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International Conference on Computer Vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 764-775页

作者： Jinchuan Qian Li Jiang Zhihuan Song State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Shanghai Research Institute of Huawei Technology Co. LtdShanghai 200127China

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

关键词： Auto-encoder(AE) deep learning fault diagnosis locally linear model nonlinear process reconstruction based contribution(RBC)

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Capacity Model and Optimal Scheduling Strategy of Multi-Microgrid Based on Shared Energy Storage

SSRN

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SSRN 2024年

作者： Dai, Bin Wang, Honglei Li, Bin Li, Chengjiang Tan, Zhukui Electrical Engineering College Guizhou University Guiyang550025 China Key Laboratory of "Internet+" Collaborative Intelligent Manufacturing in Guizhou Provence Guizhou Guiyang550025 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China School of Engineering University of Tasmania HobartTAS7005 Australia Electric Power Research Institute Guizhou Power Grid Co. Ltd. Guiyang550002 China

The large-scale use of renewable energy (RE) requires proportional investment in energy storage to solve the uncertainty of both the supply and demand sides of the power grid, which brings about the problems of high investment costs and long payback periods. This paper proposes a multi-microgrid energy storage sharing (SES) model. The SES model shapes the virtual energy storage capacity during power system operation, and the demand for energy storage capacity is reduced. A benefit distribution mechanism is developed to ensure fair income distribution among participants based on their investment proportion, promoting direct benefit interaction. A bi-level optimization method is designed to optimize the energy storage capacity and scheduling strategy simultaneously to ensure that they match each other. A non-dominated sorting equilibrium optimizer algorithm is proposed to avoid the Pareto solution set falling into local optimal and ensure the reasonable realization of the proposed benefit distribution mechanism. The results show that compared with distributed energy storage, the SES model reduces the required storage capacity of the system by 43.27% and reduces the daily investment and operation and maintenance cost by 25.98%. With the same operational performance, the SES model requires less storage capacity and 97.30% self-consumption of RE. © 2024, The Authors. All rights reserved.

关键词： Investments

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Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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Science China(Information Sciences) 2020年第11期63卷 195-207页

作者： Yuyan GUO Bin XU Weixin HAN Shuai LI Yueping WANG Yu ZHANG School of Automation Northwestern Polytechnical University Department of Computing The Hong Kong Polytechnic University Flight Automatic Control Research Institute State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

This paper investigates the state-constrained controller design of a hypersonic flight vehicle(HFV) based on an asymmetric barrier Lyapunov function(ABLF). The robust adaptive back-stepping controller with integral terms is applied for the HFV longitudinal dynamics. Considering the asymmetric angle of attack(AOA) constraint caused by the unique structure and scramjet, the controller is modified by constructing an ABLF, where the asymmetric constraint on AOA tracking error is introduced. Combined with the constraint on virtual control, the AOA is restricted to a predefined asymmetric interval. The system stability and the AOA constraint are guaranteed via Lyapunov analysis. Simulation results verify that the AOA can be kept in the given asymmetric interval while the altitude reference signal is tracked.

关键词： hypersonic flight vehicle HFV integral back-stepping control adaptive control asymmetric barrier Lyapunov function ABLF angle of attack (AOA) constraint

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Soil Health Evaluation of Non-grain Cultivated Land Based on Soil Quality Index Area Method and Soil Sensitivity-resistance Method: A Case Study of Ningbo City

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Huanjing Kexue/Environmental Science 2025年第6期46卷 3877-3889页

作者： Yang, Ya-Ting Qin, Fang-Jin Wang, Shuang Wang, Cong Zhu, Zhen-Ke Li, Gang Jin, Shu-Quan Ge, Ti-Da Guangxi Key Laboratory of Forest Ecology and Conservation State Key Laboratory for Conservation Utilization of Subtropical Agro-Bioresources College of Forestry Guangxi University Nanning530004 China State Key Laboratory for Managing Biotie and Chemieal Threats Quality and Safety of Agro-products Institute of Plant Virology Ningbo University Ningbo315211 China Ningbo General Station of Agricultural Technology Extension Ningbo315000 China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control CAS Haixi Industrial Technology Innovation Center in Beilun Ningbo315830 China Ningbo Academy of Agricultural Science Ningbo315101 China

