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Blocks of Cell-Hierarchical Identification and Optimization of Complex Spatially distributed Production Systems 5

Blocks of Cell-Hierarchical Identification and Optimization ...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Korneev, Andrey Lavrukhina, Tamara Smetannikova, Tatiana Pantyushin, Mikhail Lipetsk State Technical University Institute of Mechanical Engineering and Transport Lipetsk Russia Lipetsk State Technical University Faculty of Automation and Informatics Lipetsk Russia

ISBN: (纸本)9798350309126

Complexly structured production systems can be represented in the form of a cellular hierarchical structure. Individual stages of processing create certain impacts on the quality indicators of the finished product. the task of creating new methods for search optimization of complex processes and developing control algorithms is due to the complexity of finding exact dependencies between technological quantities and properties regulated by standards. Identification of multi-stage production processes can be carried out in the form of parallel and sequential flows. Since fulfilling the requirements of a certain trajectory does not allow obtain a single combination of output alphabets, it is necessary for each technological chain to describe its own probabilistic automaton. A separate processing stage can be described as a cell without memory and is a lower-level automaton. Each of the aggregates, in turn, is described in the form of a higher-level automaton (aggregate automaton), which includes a chain of cells formed by operation automata. this chain forms a linear iterative chain. the problem of discrete optimization comes down to determining such a technological subset for which the objective function (a criterion for assessing the effectiveness of the operating modes of complex systems) takes on the maximum value. the design of processing modes for industrial systems, taking into account many quality criteria, has the nature of a heuristic iterative process. © 2023 ieee.

关键词： Optimization

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A Moving Target Defense Approach for the distributed Dynamic Network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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21st ieee International symposium on parallel and distributed processing with Applications, 13th ieee International Conference on Big Data and Cloud Computing, 16th ieee International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

the distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 ieee.

关键词： Network security

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Research on Rolling Bearing Fault Diagnosis Based on distributed Fuzzy Broad Learning System Under Imbalanced Data 5

Research on Rolling Bearing Fault Diagnosis Based on Distrib...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Pan, Haiyang Liu, Haoran Zheng, Jinde Tong, Jinyu Cheng, Jian School of Mechanical Engineering Anhui University of Technology Ma'anshan243032 China

ISBN: (纸本)9798331529192

As a novel fast and effective intelligent algorithm, Broad Learning System (BLS) is popularly used in data classification task. However, the real data are often imbalanced, which makes BLS have limited performance in dealing with imbalanced data and cannot achieve good classification results. In view of this, a method based on distributed Fuzzy Broad Learning System (DFBLS) under imbalanced data is proposed in this paper. the model assigns a fuzzy affiliation value to each training point, reducing the effect of imbalanced samples on the model, and DFBLS executes fuzzy function in parallel, thus improving computational efficiency and processing speed. Moreover, DFBLS learns the features in the input data by mapping features and enhancement features, which solves the deficiency of fuzzification that leads to the loss or confusion of data samples. Finally, it is validated by two rolling bearing experimental datasets, and the research results show that this technology has demonstrated excellent performance in various evaluation indexes and provides an efficient solution to the fault diagnosis problem in imbalanced data sets. © 2024 ieee.

关键词： Adversarial machine learning

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

the reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 ieee.

关键词： Graphics processing unit

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Cost-Effective Dynamic Alliance Pricing Mechanism Based on distributed Edge-Intelligence 21

Cost-Effective Dynamic Alliance Pricing Mechanism Based on D...

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作者： Cao, Zhihan Zheng, Xi Guo, Jianxiong Jia, Weijia Xu, Yang Wang, Tian Beijing Normal University Zhuhai China Fudan University Shanghai China Macquarie University Australia Hunan University Changsha China

ISBN: (纸本)9798350329223

In Internet of thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. the pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes, but also makes full use of the advantages of mutual cooperation between nodes. Tn order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance's candidate solutions to improve pricing efficiency and edge benefit. the experimental results show that, compared with the traditional game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%-61.37%. Meanwhile, the prediction accuracy was improved by 16.47%38.86%, and the average prediction time was reduced by 42.81 %-65.57%. © 2023 ieee.

关键词： Profitability

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels 21

Cross-subject Drowsiness Recognition Model Based on the Mult...

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作者： Li, Songquan Zhu, Rongbo Yuan, Jun Wan, Shaohua Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Shenzhen Institute of Nutrition and Health Wuhan430070 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory Ministry of Agriculture Shenzhen518000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China

ISBN: (纸本)9798350329223

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. this paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. the experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%. © 2023 ieee.

关键词： Electroencephalography

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Novel Current Sharing Control Method To Reduce Circulating Current in Multi-Bus Microgrids 5

Novel Current Sharing Control Method To Reduce Circulating C...

