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Chinese control and Decision Conference, CCDC

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.

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Constrained Multi-Objective Optimal Combined Economic Emission Dispatch

Constrained Multi-Objective Optimal Combined Economic Emissi...

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2022 International Conference on electrical engineering and Sustainable Technologies, ICEEST 2022

作者： Ali, Mazhar Khan, Muhammad Ahmad Zain Yousaf, Muhammad Tahir, Muhammad Faizan Haleem Shah, Sayyed Qaboos, Abdullah Tianjin University School of Electrical and Electronic Information Control Engineering Tianjin China Tianjin University School of Electrical and Information Engineering Tianjin China Hubei University of Automotive Technology School of Electrical and Information Engineering Shiyan China South China University of Technology School of Electric Power Guangzhou China COMSATS University Islamabad Department of Electrical and Computer Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665462099

In this paper, researchers adopt multi-objective particle swarm optimization to tackle the issue of multi-objective combined economic emission dispatch (CEED). The equality and inequality restrictions in this optimization process make it non-linear. In most cases, it divides up the total amount of power generated by all the units in a given system. Primary goals include lowering fuel costs and minimizing environmental impact within prescribed constraints. Economic dispatch is often formulated as a nonlinear system, and the resulting solutions often fall behind expectations. However, these methods are not suitable for solving the economic dispatch issue because of their non-convex character. Several AI methods are used for the economic dispatch issue due to its complexity and the fact that it is neither linear nor smooth. The conventional form of the meta-heuristic approach, particle swarm optimization (PSO), has difficulty dealing with such difficulties, yet it is still widely utilized. In order to solve this issue, this work presents an updated form of particle swarm optimization. The efficiency of the customized version of specific swarm optimization (PSO) is verified in the MATLAB Simulink environment. Finding a Pareto-optimal, industrially, the multi-objective solution to the emission dispatch issue using multi-objective particle swarm optimization is shown to be a viable option. © 2022 IEEE.

关键词： Pareto principle

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Design and Implementation of 1.28 Tbps DWDM based RoF system with External Modulation and Dispersion Compensation Fiber 2

Design and Implementation of 1.28 Tbps DWDM based RoF system...

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2nd International Conference on Physics and Applied Sciences, ICPAS 2021

作者： Mohsen, D.E. Hammadi, A.M. Alaskary, A.J. Department of Computer Engineering Techniques Electrical Engineering Technical College Baghdad Iraq Department of Control and Automation Technical Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq

The improvement of 5G networks started in 2019 and is commonly accepted to bring changes not restricted to individuals' day-by-day life. The interconnection between the nodes of 5G network is realized through the utilization of optical transceiver modules and optical fibers. The most interesting part in the 5G communication network is the communication between the Central Office (CO) and the Base Station (BS), which has been widely investigated by several researchers to enhance and optimize such network performance. As a result, in this paper we demonstrate, design and implemented a) based on Dense Wavelength Division Multiplexing (DWDM Radio over Fiber (RoF) system and by using the software of Optisystem 17.1 version. Such system is considered a 32X40 Gbps of data transmission for higher speed transmission system towards the Tera bit per second (Tbps) communication. The channels of 1, 4, 8, 12, 16, 20, 24, 28 and 32 were selected as samples for the investigation. The performance analysis would be based on the parameters of the eye diagram, (Quality Factor) Q-factor and Min Bit Error Rate (BER) and for distances of 60, 120 and 180 km respectively. Analyzing results indicate a higher performance system toward the 1.28 Tbps of data rate transmission. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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Predicting of Photovoltaic Power Generation Using Similarity-Based Forecasting Models

Predicting of Photovoltaic Power Generation Using Similarity...

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International Middle-East Power System Conference, MEPCON

作者： Mohamed R. Aboelmagd Ali Selim Mamdouh Abdel-Akher Electrical Engineering Dept Aswan University Aswan Egypt Control and Computer Engineering Dept almaaqal university Basra Iraq Department of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt

Accurate forecasting in photovoltaic energy is one of the most essential integrated and influential events for managing the system of new and renewable energy sources in the power grid. The paper presents a practical approach using neural networks to predict photovoltaic energy generation. Applying neural networks in PV power generation forecasting is challenging due to unpredictable environmental changes such as solar radiation, temperature, humidity, wind speed, etc. Neural network methods are distinguished by achieving high performance in prediction compared to traditional mathematical models that have difficulties dealing with complex nonlinear relationships, resulting in substandard prediction. The proposed neural network-based forecasting model offers an effective way to improve the precision of estimating PV generation. It has a great deal of promise to enhance the integration and dependability of solar energy in the current electricity landscape because of its capacity to capture complex linkages in the data and adapt to changing circumstances.

