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Power Switching Based on Trajectory Planning and Sliding Mode control for Solid Oxide Fuel Cell Systems

Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1968-1979页

作者： Zhen Wang Guoqiang Liu Xingbo Liu Jie Wang Zhiyang Jin Xiaowei Fu Zhuo Wang Bing Jin Zhonghua Deng Xi Li Key Laboratory of Imaging Processing and Intelligent Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Research Institute of Huazhong University of Science and Technology in Shenzhen ShenzhenChina Hubei Huazhong Electric Power Technology Development Co. Ltd.WuhanChina School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanChinaand he is also with the Wuhan Huamao Automation Co.Ltd.WuhanChina

To improve the safety of the solid oxide fuel cell(SOFC)systems and avoid the generation of large amounts of pollutants during power switching,this paper designs a power switching strategy based on trajectory planning and sliding mode control(TP-SMC).The design elements of the power switching strategy are proposed through simulation analysis at ***,based on the gas transmission delay time and the change of gas flow obtained from testing,trajectory planning(TP)is *** with other power switching strategies,it has been proven that the power switching strategy based on TP has significantly better control ***-thermore,considering the shortcomings and problems of TP in practical application,this paper introduces sliding mode control(SMC)on the basis of TP to improve the power switching *** final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay *** with TP,TP-SMC can ensure that under uncertain conditions,the SOFC system does not experience fuel starvation and temperature exceeding limit during power ***,the NOx emissions are also within the normal and acceptable *** paper can guide the power switching process of the actual SOFC sys-tems to avoid safety issues and excessive generation of NOx,which is very helpful for improving the performance and ser-vice lifeof the SOFCsystems.

关键词： Solid oxide fuel cell(SOFC)system power switching safety NOx emission trajectory planning sliding mode control

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

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arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data control 63

Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Qian, Juan Wang, Xiaoling Astolfi, Daniele Su, Housheng Jiang, Guo-Ping Nanjing University of Posts and Telecommunications College of Automation and the College of Artificial Intelligence Nanjing210023 China Nanjing210023 China Université Claude Bernard Lyon 1 Cnrs Lagepp Umr 5007 43 boulevard du 11 novembre 1918 VilleurbanneF-69100 France East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Luoyu Road 1037 Wuhan430074 China

ISBN: (纸本)9798350316339

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent's dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach. © 2024 IEEE.

关键词： Decentralized control

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Deployment of Heterogeneous Multi-Agent Systems via a PDE Approach

Deployment of Heterogeneous Multi-Agent Systems via a PDE Ap...

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Chinese Automation Congress (CAC)

作者： Jingtao Man Zhigang Zeng Qiang Xiao Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China School of Artificial Intelligence and Automation (of Huazhong University of Science and Technology) Wuhan China

Spatial deployment of large-scale heterogeneous multi-agent systems (HMASs) over desired 2D or 3D curves is investigated in this paper. With assumption that HMASs consist of numerous first-order agents (FOAs) and second-order agents (SOAs) that could obtain local information of desired curves and their positions relative to their closest neighbors, the collective dynamics of large-scale HMASs are modeled as heterogeneous partial differential equations (PDEs). In particular, this paper introduces series-dependent topological weights between neighboring agents, which are more versatile and practical than constant topological weights commonly used in previous studies. A novel single-point control scheme is proposed, where an informed agent is situated between the last FOA and first SOA. This operation could not only ensure successful implementation of spatial deployment, but also guarantee well-posedness of the constructed heterogeneous error PDEs. By utilizing inequality techniques, sufficient conditions for exponential convergence of error system are derived. A numerical example is presented to demonstrate effectiveness of the proposed approaches.