The aim of this study was to understand the soil health Status of non-grain cultivated land in Ningbo, identify the main obstacle factors of non-grain cultivated land, explore the effect of non-grain on soil quality, and construct a comprehensive soil health evaluation System of non-grain cultivated land in Ningbo. Taking the soil of control cultivated land and typical non-grain cultivated land in Ningbo as the research object, 268 surface soil samples of 0-20 cm were collected under the utilization of control cultivated land, orchards, and nursery-grown plants. The 22 indexes of soil physics, chemistry, biology, and heavy metals were determined;the minimum data set was constructed by using principal component analysis, correlation analysis, and Norm value;and the soil health evaluation System in Ningbo was constructed using the soil quality index area method and soil sensitivity-resistance method, combined with soil multifunctionality to judge the quality of non-grain cultivated land in Ningbo. The results showed that: ® Compared with the soil of control cultivated land, soil bulk density (BD) in orchards and nursery-grown plants increased significantly (P © 2025 Science Press. All rights reserved.

关键词： Iron analysis

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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

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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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Removal performance,mechanisms,and influencing factors of biochar for air pollutants:a critical review

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Biochar 2022年第1期4卷 678-701页

作者： Zhipeng Zhao Bing Wang Benny K.G.Theng Xinqing Lee Xueyang Zhang Miao Chen Peng Xu College of Resource and Environmental Engineering Guizhou UniversityGuiyang 550025China Key Laboratory of Karst Georesources and Environment Ministry of EducationGuiyang 550025China Manaaki Whenua-Landcare Research Palmerston NorthNew Zealand State Key Laboratory of Environmental Geochemistry Institute of GeochemistryChinese Academy of SciencesGuiyang 550081China Jiangsu Key Laboratory of Industrial Pollution Control and Resource Reuse School of Environmental EngineeringXuzhou University of TechnologyXuzhou 221018China

The emission of air pollutants from various industries is a major contributor to environmental *** removal of these pollutants before they are discharged into the environment has become an important means of controlling air *** has attracted increasing attention because of its low cost,high porosity,large specific surface area,abundant surface functional groups,and high removal *** physicochemical properties of biochar are greatly affected by feedstock types,preparation,and modification *** this reason,the capacity and propensity of biochar for removing air pollutants are rather *** understand the existing research status and grasp the latest research progress,a systematic review on the removal of different air pollutants by biochar is highly *** on the recent research,this paper systematically analyzes and summarizes the preparation and modification methods of biochar commonly used for the removal of six air pollutants(SO_(2),H_(2)S,CO_(2),Hg0,VOCs,and NH_(3)),as well as the removal performance and ***,the potential influencing factors(preparation parameters,physicochemical characteristics of biochar,and removal conditions)are ***,the regeneration of biochar,suggestions,and future perspectives are proposed.

关键词： Biochar Modification Air pollutants Removal performance Removal mechanisms Influencing factors

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Reinforcement Learning-Based Fault-Tolerant control of Uncertain Strict-Feedback Nonlinear systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation industrial control

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LF-3PM: a LiDAR-based Framework for Perception-aware Planning with Perturbation-induced Metric

LF-3PM: a LiDAR-based Framework for Perception-aware Plannin...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Kaixin Chai Long Xu Qianhao Wang Chao Xu Peng Yin Fei Gao City University of Hong Kong Hong Kong China Huzhou Institute of Zhejiang University Huzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Just as humans can become disoriented in featureless deserts or thick fogs, not all environments are conducive to the Localization Accuracy and Stability (LAS) of autonomous robots. This paper introduces an efficient framework designed to enhance LiDAR-based LAS through strategic trajectory generation, known as Perception-aware Planning. Unlike vision-based frameworks, the LiDAR-based requires different considerations due to unique sensor attributes. Our approach focuses on two main aspects: firstly, assessing the impact of LiDAR observations on LAS. We introduce a perturbation-induced metric to provide a comprehensive and reliable evaluation of LiDAR observations. Secondly, we aim to improve motion planning efficiency. By creating a Static Observation Loss Map (SOLM) as an intermediary, we logically separate the time-intensive evaluation and motion planning phases, significantly boosting the planning process. In the experimental section, we demonstrate the effectiveness of the proposed metrics across various scenes and the feature of trajectories guided by different metrics. Ultimately, our framework is tested in a real-world scenario, enabling the robot to actively choose topologies and orientations preferable for localization. The source code is accessible at https://***/ZJU-FAST-Lab/LF-3PM.

关键词： Measurement Location awareness Learning systems Laser radar Source coding Robot sensing systems Planning Trajectory Topology Reliability

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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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