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2011 1st IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2011

作者： Mashi, Mohammad Ament, Christoph Department of System Analysis Faculty of Computer Science and Automation Ilmenau University of Technology Ilmenau Germany

ISBN: (纸本)9781457710841

All manufacturing companies have been trying to integrate intelligence-oriented strategies into their workflow. This saves time and money and leads to a better quality of goods. This intelligent strategy is in the subject area of Artificial Intelligence (AI). In our contribution a Knowledge-Based System (KBS) for image processing and pattern recognition, machine learning (ML) is used to measure the quality of the distribution of salami slices on pizza crusts. Measuring quality provides the opportunity to correct the position of the salami slices by automated systems (e.g. a robot arm) in the future rather than by employees. © 2011 IEEE.

关键词： Fuzzy logic

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Agent DEVS simulation of the evacuation process from a commercial building during a fire 12th

Agent DEVS simulation of the evacuation process from a comme...

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12th International Scientific and Technical Conference computer science and Information Technologies, CSIT 2017

作者： Kulakowski, Andrzej Rogala, Bartosz Faculty of Electrical Engineering Automation Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783319705804

The paper describes a simulation application for the process of evacuating people from a building in the event of a fire. This simulation was developed as an agent system using the DEVS formalism. The following sections show the design and implementation of such a system using the Adevs library. A series of simulation tests were then performed for different simulation scenarios. © 2018, Springer International Publishing AG.

关键词： Office buildings

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Battery-friendly charging process scheduling of electric vehicle fleets at company sites

Battery-friendly charging process scheduling of electric veh...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Bodenschatz, Nicki Eider, Markus Kratschmer, Daniel Berl, Andreas Zimmermann, Armin Technische Hochschule Deggendorf Faculty Computer Science Deggendorf Germany Technische Universität Illmenau Department of Computer Science and Automation Ilmenau Germany

ISBN: (纸本)9798350322972

Companies adopt electric cars for their vehicle fleets to be more environmental friendly and sustainable. However, there is a battery degradation over the course of time. Wrong charging behavior can accelerate this process. To improve the battery-lifetime, an intelligent scheduling of the charging processes is necessary. The schedule also needs to take other aspects, like the power grid or planned tours of the cars in consideration, to guarantee their full operability. The paper formulates this scenario as an integer linear problem to compute optimal battery-friendly charging schedules that take into account the various requirements and restrictions of electric vehicle fleets in companies. Results for examples from an application project validate the research. © 2023 IEEE.

关键词： Electric vehicles

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Modeling and control of system with pump and pipeline by pole placement method

Modeling and control of system with pump and pipeline by pol...

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作者： Brezina, Tomas Kovar, Jiri Hejc, Tomas Faculty of Mechanical Engineering Brno University of Technology Institute of Automation and Computer Science Czech Republic

ISBN: (纸本)9788080754778

The present paper deals with control of system consists surge tank, pump a and pipeline. Model of this system is based on hydro-electrical analogy. For purpose of control it is designed a regulator with estimator. Regulator is developed by pole-placement method. Numerical solutions of this model are presented at the end of this article. © 2011 Univ of Trencin.

关键词： Pipelines

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An analysis of the BBO Fans online social gaming community

An analysis of the BBO Fans online social gaming community

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作者： Posea, Vlad Balint, Mihaela Dimitriu, Alexandru Iosup, Alexandru Faculty of Automation and Computer Science Politehnica University of Bucharest Romania Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

ISBN: (纸本)9781424473359

Online social gaming is an emerging Internet application that combines online gaming and online social networking functionality for the benefit of millions of daily users. While researchers have investigated the structure of (social) networks for decades, the activity characteristics and the community structure of online social gaming remain relatively unknown. To address this situation, in this work we investigate the BBO Fans club for online bridge. First, we introduce a method to collect and analyze data from BBO Fans and its underlying gaming platform. Our method is novel in that it addresses the lack of a strict definition of social relationship between BBO Fans players, and it defines several player types with implications on community formation and operation. Second, we use the proposed method to collect and analyze a 40-day BBO Fans dataset comprising over 140,000 unique players and over 3,000,000 unique play sessions. T hird, we compare the characteristics of BBO Fans with other large social networking and online gaming applications. We find in particular that BBO Fans generates similar levels of activity as but has different community characteristics than similarly-sized games on FaceBook, the largest social network in the world.

关键词： Data mining

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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming Wenyin Gong Ling Wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

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The monitoring of energy consumptions in a urban transportation system

The monitoring of energy consumptions in a urban transportat...

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6th International Conference on Electrical and Electronics Engineering, ELECO 2009

作者： Nicolae, Petre-Marian Nicolae, Ileana-Diana University of Craiova Faculty of Electrotechnips Craiova Romania University of Craiova Faculty of Automation Computer Science and Electrons Craiova Romania

ISBN: (纸本)9789944898188

The paper presents a detailed analysis of the energy consumptions recorded in the distribution substations from a public transportation system. One presents the evolution of the energy consumptions at the level of the public transportation system and individually, at the level of transformation substations. One performs an analysis of the power factor at global level and respectively separately, for each transformation substation. There are presented aspects concerning the penalties applied to consumer due to the decreasing of the power factor from its own distribution network. The measuring process used the analogue apparatus and a quality analyzer from the distribution stations.

关键词： Energy utilization

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A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

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computers, Materials & Continua 2022年第1期70卷 73-89页

作者： Harshita Patel Dharmendra Singh Rajput Ovidiu Petru Stan Liviu Cristian Miclea School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India Technical University of Cluj Napoca Faculty of Automation and Computer ScienceCluj Napoca400114Romania

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other *** Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important *** case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to *** paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this *** have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing *** K nearest neighbor is a non-parametric classification method that is mostly used in machine learning *** classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced *** proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional *** show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

关键词： Machine learning fuzzy classification nearest neighbor adaptive approach optimal weights

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization With Variance Reduction and Nonasymptotic Convergence Rates

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen Central South University School of Automation Hunan Changsha 410083 China City University of Hong Kong Department of Mechanical Engineering Hong Kong University of New South Wales School of Electrical Engineering and Telecommunications Sydney 2052 NSW Australia Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing 400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen 518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into Prox-DBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks. © 2013 IEEE.

关键词： Byzantine-resilient algorithms composite optimization decentralized stochastic optimization system security variance reduction

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Adaptive Fuzzy Yaw Moment Control of Humanoid Robot Based On Ankle Joint 34

Adaptive Fuzzy Yaw Moment Control of Humanoid Robot Based On...

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第三十四届中国控制会议

作者： YANG Liang LIU Zhi ZHANG Yun Faculty of Automation Guangdong University of Technology School of Computer Engineering University of Electronic Science and Technology of China Zhongshan Institute

ISBN: (纸本)9781467374439

This paper investigates the problem of yaw moment control of humanoid robot and presents a robust adaptive control system for compensating the undesired yaw moment. In order to get the ideal ankle joint trajectory with low energy consumption,a novel yaw moment control based on ankle is proposed. The main strategy in this method is to adjust ankle joint trajectory in a way to exert a moment for counteracting the factors which generate the undesired yaw moment. Given the optimized ankle joint angles motion, an adaptive fuzzy control system is proposed to track the desired trajectories with model uncertainties and the stability proof is provided. Simulation results validate the proposed method.

关键词： humanoid robot,yaw moment,adaptive fuzzy control,robustness

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