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5th ieee International Conference on Modern Electrical and Energy System, MEES 2023

作者： Hassi, Youssef Amine Ait Ben Hennane, Youssef Berdai, Abdelmajid Tytiuk, Valerii Electrical Engineering Casablanca Morocco Université de Lorraine Electrical Engineering Nancy France Kryvyi Rih National University Department of Electromechanics Kryvyi Rih Ukraine

ISBN: (纸本)9798350359787

In recent times, there has been a growing interest in AC microgrids due to their versatility in operating as both islanded and grid-connected systems. When operating in islanded mode, the widely favored primary control mechanism for managing load distribution among distributed generators is droop control. However, in the context of meshed microgrids, certain challenges arise due to load fluctuations and varying impedances in transmission lines. these challenges manifest as significant drawbacks, such as inaccurate current sharing and rise in circulating current values among parallel distributed generators. To address these concerns, this research paper introduces a novel current sharing approach that relies on real voltage and current feedback. this method is applicable to both single-bus and multi-bus microgrids. Notably, the proposed technique shows promise in effectively mitigating circulating currents to very low values, outperforming both droop control and existing approaches found in the literature. To substantiate the efficacy and resilience of this method, the researchers conduct comprehensive simulations using MATLAB/Simulink. the simulations serve to demonstrate the superior performance of the new current sharing technique in managing microgrid operations. © 2023 ieee.

关键词： distributed power generation

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As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensus is easy! 36

As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensu...

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36th ieee International parallel and distributed processing symposium (ieee IPDPS)

作者： Civit, Pierre Gilbert, Seth Gramoli, Vincent Guerraoui, Rachid Komatovic, Jovan Sorbonne Univ LIP6 CNRS Paris France NUS Singapore Singapore Singapore Univ Sydney EPFL Sydney NSW Australia Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781665481069

It is known that the agreement property of the Byzantine consensus problem among n processes can be violated in a non-synchronous system if the number of faulty processes exceeds t(0) = left perpendicularn/3right perpendicular - 1 [10], [19]. In this paper, we investigate the accountable Byzantine consensus problem in non-synchronous systems: the problem of solving Byzantine consensus whenever possible (e.g., when the number of faulty processes does not exceed t(0)) and allowing correct processes to obtain proof of culpability of (at least) t(0) + 1 faulty processes whenever correct processes disagree. We present four complementary contributions: 1) We introduce ABC: a simple yet efficient transformation of any Byzantine consensus protocol to an accountable one. ABC introduces an overhead of only two all-toall communication rounds and O(n(2)) additional bits in executions with up to t(0) faults (i.e., in the common case). 2) We define the accountability complexity, a complexity metric representing the number of accountabilityspecific messages that correct processes must send. Furthermore, we prove a tight lower bound. In particular, we show that any accountable Byzantine consensus protocol incurs cubic accountability complexity. Moreover, we illustrate that the bound is tight by applying the ABC transformation to any Byzantine consensus protocol. 3) We demonstrate that, when applied to an optimal Byzantine consensus protocol, ABC constructs an accountable Byzantine consensus protocol that is (1) optimal with respect to the communication complexity in solving consensus whenever consensus is solvable, and (2) optimal with respect to the accountability complexity in obtaining accountability whenever disagreement occurs. 4) We generalize ABC to other distributed computing problems besides the classic consensus problem. We characterize a class of agreement tasks, including reliable and consistent broadcast [5], that ABC renders accountable.

关键词： Measurement distributed processing Lattices Complexity theory Consensus protocol Reliability Task analysis

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distributed Cloud Computing Architecture in Hyperspectral Remote Sensing Image Classification under Big Data 5

Distributed Cloud Computing Architecture in Hyperspectral Re...

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5th ieee International Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Feng, Kai Wu, Yi Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel processing efficiency of massive remote sensing data. the purpose of this paper is to study the application of distributed cloud computing architecture in hyperspectral remote sensing image classification based on big data. the related technologies of cloud computing are expounded, and the model of SCSRC, a sparse representation classification method based on spatial correlation constraints, is proposed. By analyzing the principle and solution method of SCSRC model, a distributed classification algorithm DP-SCSRC based on spatial correlation constraint sparse representation is proposed on the Spark platform, and the following optimization methods are designed for the core steps. Finally, through experiments with different computing nodes and different amounts of computing data, it is proved that the DP-SCSRC algorithm has a good distributed speedup ratio and data scalability under the premise of ensuring the classification accuracy. © 2022 ieee.

关键词： distributed cloud

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PE-FedAvg: A Privacy-Enhanced Federated Learning for distributed Android Malware Detection 21

PE-FedAvg: A Privacy-Enhanced Federated Learning for Distrib...

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作者： Tang, Junwei Xu, Zilong Ye, Luyao Peng, Tao He, Ruhan Hu, Xinrong Tian, Wenlong Wuhan Textile University Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion China Wuhan Textile University School of Computer Science and Artificial Intelligence China University of South China School of Computer Science China Nanyang Technological University School of Physical and Mathematical Sciences Singapore

ISBN: (纸本)9798350329223

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. therefore, this paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. this achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware. © 2023 ieee.

关键词： Android malware

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