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A Ray-based Asynchronous Distributed Parallel K-SVD Method 12

A Ray-based Asynchronous Distributed Parallel K-SVD Method

引用

12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Li, Yongkang Chen, Xiaodong Wan, Xiaohui Sun, Jin Zheng, Peng Wang, Yunchang Wu, Zebin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Quzhou Transportation Bureau Department of Industry Development Quzhou China Chang shan Inspection and Testing institute Department of Quality Control and Testing Quzhou China

ISBN: (纸本)9798331511074

K-SVD (K-Singular Value Decomposition)is a com-monly used dictionary learning method that progressively opti-mizes dictionaries to better represent data by iterating sparse coding and dictionary update steps. However, with the increasing data size, the traditional K -SVD method faces significant challenges in terms of computational efficiency. Therefore, this paper proposes an asynchronous distributed parallel K-SVD method based on Ray framework to cope with the computational efficiency problem in large-scale data processing. This method achieves efficient parallel computation by decomposing the sparse coding and dictionary updating steps of the K-SVD method into multiple independent tasks and utilizing the distributed scheduling capability of the Ray framework. Since the zero elements of the coefficient matrix are unevenly distributed, we introduce a stride partitioning approach when performing the decomposition. In addition, in order to avoid the method falling into local optima and failing to find global optima due to parallelism, this paper synchronizes with the global at the same time as the atomic update of the dictionary. Meanwhile, for the purpose of avoiding the overhead caused by data synchronization, a parameter server is introduced to realize asynchronous updating of dictionary and coefficient matrix to reduce the overhead caused by communication. The experimental results show that, compared with the traditional K-SVD method, the method proposed in this paper has a significant advantage in computational efficiency and is able to efficiently process hyperspectral image datasets under the premise of guaranteeing the sparse representation accuracy. Compared with the synchronous parallel K-SVD method, the proposed method has faster error convergence and does not fall into local optima. © 2024 IEEE.

关键词： Network security

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An Efficient Hybrid Red Panda Optimization-Similarity-Navigated Graph Neural Network with Blockchain Technology for Shared Electric Vehicle Charging 4

An Efficient Hybrid Red Panda Optimization-Similarity-Naviga...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sammy, F. Rajeshwari, S. Prashanth, G.K. Thirumalaimuthu, Babu Pokhariya, Hemant Singh Natrayan, L. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522 302 India Bangalore Institute of Technology Department of Mathematics Karnataka Bengaluru560004 India Siddaganga Institute Technology Department of Mca Karnataka Tumkur-572103 India St. Joseph's College of Engineering Department of Instrumentation and Control Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Blockchain technology for shared electric vehicle (EV) charging makes use of a distributed ledger in handling different transactions which promotes authenticity in charging. However, this technology can be non-scalable at times which slows down the transaction rates as the scale increases in an organization and may also demand high computational as well as energy requirements which increases the inefficiency of the total system, the operating expenses of EV charging station, costs of energy utilized for charging, cost of maintenance and cost of station infrastructure may prove to be a weakness, impacting the feasibility of infrastructure deployment and upkeep expenses of the charging stations. To overcome these drawbacks, this paper proposes a hybrid method for blockchain technology with shared EV charging. The proposed approach combines the use of the Red Panda Optimization (RPO) with Similarity-Navigated Graph Neural Network (SNGNN). Hence, it is named as hybrid RPO-SNGNN approach. Reducing the operating cost of EV charging position and maximizes the security efficiency is the main aim of the proposed approach. To optimize scheduling and resource allocation by finding efficient paths by RPO and predict future demand and user behaviour patterns by SNGNN. By then, the proposed model has been put into practice on the MATLAB work platform and contrasted with other existing methods. The proposed technique outperforms all existing techniques, including Fuzzy-based Cross Hierarchical Reinforcement Q Learning (FCHRQL), Non-Dominated Sorting Genetic Algorithm-Long Short-Term Memory Network (NSGA-II-LSTMN), and Ant Colony Optimization-Proximal Policy Optimization (ACO-PPO). The proposed method shows the operating cost of $550 and the security rate is 97%. From the result, the proposed approach operating cost is low and the security rate is high when compared to the existing approaches. © 2024 IEEE.

关键词： Charging stations

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An Abstract Interpretation-Based Framework for WCET Analysis of Parallel Programs

An Abstract Interpretation-Based Framework for WCET Analysis...