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Point-Query Quadtree for Crowd Counting, Localization, and More

arXiv

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arXiv 2023年

作者： Liu, Chengxin Lu, Hao Cao, Zhiguo Liu, Tongliang Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China The University of Sydney Australia

We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation;too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://***/cxliu0/PET. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A New Multi-Scale Model for Medium-Term Load Forecasting

A New Multi-Scale Model for Medium-Term Load Forecasting

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第35届中国控制与决策会议

作者： Peng Liu Hongliang Fang Jiang-Wen Xiao Yan-Wu Wang Xiao-Kang Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education

When a large number of distributed power and renewable energy are connected to the smart grid,the volatility and intermittency of renewable energy bring some challenges to the smart ***,accurate medium-term load forecasting is essential because it is conducive to the stability of the power grid and can provide data support for the power generating *** factors affect medium-term load forecasting and the real-time electricity price is a very important factor among *** this paper,a multi-scale model based on LSTM model is proposed to extracts features from 3 different time scales including half-hourly time scale,daily time scale and monthly time *** first,the half-hourly data is processed by a half-hourly data processing layer to extract the half-hourly ***,its output is concatenated with the daily load data and is input into a daily data processing ***,the daily features are concatenated with the monthly load data and they are input into a monthly data processing layer to extract the monthly features and get the final forecasting *** case study results demonstrate that the proposed multi-scale model has better performance than the single-scale models.

关键词： Deep learning medium-term load forecasting LSTM real-time electricity price

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Point-Query Quadtree for Crowd Counting, Localization, and More

Point-Query Quadtree for Crowd Counting, Localization, and M...

引用

International Conference on Computer Vision (ICCV)

作者： Chengxin Liu Hao Lu Zhiguo Cao Tongliang Liu Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China The University of Sydney Australia

We show that crowd counting can be viewed as a decomposable point querying process. This formulation enables arbitrary points as input and jointly reasons whether the points are crowd and where they locate. The querying processing, however, raises an underlying problem on the number of necessary querying points. Too few imply underestimation; too many increase computational overhead. To address this dilemma, we introduce a decomposable structure, i.e., the point-query quadtree, and propose a new counting model, termed Point quEry Transformer (PET). PET implements decomposable point querying via data-dependent quadtree splitting, where each querying point could split into four new points when necessary, thus enabling dynamic processing of sparse and dense regions. Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable. We demonstrate the applications of PET on a number of crowd-related tasks, including fully-supervised crowd counting and localization, partial annotation learning, and point annotation refinement, and also report state-of-the-art performance. For the first time, we show that a single counting model can address multiple crowd-related tasks across different learning paradigms. Code is available at https://***/cxliu0/PET.

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Statistical Feasibility Robust Optimization for Hydrogen-Data Center Microgrid Operation

SSRN

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SSRN 2024年

作者： Duan, Juntao Li, Yuanzheng Li, Yang Liu, Zhiwei Zhao, Yong School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The increasing processing demands of information technology (IT) workloads cause the booming of data centers (DCs) in recent years. The servers in the DC emit much heat when processing the IT workloads and thus the DC temperature rises. The power consumption when controlling the DC temperature using the air conditioning (A/C) of the DC is high, which leads to expensive power bills. To reduce the power cost, using renewable energy (RE) to power DC has raised much attention in recent years. However, the intermittency of RE and the uncertainties of outside temperature pose operational challenges. Therefore, in this paper, a data-driven statistical-feasibility-based robust rolling optimization framework for the operation of hydrogen-DC microgrid (H2-DCMG) is proposed to control the DC temperature considering these uncertainties and the intermittency of RE. In the framework, the hydrogen energy storage system (HESS) is introduced to deal with the intermittency of RE, and robust optimization (RO) is adopted to deal with the uncertainty of outside temperature. The statistical feasibility is introduced into the framework to decrease the conservatism of RO, through relaxing the DC temperature constraints without harming normal DC operations. Moreover, we propose the Statistical-Guarantee-based Vertex Link (SGVL) algorithm to construct a polyhedron uncertain set of outside temperatures that can ensure the results of dispatch are economical and satisfy statistical feasibility. The constructed polyhedron uncertain set is linear, which reduces computational complexity in solving. A case study based on the real-world temperature dataset verifies the superiority of the SGVL algorithm on the aspect of the economic cost. It further validates the effectiveness of the proposed robust optimization framework for DC temperature control. © 2024, The Authors. All rights reserved.

关键词： Air conditioning

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