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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

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computer Modeling in engineering & sciences 2024年第5期139卷 2139-2154页

作者： Tianming Zhang Zebin Chen Haonan Guo Bojun Ren Quanmin Xie Mengke Tian Yong Wang Department of Computer and Science Shanghai Jiaotong UniversityShanghai200240China Aerospace System Engineering Shanghai Shanghai200240China State Key Laboratory of Precision Blasting Jianghan UniversityWuhan430056China Beijing Microelectronics Technology Institute Beijing100076China

The data analysis of blasting sites has always been the research goal of relevant *** rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of *** Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine *** this paper,we design a distributedmachine learning training application based on the AWS Lambda *** on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively *** also encrypts the data set,effectively reducing the risk of data *** rent a cloud server and a Lambda,and then we conduct experiments to evaluate our *** results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

关键词： Serverless computing object detection blasting

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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Performance Analysis of Disruptive Instances in Cloud Environment 16

Performance Analysis of Disruptive Instances in Cloud Enviro...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Nandy, Pranab Saha, Rounak Satpathy, Anurag Chakraborty, Sandip Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Missouri University of Science and Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering United States Indian Institute of Technology Kharagpur Cloud and Smart System Services Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further. © 2024 IEEE.

关键词： Cloud Computing Disruptive Instance On-demand Spot Instance

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Securing Autonomous Vehicles Visual Perception: Adversarial Patch Attack and Defense Schemes With Experimetal Validations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Liang, Jingguo Yi, Ru Chen, Jicheng Nie, Yiming Zhang, Hui School of Transportation Engineering and Science Beihang University Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China National Innovation Institute of Defense Technology Beijing P.R. China

Autonomous vehicles (AVs) heavily depend on machine learning-based algorithms for the purpose of environmental perception. However, extensively utilized deep learning-based visual perception methods are susceptible to adversarial visual sensor attacks, thereby introducing latent risks to vehicle driving safety. In this paper, we first investigate a novel adversarial patch attack method for the DNN-based visual object detection method widely used in AVs. The proposed adversarial attack strategy contains an evasion attack mode and a misclassified attack mode. For each mode, the corresponding adversarial patch generation method is designed by a dedicated optimization process. In addition, we propose an adversarial attack defense method based on texture feature and local denoising. According to the texture features, the method can detect the locations of the possible adversarial patches in the image, and then the image is denoised locally from the detection results. The attack effects of the generated adversarial patch are validated on the KITTI dataset and in real driving scenes, respectively. The results show that the average accuracy of car and pedestrian detection under evasion attacks decreases by 56% and 68%, respectively, while under misclassification attacks, the average accuracy decreases by 35% and 85% for car and pedestrian, respectively. The proposed method can effectively mislead the target detection model which seriously affects the perception module. This paper conduct a defense experiment using data generated by the adversarial patch, resulting in a 92% increase in mean average precision for detecting cars and pedestrians, with detection time reduced to 17% of its original value. The experimental results demonstrate the defensive effectiveness and efficiency of the proposed defense method. IEEE

关键词： Autonomous vehicles

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Propagation of Laguerre - Gaussian beam in turbulent plasma sheaths 4

Propagation of Laguerre - Gaussian beam in turbulent plasma ...

引用

4th International Computational Imaging Conference, CITA 2024

作者： Zhang, Lirong Dong, Jiduo Zhang, Hao Liu, Yang Shi, Hongyan Zang, Qing Department of Physics Harbin Institute of Technology Harbin China Institute of System Engineering Academy of Military Science Beijing China

ISBN: (纸本)9781510688834

Laser communication provides a promising solution for solving the communication blackout problem in reentry and hypersonic flight. Application of the orbital angular momentum (OAM) of laser has advantages in communication capacity and security. This paper has investigated the propagation of Laguerre - Gaussian beam in plasma sheaths for different turbulence intensity, in order to evaluate the feasibility of OAM communication. Through numerical simulation, basic characteristics of Laguerre - Gaussian beam are gotten, such as the distribution of laser intensity and phase, the scintillation coefficient, the spiral spectrum and detection probability. The effects of turbulence intensity and topological charge number on laser intensity scintillation coefficient and OAM detection probability are analyzed. The results show that when the variance of refractive index fluctuation is below 10-14, the above characteritics of laser propagation are less influenced by sheath turbulence, which shows the potential feasibility of OAM communication in alleviating the blackout problem. For more intense turbulence, intersymbol interference is predicted to be obvious, and compensation measures are necessary. When the refractive index variance is greater than 10-11, the weight of original OAM state is smaller than 0.01, and the OAM communication through plasma sheaths is difficult. © 2025 SPIE.

关键词： Gaussian beams

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Scattering of laser by ablation particles in hypersonic plasma flow

Scattering of laser by ablation particles in hypersonic plas...

引用

2024 Advanced Fiber Laser Conference, AFL 2024

作者： Dong, Jiduo Zhang, Lirong Wei, Binbin Zhang, Hao Shi, Hongyan Liu, Chunheng Liu, Yang Zang, Qing Department of Physics Harbin Institute of Technology Harbin China Institute of System Engineering Academy of Military Science Beijing China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

Laser communication has potential to solve the communication blackout problem during hypersonic flight of reentry capsule or hypersonic vehicles. The influence of ablation particles from thermal protection materials of the vehicles is an important factor for the laser communication design. This paper firstly estimates the basic parameters of ablation particles from previous experimental reports. On this basis, attenuation by Mie and Debye scattering is calculated. It is indicated that the attenuation caused by Debye scattering is far less than 1. And the attenuation by Mie scattering is obvious when the particle number density exceeds 104 /cm3. From the results in this paper, 5 dB redundancy corresponding to the influence of ablation particles is suggested for the laser communication system design. © 2025 SPIE.

关键词： Hypersonic vehicles

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Murshleen, Mohd. Prabhu, A. Jain, Garima Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Computer Science & Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo. © 2024 IEEE.

关键词： Search engines

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