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arXiv 2024年

作者： Yu, Wenlu Xu, Jie Zhao, Chengwei Zhao, Lijun Nguyen, Thien-Minh Yuan, Shenghai Bai, Mingming Xie, Lihua State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou311200 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud;the system process noise is difficult to be determined during state estimation of the complex motions;and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I2EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： state estimation

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Observer-Based SMC for Dynamic Wireless Charging System of Electric Vehicles with Complex Disturbances

Observer-Based SMC for Dynamic Wireless Charging System of E...

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Jiawang Yue Zhitao Liu Zhao Jiang Daidai Chen Hongye Su Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Ningbo Preh Joyson Automotive Electronics Co. Ltd Ningbo Joyson Advanced Energy Research Institute Co. Ltd Northwestern Polytechnical University Ningbo China Ningbo Joyson Advanced Energy Research Institute Co. Ltd Ningbo China

This paper focuses on voltage control for a dynamic wireless charging system of electric vehicles based on an extended state observer. Each dynamic wireless charging system will suffer from complex disturbances, leading to the challenge of output voltage control. To address this issue, we propose a disturbance rejection control scheme that utilizes an extended state observer to estimate the unknown disturbances. A dynamic sliding-mode surface is designed to suppress estimation error, and a sliding mode control law based on the estimated information and sliding-mode surface is proposed to accurately track the expected voltage. We prove the stability of the closed-loop system, and simulation results confirm the effectiveness of the proposed disturbance rejection control scheme for dynamic wireless charging systems based on the extended state observer.

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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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Optimal Model and Dispatch Strategy of a Multi-Energy Microgrid Based on Hybrid Pumped Hydro-Battery Storage and Ground Source Heat Pump

SSRN

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SSRN 2023年

作者： Li, Xinyue Wang, Honglei Li, Bin Tan, Zhukui Electrical Engineering College Guizhou University Guiyang550025 China Key Laboratory of "Internet+" Collaborative Intelligent Manufacturing in Guizhou Provence Guiyang Guizhou550025 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Electric Power Research Institute Guizhou Power Grid Co. Ltd Guiyang550002 China

The carbon neutrality target and the carbon emission problem caused by the ever-increasing energy demand necessitate the extensive use of renewable energy (RES) generation technologies. Multi-energy microgrid (MEMG) become effective vehicles for managing RES generation and storage. However, the uncertainty of RES and the randomness of load demand poses a challenge to the design and operation of MEMG. This study proposes a MEMG including combined cooling, heating, power system, pumped hydro storage, and ground source heat pump to solve the problem. Combined with demand-side management strategies, a system is designed considering complementary configuration and energy conversion management. A corresponding optimization model is established to minimize the total cost and maximize the island operation while satisfying the demand. The experimental results show that the established model and scheduling method improves the system’s ability to cope with the RES’ uncertainty and reduces the capacity of the energy storage system. In summer, the daily investment cost is saved by 1439RMB, and the utilization rate of clean energy reaches 96%. Compared with the uncombined system, the MEMG reduces 1620.02kg of carbon dioxide per day, realizing the full utilization of clean energy and having the role of demonstration and promotion. © 2023, The Authors. All rights reserved.

关键词： Battery storage

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Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition

arXiv

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arXiv 2024年

作者： Yue, Hao Wu, Yingtao Wang, Min Hu, Hesuan Wu, Weimin Zhang, Jihui Shandong Qingdao266580 China College of Information Engineering Yangzhou University Jiangsu Yangzhou225127 China School of Electro-Mechanical Engineering Xidian University Shanxi Xi’an710071 China School of Computer Science and Engineering College of Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China Institute of Complexity Science School of Automation Qingdao University Shandong Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Shandong Qingdao266071 China

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

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A Hybrid Algorithm Introducing Cross Mutation and Non-linear Learning Factor for Optimal Allocation of DGs and Minimizing Annual Network Loss in the Distribution Network

引用

IAENG International Journal of Applied Mathematics 2021年第3期51卷 1-18页

作者： Chen, Gonggui Li, Shitao Long, Hongyu Zeng, Xianjun Kang, Peng Zhang, Jinming 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 State Grid Chongqing Electric Power Company Chongqing400015 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China

