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Research on Multi-objective Active Power Optimization Simulation of Novel Improved Water Cycle Algorithm

IAENG International Journal of Applied Mathematics 2021年第1期51卷 1-20页

作者： Chen, Gonggui Han, Ying Zhang, Zhizhong Zeng, Xianjun Li, Shuaiyong Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Communication Network and Testing Technology Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China

Water cycle algorithm (WCA) is a heuristic algorithm proposed in recent years. To overcome the insufficiency of standard WCA algorithm in solving the non-convex optimal power flow (OPF) problems, the multi-objective novel improved water cycle algorithm (MONIWCA) is proposed in this paper. The evaporation process is improved in WCA by introducing evaporation rate and the normal distribution optimization mechanism is used to mutate the individual position. The modified WCA also adopts a constraint-based strategy to ensure zero constraint violations. In order to obtain a high-quality Pareto optimal solution set (POS) and select the best compromise solution (BCs), a global ranking strategy is proposed. The global ranking strategy includes the novel constraint handling method, the rank index calculation and the BCs on fuzzy satisfaction theory to deal with the complex constraints of the optimization problem. The MONIWCA has been simulated under the constraints of zero violations on IEEE 30, IEEE 57 and IEEE 118 standard test systems, including six dual-objective cases and one tri- objective case. The simulation results are compared with the multi-objective particle swarm optimization (MOPSO). The results show that the improved method can effectively solve the MOOPF problem, not only to obtain a uniform continuous Pareto solution set but also to achieve a better compromise solution. In addition, the two performance indicators of the generational distance (GD) and the spacing (SP) also show that the MONIWCA algorithm has uniform distribution, high convergence and strong stability. © 2021, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Breakdown of effective-medium theory by a photonic spin Hall effect

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Science China(Physics,Mechanics & Astronomy) 2023年第11期66卷 145-154页

作者： Shuaijie Yuan Xinxing Zhou Yu Chen Yuhan Zhong Lijuan Sheng Hao Hu Hongsheng Chen Ido Kaminer Xiao Lin Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and ApplicationsSchool of Physics and ElectronicsHunan Normal UniversityChangsha410081China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong ProvinceInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhen518060China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou310027China International Joint Innovation Center Electromagnetics Academy at Zhejiang UniversityZhejiang UniversityHaining314400China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore639798Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of EducationCollege of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing211106China Key Laboratory of Advanced Micro/Nano Electronic Devices&Smart Systems of Zhejiang Jinhua Institute of Zhejiang UniversityZhejiang UniversityJinhua321099China Shaoxing Institute of Zhejiang University Zhejiang UniversityShaoxing312000China Department of Electrical Engineering Technion-Israel Institute of TechnologyHaifa32000Israel

Effective-medium theory pertains to the theoretical modelling of homogenization,which aims to replace an inhomogeneous structure of subwavelength-scale constituents with a homogeneous effective *** effective-medium theory is fundamental to various realms,including electromagnetics and material science,since it can largely decrease the complexity in the exploration of light-matter interactions by providing simple acceptable ***,the effective-medium theory is thought to be applicable to any all-dielectric system with deep-subwavelength constituents,under the condition that the effective medium does not have a critical angle,at which the total internal reflection *** we reveal a fundamental breakdown of the effective-medium theory that can be applied in very general conditions:showing it for deep-subwavelength all-dielectric multilayers even without a critical *** finding relies on an exotic photonic spin Hall effect,which is shown to be ultrasensitive to the stacking order of deep-subwavelength dielectric layers,since the spin-orbit interaction of light is dependent on slight phase accumulations during the wave *** results indicate that the photonic spin Hall effect could provide a promising and powerful tool for measuring structural defects for all-dielectric systems even in the extreme nanometer scale.

关键词： photonic spin Hall effect effective-medium theory spin-orbit interaction of light

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Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism*

Parallel Treasury for TRUE DAOs: Model, Indicators and Mecha...

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control Conference (ANZCC), Australian and New Zealand

作者： Sangtian Guan Juanjuan Li Wenwen Ding Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.

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Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

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Heterogeneous Federated Learning for Multi-Center Ophthalmic Disease Diagnosis

SSRN

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SSRN 2023年

作者： Gu, Aoguang Jiang, Xiaoyu Gao, Zhiyuan Jin, Kai Ye, Juan Ge, Zhiqiang The State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Ophthalmology The Second Affiliated Hospital of Zhejiang University Zhejiang Hangzhou310027 China The Peng Cheng Laboratory Guangdong Shenzhen518000 China

Deep learning and computer vision (CV) can easily recognize pictures that the human doctor would have to spend a lot of time and labor to process. Thus they have a wide range of applications in the field of ophthalmology, which is highly dependent on fundus images. However, training a robust and highly accurate model requires a large number of labeled high-quality images, which is often difficult for a single institution to provide. A natural solution is to use data from multiple medical centers to train the model. But due to privacy protection, data heterogeneity and other problems, these data cannot be shared directly. Based on a real case of ophthalmic disease diagnosis with multiple data sources, this paper proposes solutions to the heterogeneity of data amount, features, and labels between datasets, establishes a heterogeneous federated learning framework, and applies federated learning to the diagnosis of multiple types of ophthalmic diseases for the first time. The experimental results on clinical multi-center datasets show that federated learning can greatly improve the diagnostic accuracy of clients with fewer data, and the accuracy of clients with more data can also be slightly improved. The ablation experiment proves that the proposed methods to deal with data heterogeneity are effective. Our research shows that through heterogeneous federated learning, we can use multi-source and nonindependent-identical data to build accurate disease diagnosis models, and make clients that are not able to obtain effective models get a practical-level disease diagnosis model. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Iterative assist-as-needed control with interaction factor for rehabilitation robots

