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作者： Zhibin Liu Bo Peng Mingming Cai School of Computer and Control Engineering Qiqihar University Department of Information Engineering Qiqihar Institute of Engineering School of Computer and Information Engineering Henan University of Economics and Law

With the continuous development of the Internet and the promotion of the epidemic, the construction of enterprise networking is becoming more and more important. Website construction has become an essential part of enterprise ***, small and medium-sized enterprises are facing economic and human pressures. Enterprise website, online office, online mall VPN, forums and other services for the development of the company is particularly important. Through the investigation and analysis of 20 small and medium-sized enterprises, domestic and foreign academic papers and online data, this paper introduces the importance of cloud on enterprise network, constructs the virtual cloud construction system based on CMS, and provides solutions from the aspects of capital cost, hardware procurement, software environment, system development, later operation and maintenance and labor cost, so as to realize simple, rapid, efficient, safe and lowcost deployment. At the same time, it meets the development of enterprise science and technology, perfects and summarizes the architecture model, realizes the demand of network construction of small and medium-sized enterprises efficiently and quickly, and encourages the development of entrepreneurs and small and medium-sized enterprises.

关键词： small and medium-sized enterprises cloud services CMS WEB network site

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State-Space Piece-Wise Affine System Identification with Online Deterministic Annealing

State-Space Piece-Wise Affine System Identification with Onl...

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European control Conference (ECC)

作者： Christos N. Mavridis Aris Kanellopoulos John S. Baras Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Department of Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose an online identification scheme for discrete-time piece-wise affine state-space models based on a system of adaptive algorithms running in two timescales. A stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale, estimating the partition of the augmented state-input space that defines the switching signal. At the same time, an adaptive identification algorithm, running at a higher timescale, updates the parameters of the local models based on the estimate of the switching signal. Identifiability conditions for the switched system are discussed and convergence results are given based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for piece-wise affine systems, the proposed approach progressively estimates the number of modes needed and is appropriate for online system identification using sequential data acquisition. This progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off, desired in practical applications. Experimental results validate the efficacy of the proposed methodology.

关键词： Switched systems Annealing Stochastic processes Switches Approximation algorithms Real-time systems System identification

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems Science & engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Range-Limited Pursuit-Evasion

Range-Limited Pursuit-Evasion

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Weintraub, Isaac E. Von Moll, Alexander Pachter, Meir Control Science Center Air Force Research Laboratory Wright-Patterson AFBOH United States Air Force Institute of Technology Electrical and Computer Engineering Wright-Patterson AFBOH United States

ISBN: (纸本)9798350338782

In pursuit-evasion the objective of the pursuer is to capture the evader. In this work, the faster pursuer is modeled to have limited range and therefore optimal strategies for the pursuer and evader change. Depending upon the range limits of the pursuer the evader may evade capture by the pursuer. This paper describes the optimal strategies and nuances that appear for point-capture or when the pursuer is endowed with a non-zero capture radius. © 2023 IEEE.

关键词： Multi agent systems

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Long Term Load Forecasting Using Adaptive Neural Fuzzy Inference System: Case Study on Woldia Power Distribution Systems

SSRN

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SSRN 2024年

作者： Sinishaw, Getaye Yeshaneh Mitiku, Hunachew Moges Molla, Tesfahun Desale School of Electrical and Computer Engineering Electrical Power Woldia Institute of Technology Woldia University Woldia Ethiopia School of Electrical and Computer Engineering Industrial Control Woldia Institute of Technology Woldia University Woldia Ethiopia

Abstract: - Now a day’s power consumption in Woldia city is becoming increased. As a result, there is a need of load forecasting to know the electrical load consumption and be ready to supply the customers of woldia city. Therefore, load forecasting strengthens the power systems' dependability and energy efficiency. Power systems are highly distributed, highly complex graphically, and subject to numerous unforeseen events, making power system problems difficult to solve. It has considered the different demographic aspects, such as population growth, time, historical load, economic growth of the city and current load of the distribution system. In this thesis, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models have been used to determine the forecasted load using data collection obtained from woldia city and the results of the two models have been compared. The data sets cover a seven-year period (2016/17- 2022/23) on monthly basis and forecast for ten years (2023/24-2032/33). The performance evaluations of ANFIS model have been executed better when compared with ANN. The performance specification of load forecasting MAPE and RMSE in ANFIS were improve by 2.0196% and 2.859984 respectively as compared with ANN. The prediction performance of the ANFIS model for load forecasting based on the previous data trained was investigated and a good performance of future load forecasting has been recoded. It can be decided that the future load consumption of woldia city is directly related with the population growth and the GDP of the city. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Prioritization Method of Test Data for Intelligent Software based on Multi-objective Optimization 9

Prioritization Method of Test Data for Intelligent Software ...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Xia, Chunyan Huang, Yifan Huang, Song Zheng, Changyou Wang, Yutong Wang, Yu College of Command and Control Engineering Army Engineering University of Pla China College of Computer and Information Technology Mudanjiang Normal University China Illinois Institute of Technology United States

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of the information industry, intelligent software testing has become one of the hot research. This paper studies how to extract useful data from the original test set to test the modified model for the intelligent software. First, the initial test data are obtained according to the label classification of the original structured documents. Second, the non-dominated sorting genetic algorithm is used to prioritize the test data. Finally, the experimental results of intelligent software show that compared with the random method, the accuracy of the penalty_money prediction model is reduced by 0.04% 34.91 %, and the accuracy of the penalty_imprisonment prediction model is reduced by 0.08% 32.63 %, indicating that the proposed method can find more defects of the penalty prediction model earlier, to reduce the cost of intelligent software testing. © 2022 IEEE.

关键词： Multiobjective optimization

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)

arXiv

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arXiv 2025年

作者： Mehdifar, Farhad Dhullipalla, Mani H. Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents’ objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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Preprocessing Techniques for Rectal Cancer Diagnosis using MR Images 24

Preprocessing Techniques for Rectal Cancer Diagnosis using M...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： D'Almeida, Sonal Patreena Kamath, Surekha Rajagopal, K.V. Manjunath, K.N. Department Of Instrumentation And Control Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Radiodiagnosis And Imaging Kasturba Medical College Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Computer Science And Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9798400708329

The use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable course of action. Image pre-processing is used to improve the quality of image and making it better for analysis and further processing. The study carried out in this paper is concentrated on pre-processing methods like Gaussian and Sobel filtering. The filtering techniques were implemented on Pelvic MR images obtained from Kasturba Medical College (KMC), Manipal. The findings suggest that Sobel filtering had a superior output image quality, indicating its potential as a preferred preprocessing technique for enhancing Pelvic MR images in rectal cancer diagnosis and treatment planning. The output image efficiency is calculated by Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE). © 2024 Owner/Author.

关键词： Mean square error

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