Distributed generators (DGs) are recognized as an effective method for controlling the power loss, voltage stability, etc.. In this paper, a novel hybrid algorithm of beetle antennae search (BAS) and particle swarm optimization (PSO) is presented for optimal allocation of DGs in radial distribution network. The BAS describes the beetle’s individual search by smell, PSO describes the group search of birds by location. The proposed algorithm combines their advantages, proceeds with individual optimization while conducting group optimization. Therefore, the proposed algorithm searches widely, and converges fast. In this paper, a series of improvement measures are proposed to deal with the shortcoming of PSO-BAS which is easy to fall into local optimum. These methods include equal interval initialization, cross mutation, and non-linear learning factor. This paper will show the comparison results of PSO-BAS and IPSO-BAS in the six confessed test functions to prove the necessity of the improved methods. Simultaneously, in order to verify the feasibility and effectiveness of this proposed algorithm in terms of practical application, it is tested on the standard IEEE 33-bus, IEEE 69-bus and IEEE 119-bus systems. The results of active power loss and voltage stability show that proposed algorithm is more effective and more suitable for the power distribution system than other algorithms. At the same time, this article also explores the impact of new energy sources on the annual network loss. Besides, a method for optimizing the annual network loss is proposed. Here, the IPSO-BAS algorithm is used to adjust the size of multiple biomass energy sources within 24 hours to optimize the network loss, and a comparison plan is designed to verify the feasibility of the proposed method. According to the final result, this proposed method can greatly reduce the annual network loss. © 2021. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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AcWRKY28 mediated activation of AcCPK genes confers salt tolerance in pineapple(Ananas comosus)

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Horticultural Plant Journal 2024年第2期10卷 398-412页

作者： Qiao Zhou Samaranayaka Vidana Gamage Nirosha Priyadarshani Rongjuan Qin Han Cheng Tiantian Luo Myat Hnin Wai Mohammad Aqa Mohammadi Yanhui Liu Chang Liu Hanyang Cai Xiaomei Wang Yeqiang Liu Yuan Qin Lulu Wang Guangxi Key Lab of Sugarcane Biology State Key Laboratory for Conservation and Utilization of Subtropical Agro-BioresourcesCollege of AgricultureGuangxi UniversityNanningGuangxi 530004China State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops College of Life ScienceCollege of Plant ProtectionCollege of AgricultureCollege of HorticultureFujian Provincial Key Laboratory of Haixia Applied Plant Systems BiologyCenter for Genomics and BiotechnologyFujian Agriculture and Forestry UniversityFuzhouFujian 350002China Pingtan Science and Technology Research Institute of Fujian Agriculture and Forestry University PingtanFujian 350400China National Education Commission BMICHColombo00700Sri Lanka Fishery Multiplication Management Station of Lijiang River Water Supply Hub Project GuilinGuangxi 541001China Horticulture Research Institute Guangxi Academy of Agricultural SciencesNanning Investigation Station of South Subtropical Fruit TreesMinistry of AgricultureNanningGuangxi 530007China

Unfavorable environmental cues severely affect crop productivity resulting in significant economic losses to farmers. In plants, multiple regulatory genes, such as the WRKY transcription factor (TF) family, modulate the expression of defense genes. However, the role of the pineapple WRKY genes is poorly understood. Here, we studied the pineapple WRKY gene, AcWRKY28, by generating AcWRKY28 over-expressing transgenic pineapple plants. Overexpression of AcWRKY28 enhanced the salt stress resistance in transgenic pineapple lines. Comparative transcriptome analysis of transgenic and wild-type pineapple plants showed that “plant-pathogen interaction” pathway genes, including 9calcium-dependent protein kinases (CPKs), were up-regulated in AcWRKY28 over-expressing plants. Furthermore, chromatin immunoprecipitation and yeast one-hybrid assays revealed AcCPK12, AcCPK3, AcCPK8, AcCPK1, and AcCPK15 as direct targets of AcWRKY28. Consistently, the study of AcCPK12 over-expressing Arabidopsis lines showed that AcCPK12 enhances salt, drought, and disease resistance. This study shows that AcWRKY28 plays a crucial role in promoting salt stress resistance by activating the expression of AcCPK genes.

关键词： Pineapple AcWRKY AcCPK Transcription factor Salt stress ChIP

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HIDG-based PSO Algorithm: Suspicious Sensor Selection for Power Grid Monitoring System Under Data Integrity Attack

HIDG-based PSO Algorithm: Suspicious Sensor Selection for Po...

引用

Electrical Engineering and control Technologies (CEECT), Conference on

作者： Pandeng Li Zhihong Liang Yiwei Yang Liangyu Dong Yuhan Suo Yi Hao Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou Guangdong China China Industrial Control Systems Cyber Emergency Response Team Beijing China School of Automation Beijing Institute of Technology Beijing China

With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system.

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