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Science China(Technological Sciences) 2021年第4期64卷 836-846页

作者： CAO Ran CHENG Long YANG ChenGuang DONG Zhe School of Artificial Intelligence University'of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Institute of Automation.Chinese Academy of SciencesBeijing 100190China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China School of Electrical and Control Engineering North China Universityof TechnologyBeijing 100144China

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system’s stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient’s motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

关键词： human-robot interaction impedance stiffness control rehabilitation

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A Novel Approach Based on Modified and Hybrid Flower Pollination Algorithm to Solve Multi-objective Optimal Power Flow

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IAENG International Journal of Applied Mathematics 2021年第4期51卷 1-18页

作者： Chen, Gonggui Qin, Qilin Zhou, Ping Kang, Peng Zeng, Xianjun Long, Hongyu Zou, Mi Professor of Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Master degree candidate of Chongqing University of Posts and Telecommunications Chongqing400065 China Senior engineer of Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China Senior engineer of State Grid Chongqing Electric Power Company Chongqing400015 China Professor level senior engineer of Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Assistant professor of Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

In this paper, a modified and hybrid flower pollination algorithms (MHFPA) is proposed for dealing with the multi-objective optimal power flow (MOOPF) problem with conflictive objectives. The algorithm combines the mutation and crossover process in the differential evolution (DE) algorithm, introduces the sinusoidal nonlinear dynamic switching probability (SNDSP) and the elite strategy of elder generation (ESEG), which can improve the shortcomings of the original pollen algorithm that it is easy to fall into the local optimum and the diversity is insufficient. A screening approach with Pareto-dominant rule (SAPR) is proposed to ensure that the state variable can satisfy the inequality constraints of the power system. A uniformly distributed Pareto optimal set (POS) is obtained by the non-dominant sorting with elite strategy (NSES) based on Rank and Density estimation, and the best trade-off solution (BTS) is determined from the POS obtained by the fuzzy affiliation theory. For practicality, the total fuel cost, active power loss, emissions and voltage deviation are selected as objective functions. Due to the limitations of the actual power system, the valve point effect is also considered. The IEEE30-, 57- and IEEE118-bus test systems are used to verify the performance of the proposed MHFPA. In addition, two © 2021. All Rights Reserved.

关键词： Electric load flow

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Human hair follicle-derived mesenchymal stem cells:Isolation,expansion,and differentiation

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World Journal of Stem Cells 2020年第6期12卷 462-470页

作者： Bo Wang Xiao-Mei Liu Zi-Nan Liu Yuan Wang Xing Han Ao-Bo Lian Ying Mu Ming-Hua Jin Jin-Yu Liu Department of Toxicology School of Public HealthJilin UniversityChangchun 130021Jilin ProvinceChina Research Center for Analytical Instrumentation Institute of Cyber-Systems and ControlState Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhou 310000Zhejiang ProvinceChina

Hair follicles are easily accessible skin appendages that protect against cold and potential *** follicles contain various pools of stem cells,such as epithelial,melanocyte,and mesenchymal stem cells(MSCs)that continuously self-renew,differentiate,regulate hair growth,and maintain skin ***,MSCs derived from the dermal papilla or dermal sheath of the human hair follicle have received attention because of their accessibility and broad differentiation *** this review,we describe the applications of human hair follicle-derived MSCs(hHF-MSCs)in tissue engineering and regenerative *** have described protocols for isolating hHF-MSCs from human hair follicles and their culture condition in *** also summarize strategies for maintaining hHF-MSCs in a highly proliferative but undifferentiated state after repeated in vitro passages,including supplementation of growth factors,3D suspension culture technology,and 3D aggregates of *** addition,we report the potential of hHF-MSCs in obtaining induced smooth muscle cells and tissue-engineered blood vessels,regenerated hair follicles,induced red blood cells,and induced pluripotent stem *** summary,the abundance,convenient accessibility,and broad differentiation potential make hHF-MSCs an ideal seed cell source of regenerative medical and cell therapy.

关键词： Human hair follicle Regenerative therapy Mesenchymal stem cell Tissue engineering Cell differentiation

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Design and demonstration of a dynamic wireless power transfer system for electric vehicles

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Science China(Information Sciences) 2019年第12期62卷 223-241页

作者： Ze ZHOU Liyan ZHANG Zhitao LIU Longhua MA Miao HUANG Hongye SU School of Automation Wuhan University of Technology State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Ningbo Institute of Technology Zhejiang University

The usage of electric vehicles is being globally promoted with the development of clean energy. However, the development of electric vehicles is limited by the battery capacity and the charging tech-

关键词： cycle Design and demonstration of a dynamic wireless power transfer system for electric vehicles

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