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Distributed Optimization with Gradient Descent and Quantized Communication

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IFAC-PapersOnLine 2023年第2期56卷 5900-5906页

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Digital Futures SE-100 44 Stockholm Sweden Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node's estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples.

关键词： Distributed optimization quantized communication directed graphs finite-time consensus

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Optimisation-Based Iterative Learning control for Distributed Consensus Tracking

Optimisation-Based Iterative Learning Control for Distribute...

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UKACC International Conference on control (control)

作者： Yueqing Zhang Bin Chen Bing Chu Zhan Shu Faculty of Engineering and Physical Sciences University of Southampton Southampton Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.

关键词： System dynamics Heuristic algorithms Scholarships Predictive models Prediction algorithms Performance analysis Reliability

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Maximizing social power in multiple independent Friedkin-Johnsen models

Maximizing social power in multiple independent Friedkin-Joh...

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European control Conference (ECC)

作者： Lingfei Wang Yu Xing Claudio Altafini Karl H. Johansson Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of maximizing social power for a group of agents, who participate in multiple meetings described by independent Friedkin-Johnsen models. A strategic game is obtained, in which the action of each agent (or player) is her stubbornness over all the meetings, and the payoff is her social power on average. It is proved that, for all but some strategy profiles on the boundary of the feasible action set, each agent's best response is the solution of a convex optimization problem. Furthermore, even with the non-convexity on boundary profiles, if the underlying networks are given by a fixed complete graph, the game has a unique Nash equilibrium. For this case, the best response of each agent is analytically characterized, and is achieved in finite time by a proposed algorithm.

关键词： Network topology Europe Games Nash equilibrium Cost function Convex functions

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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

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computers, Materials & Continua 2021年第10期69卷 1323-1341页

作者： Walid El-Shafai Noha A.El-Hag Ghada M.El-Banby Ashraf A.M.Khalaf Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of EngineeringMinia UniversityEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the *** has dramatically affected daily lives,public health,and the world *** paper presents a segmentation and classification framework of COVID-19 images based on deep ***,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT ***,the resulting segmented images are used to identify the infected region,whether COVID-19 or *** proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and *** maxpooling layers are employed with a stride of 2 and a 2×2 *** classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT *** acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

关键词： Classification segmentation COVID-19 CNN deep learning diagnosis applications

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A Simultaneous Wireless Information and Power Transfer-based Multi-hop Clustering Routing Protocol for RF Energy Harvesting-Cognitive Radio Sensor Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 991-1007页

作者： Wang, Ji-Hong Su, Xi-Tong Ge, Yi-Yang Chen, Yeh-Cheng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University China School of Electrical Engineering Northeast Electric Power University 169 Changchun Road Jilin132012 China Department of Computer Science University of California DavisCA001313 United States

Radio frequency energy harvesting (RF EH) and clustering are promising solutions to solve the energy constraint problem faced by cognitive radio sensor networks (CRSNs). However, study on clustering routing protocol design for RF EH-CRSNs is still in the initial stage, and there lacks of solutions for inter-cluster routing problem, which restricts the network scalability. In addition, unbalanced harvested energy and energy consumption may result in premature death of nodes in key position, which will significantly reduce the network performance. In this paper, a simultaneous wireless information and power transfer (SWIPT)-based multi-hop clustering routing protocol (SMCRP) is proposed for RF EH-CRSNs to well balance the residual energy distribution among nodes and postpone node death. Apart from the RF energy harvested from dedicated or ambi-ent energy sources, the energy obtained through intra-cluster and inter-cluster SWIPT is also considered to facilitate high-quality cluster heads selection, cluster formation and inter-cluster route establishment. Power splitting ratio is derived based on the residual energy of CRSNs nodes and their energy consumption to enable effective implementation of SWIPT in CRSNs. Simulation results show that SMCRP is effective in balancing the residual energy distribution among nodes and improving node sustainability. To be specific, compared with current clustering protocols, the network lifetime is extended by 64.07% while intra-cluster energy balance degree and network energy balance degree are improved by 15.1% and 7.82%, respectively. © 2023 ISSN 2414-8105 (Online).

关键词： Cognitive radio

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Extremum Seeking and Adaptive Dynamic Programming for Distributed Feedback Optimization

Extremum Seeking and Adaptive Dynamic Programming for Distri...

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IEEE Conference on Decision and control

作者： Tong Liu Miroslav Krstić Zhong-Ping Jiang Department of Electrical and Computer Engineering Tandon School of Engineering Control and Networks Lab New York University Brooklyn NY USA Department of Mechanical and Aerospace Engineering University of California San Diego CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents’ outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents’ trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method.

关键词： Adaptation models Costs Distributed feedback devices Cost function Dynamic programming Trajectory Numerical models Signal design Multi-agent systems Convergence

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