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IEEE International Conference on Networking, Sensing and control

作者： Xinbang Hu Guanjun Liu Xiaofeng Li Kaiwen Zhang Jian Guan Bin Gu Department of Computer Science Tongji University Shanghai China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Estimating the Worst-Case Execution Time (WCET) of programs in an embedded multi-core environment is fundamental for schedulability analysis. In this paper, we propose a framework for calculating the WCET of programs originally designed for single-core systems when migrated to and executed in parallel on multi-core environments. First, we decompose single-core tasks and convert them into parallel programs using OpenMP. Our approach extends the task dependency graph to describe relevant information for parallel tasks (such as execution time estimation, core allocation, and inter-task dependencies). Finally, we present an iterative algorithm based on abstract interpretation. This algorithm takes the parallel task dependency graph as input and uses fixed-point analysis to determine the WCET of parallel programs. We conducted experiments on a physical machine to verify the feasibility of our solution.

关键词： Runtime Program processors Multicore processing Estimation Parallel processing Benchmark testing Real-time systems Sensors Iterative methods Resource management

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Switching control for Identification Deception in Cyber-Physical Systems

Switching Control for Identification Deception in Cyber-Phys...

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IEEE Conference on Decision and control

作者： Christos Mavridis Aris Kanellopoulos Henrik Sandberg Karl Henrik Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching.

关键词： Linear systems Asymptotic stability Switching systems Statistical analysis Simulation Switches Cyber-physical systems control systems Stability analysis Security

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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Research on a Transmission Line Insulator Defect Detection Algorithm Based on Small Sample Learning 7

Research on a Transmission Line Insulator Defect Detection A...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Guo, Chunhai Lu, Zhenghua Zhang, Meiling Li, Qiang Shen, Yingkao Zhang, Kong Guo, Xinming Dali Bureau of Csg Ehv Power Transmission Company Dali China School of Control and Computer Engineering North China Electric Power University Beijing China Research Institute Dali China

ISBN: (纸本)9798350345094

The stable operation of power equipment is an important prerequisite for ensuring production and life. As an important power insulation equipment, the traditional insulator defect detection algorithm often requires a mount number of defect samples for model training. However, in reality, due to the low occurrence rate of insulator defects, there is a serious shortage of defect image datasets available for model training, which often leads to problems such as inability to train, overfitting, and low accuracy of the model. In order to address this issue, this paper proposes an improved YOLOv7 insulator defect detection algorithm under small sample conditions. Firstly, the CBAM attention mechanism is added to the YOLOv7 backbone network to improve the feature extraction ability of the model under complex backgrounds and small insulator targets;Secondly, introduce meta learning technology into the network model to enhance the algorithm's accurate detection ability under small sample conditions. The experimental results show that the improved YOLOv7 algorithm can accurately identify insulator defects in transmission lines on a small-scale raw dataset, with mAP reaching 96.8%, Precision reaching 95.3%, and Recall value reaching 93.6%. The improved method has broad application prospects in the detection of insulators in power systems. © 2023 IEEE.

关键词： Defects

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Improvement Algorithm Analysis and Implementation of Plant Disease and Pest Recognition Based on YOLO

Improvement Algorithm Analysis and Implementation of Plant D...

引用

computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Yongxiang Wei Xin Pan School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Technology and Engineering Changchun Institute of Technology Changchun Jilin China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness.

关键词： Deep learning YOLO Plant diseases Accuracy Target recognition Heuristic algorithms Crops

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural Networks for Medical Image Classification 5th

BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural ...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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An AI-Driven Framework for Personalized Diet Generation and Nutrition Suggestions Using Machine Learning, computer Vision and NLP

An AI-Driven Framework for Personalized Diet Generation and ...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Jyoti Kanjalkar Pramod Kanjalkar Raj Khanke Rushikesh Mane Kunal Kharat Kanad Kolhe Department of Computer Science & Engineering (AIML) Vishwakarma Institute of Technology Pune India Department of Instrumentation & Control Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

To counteract unhealthy eating habits among the younger generation, a novel and economical AI-powered framework has been developed to serve as a virtual nutritionist and nutrition counsellor. The system uses machine learning, computer vision, and natural language processing to provide personalized nutrition plans via an easy-to-use online application. It generates tailored meal plans that consider user preferences, allergies, and meal times by calculating Body Mass Index (BMI) based on user input. In addition to blog links and tailored training regimens (enhanced by the LLM3 model trained on a Kaggle gym dataset), a feedback mechanism allows for continuous optimization. Real-time food classification uses VGG16 and ResNet deep learning models to detect damaged or harmful foods and send out alerts for unsuitable foods. Additionally, integration with Google Maps and KNN helps users locate nearby dietitians, fostering better access to professional guidance. These comprehensive improvements significantly contribute to maintaining a good diet.

关键词： Photography computer vision Wearable Health Monitoring Systems Visualization Web services Real-time systems Natural language processing User experience Satellite images Allergies

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