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Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

computers, Materials & Continua 2022年第12期73卷 6019-6035页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Mechanical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication *** SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity *** the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s *** advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in *** this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)*** aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient *** attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform ***,WWO algorithm is applied to choose an optimal subset of features from the pre-processed ***,Deep Belief Network(DBN)model is followed to predict the stability level of ***,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN *** order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was *** simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches.

关键词： Smart grid stability prediction deep learning energy systems machine learning metaheursitics

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

arXiv

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arXiv 2024年

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC Copyright © 2024, The Authors. All rights reserved.

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

引用

5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

Why Does Little Robustness Help? A Further Step Towards Unde...

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IEEE Symposium on Security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness

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Electrical resistivity-temperature characteristics enhancement of insulating cross-linked polyethylene composites by incorporating positive temperature coefficient particles with different Curie temperatures

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High Voltage 2023年第1期8卷 1-11页

作者： Guimin Jiang Yuanxiang Zhou Chenyuan Teng Yunxiao Zhang Xin Huang Lei Xue Luming Zhou The Wind Solar Storage Division of State Key Laboratory of Control and Simulation of Power System and Generation Equipment Xinjiang UniversityUrumqiChina State Key Laboratory of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina College of Information Engineering Zhejiang University of TechnologyHangzhouChina School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Incorporating positive temperature coefficient(PTC)ceramic particles into polymers provides a prospective alternative solution for suppressing severe electric field distortions caused by negative temperature coefficient(NTC)of electrical resistivity within polymer insulation in high voltage direct current *** effect of the Curie temperature of PTC particles on the inhibition of the NTC effect of cross-linked polyethylene(XLPE)is investigated in this *** temperature coefficient particles with varied Curie temperatures are surface-modified and melt blending with *** modified particles are dispersed on averagely in the *** electrical resistivity,space charge behaviour,and direct current(DC)electrical breakdown strength of the samples are investigated at different temperatures,exploring the effect of the Curie temperature of PTC particles on suppressing the NTC effect of *** is demonstrated that the reduction in Curie temperature can further suppress the NTC effect of XLPE,enhance the DC strength at elevated temperatures,and inhibit the internal space charge *** reduction in the Curie temperatures means that the initiation of the PTC effect advances,exhibiting a higher potential barrier and inhibiting the NTC effect more *** work may give a reference for improving the temperature stability of DC properties for XLPE cable insulation.

关键词： coefficient resistivity insulation

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Knowledge Graph for Identifying Geological Disasters by Integrating computer Vision with Ontology

引用

Journal of Earth science 2023年第5期34卷 1418-1432页

作者： Qinjun Qiu Zhong Xie Die Zhang Kai Ma Liufeng Tao Yongjian Tan Zhipeng Zhang Baode Jiang School of Computer Science China University of GeosciencesWuhan 430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China School of Resource and Environmental Sciences Wuhan UniversityWuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China

The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.

关键词： geological hazard computer vision knowledge graph city safety ontology

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

引用

7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

arXiv

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arXiv 2025年

作者： Bi, Zhangqian Wan, Yao Chu, Zhaoyang Hu, Yufei Zhang, Junyi Zhang, Hongyu Xu, Guandong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science University of Technology Sydney Sydney Australia

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pre-training language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pre-training, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used open-source PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Design and Implementation of an EMG-based system for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

引用

2022 International Conference on Smart systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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