed algorithm. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computers Enhancement algorithm Image details Visual communication

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Optimizing Crisis Management with Advanced UAV Imaging Technology 4

Optimizing Crisis Management with Advanced UAV Imaging Techn...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, Jambula Snehith Mathi, Sri Chaithanya Reddy, Polu Sai Nadh Mateti, Vedha Pranava Yadav, Sudha Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

The UAV systems that are fitted with current technology have helped in boosting the crisis management activities such as disaster relief operations, search and rescues, and policing. This paper integrates the developmental function of advanced detective UAVs into episodes of crises. These UAVs are capable of quickly and effectively assessing the damage, find people and observe post-disaster occurrences through with the help of complicated image analysis. It incorporates cameras with HD resolutions, thermal imagery systems, and numerous ML algorithms that allow entities and people to be identified besides detecting heat through barriers and thus increase the operational effectiveness during response operations. Indeed, the social implication as noticed with detective UAVs is highly useful in that it provides support services in disaster, bringing relief directly to the affected groups and guaranteeing the security of the rescue squads. Therefore, these UAVs enhance crisis consequence management, reducing death rates, the number of casualties and the extent of property damage. They do also help in safety and security to the public through assisting the police in surveillance and the fight against crime. Analyzing UAV's physical and virtual units, the challenges describe the UAV's use cases in different kinds of crises, such as natural ones, industrial failures, and criminal events. It also deals with issues such as restrictive policies, data security, and the requirement of professional personnel. From case studies and more personal accounts, detective UAVs' potential of crisis management is revolutionary: loss of life is avoided, damage to property is limited, and order is maintained. If further developed and properly integrated into the emergency management systems, UAVs will be the part of the equipment of the future for enhancing the efficiency of response to critical situations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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