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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hammoud, Obadah Tarkhanov, Ivan A. National University of Science and Technology Department of Engineering Cybernetics Moscow Russia State Academic University for the Humanities Moscow Russia Academy of Sciences Federal Research Center 'Computer Science and Control' of Russian Russia

ISBN: (纸本)9798350384307

Digital transformation requires new innovative approaches in the design of data exchange and file storage systems. The analysis of the architecture of well-known IT projects and approaches showed that each of them cannot solve one of three problems at the same time: automatic scaling, the absence of a single point of failure, and minimizing the required storage space for files and their backups. The presented architecture of DecStore, a blockchain-based peer-to-peer file storage system, solves these problems simultaneously. It reduces the total amount of disk space required by 25% through the use of erasure coding. Automatic horizontal scaling and the absence of a single point of failure are provided by a load balancer based on algorithms implemented inside the Hyperledger chaincode. DecStore can become a subsystem for secure data exchange for government-to-business and business-to-business systems for state supervision in various areas, including financial and insurance audits. © 2023 IEEE.

关键词： Autoscaling distributed storage smart contract

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Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes

arXiv

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arXiv 2024年

作者： Huang, Yan Li, Xiang Shen, Yipeng He, Niao Xu, Jinming College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of O∼ (ϵ−(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Individually-guided Evolutionary Algorithm for Solving Multi-task Optimization Problems 19

Individually-guided Evolutionary Algorithm for Solving Multi...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Wang, XiaoLing Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States

ISBN: (纸本)9781665472432

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm that is used for solving multiple optimization tasks concurrently. Most MTO algorithms limit each individual to one task, and thus weaken the performance of information exchange. To address this issue and improve the efficiency of knowledge transfer, this work proposes an efficient MTO framework named individually-guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones. To further improve the efficiency of knowledge transfer, a partial individuals' learning scheme is used to choose suitable individuals to learn from other tasks. Experimental results show its superior advantages over the multifactorial evolutionary algorithm and its variants. © 2022 IEEE.

关键词： Evolutionary algorithms

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Development of an "Artificial Lung" System for Use in Indoor Air Quality Testing 21st

Development of an "Artificial Lung" System for Use in Indoor...

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21st Polish control Conference, PCC 2023

作者： Kozyra, Andrzej Lipczynska, Aleksandra Koper, Piotr Babisz, Radoslaw Madej, Damian Nowakowski, Konrad Karwatka, Jakub Tomczok, Dominik Department of Measurements and Control Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Heating Ventilation and Dust Removal Technology Faculty of Energy and Environmental Engineering Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031351693

"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the concept of the device, its construction and performed tests. The project was implemented by students of two faculties: Automatic control, Electronics and computer science, and Energy and Environmental engineering. Project supervisors from both faculties provided substantive supervision and support for students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Air quality

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ShadowNets: Efficient and Accurate Face Recognition for Resource-Constrained Devices 25

ShadowNets: Efficient and Accurate Face Recognition for Reso...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Mirza, Inzela Iqbal, Shahzaib Alhaqbani, Bandar Alshammari, Bandar Khan, Tariq Razzak, Imran Department of Computing Islamabad Pakistan Tcc Research and Development Labs Technology Control Company Riyadh Saudi Arabia Computer and Information Sciences College Jouf University Saudi Arabia School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Over recent years, deep convolutional neural networks have advanced the field of face recognition in both verification and identification applications. In this paper, we present ShadowNets, a highly efficient and compact face recognition network. We employed multiple shadow blocks in a cascading manner where the output of each shadow block is given as the input to the next shadow block. By integrating the advantages of Focal Modulation Blocks, Shadow Block and Ghost Modules, our proposed ShadowNets achieves superior face recognition performance, optimized specifically for edge devices. This network architecture not only minimizes computational costs and storage needs but also delivers high accuracy, making it suitable for deployment in edge environments. Our extensive experiments on challenging benchmark face datasets showcase the outstanding performance by achieving accuracy 98.85% on LFW, 84.37% on CFP-FP, and 91.23% on AgeDB-30 which validates the effectiveness of ShadowNets over state-of-the-art lightweight and deep face recognition models. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Effects of Combined Stress from Salt and Herbicide on Microbial Remediation of Soil Contaminated by n-Hexadecane and Phenanthrene

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China Petroleum Processing & Petrochemical Technology 2022年第2期24卷 126-137页

作者： Liu Bingkun Zuo Shuai Wang Ruirui Dong Kangning Zhang Xiuxia Li Ruyue Hao Boyu Li Jing Yin Linghao Bai Yuhao Song Jiayu Gu Yingying Sun Juan State Key Laboratory of Petroleum Pollution Control Beijing 102206 Department of Environment and Safety Engineering China University of PetroleumQingdao 266580 Department of Computer Science and Technology China University of PetroleumQingdao 266580

In this study,physicochemical,cluster,and high-throughput sequencing analyses were used to investigate the joint effects of salt and herbicide(glyphosate)stress factors on the microbial remediation of soil contaminated by n-hexadecane and *** on the soil’s physical and chemical properties,differences in microbial quantity and enzyme activities were analyzed among the samples,and how that influenced distribution of soil community structure was focused *** 120 days of indoor simulated remediation,the cumulative degradation rate of n-hexadecane decreased by 94.92%(blank control),96.96%(low concentration salt and glyphosate stress group),65.07%(high concentration salt and glyphosate stress group),while that of phenanthrene decreased by 87.33%(blank control),86.25%(low concentration salt and glyphosate stress group),58.45%(high concentration salt and glyphosate stress group).The combined stress of salt and herbicides was capable of lowering the reduction efficiency of organic matter,total nitrogen,and total phosphorus,and also restricting the growth of microorganisms and enzyme *** analysis results indicated that the non-stress group was similar to the low-concentration compound stress group during different remedial period,whereas both of those differed starkly from the high-concentration compound stress ***-throughput sequencing revealed that the dominant soil bacteria phyla shifted from Firmicutes to Actinobacteria within 120 days of remediation.

关键词： oil-contaminated soil cluster analysis common quantity sequencing microorganisms salt stress glyphosate stress

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GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

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Protection and control of Modern Power Systems 2024年第6期9卷 108-121页

作者： Shiwei Xia Chenhui Zhang Yahan Li Gengyin Li Linlin Ma Ning Zhou Ziqing Zhu Huan Ma the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China the State Grid Shandong Dispatch and Control Center Jinan 250003China the State Grid Shandong Electric Power Research Institute Jinan 250001China the Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China

Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series ***,a data-driven method is proposed in this study,combining graph convolution network and long short-term memory network(GCN-LSTM)to analyze the transient power angle sta-bility by exploring the spatiotemporal disturbance char-acteristics of future power systems with high penetration of renewable energy sources(wind and solar energy)and power *** key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score,whereas the network adjacency matrix is constructed according to the system network *** normalized feature quan-tities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features,reflecting changes in the network ***,the spa-tial features are inputted into the LSTM network to ob-tain the temporal features,reflecting dynamic changes in the transient power angle of the ***,the spatiotemporal features are fused through a fully con-nected network to analyze the transient power angle stability of future power systems,and the softmax activa-tion cross-entropy loss functions are used to predict the stability of the *** proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system,and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power sys-tems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algo-rithms.

关键词： Future power system spatiotemporal disturbance characteristics transient power angle stabil-ity graph convolutional network long short-term memory network.

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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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