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第43届中国控制会议

作者： Chong Cao Gang Peng Dingxin He Yihao Cao Yue Xu Zhang Deng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan Lisai Technology Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Due to the limitation of the vertical angle resolution of the lidar sensor,the observation information in the vertical direction is reduced,resulting in the elevation cumulative drift phenomenon of many lidar SLAM algorithms in large-scale environment,which reduces the accuracy of pose estimation and the quality of *** order to solve this problem,based on LIO-SAM,this paper proposes a lidar SLAM algorithm with ground plane *** method can effectively extract the ground point cloud and obtain the plane normal vector,and construct the constraint relationship with the prior plane normal *** optimal state estimation is solved by nonlinear optimization method,which effectively reduces the elevation drift caused by the long-term operation of the system in large-scale *** paper compares the improved algorithm with A-LOAM,LeGO-LOAM and original LIO-SAM algorithms in challenging datasets such as KITTI,M2 DGR and *** results show that this method effectively improves the accuracy of pose estimation and the quality of mapping,and limit the elevation drift of mobile robot in ground operation,which has high application value.

关键词： Ground plane constraint elevation drift LIDAR simultaneous localization and mapping(SLAM)

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Lithium-ion battery state-of-charge estimation using a real-time moving horizon estimation algorithm

Lithium-ion battery state-of-charge estimation using a real-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Jiayu Yan Song Li Yiming Wan Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China the 29th Research Institute of China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9781665401166

For battery management systems, it is significant to reliably estimate state-of-charge (SOC) from limited measurements in real time. Based on a nonlinear SOC-dependent equivalent circuit model, we propose a real-time moving horizon estimation (MHE) algorithm for SOC estimation. For efficient computation, the sequential quadratic programming strategy is applied, and each quadratic programming (QP) iteration is solved via the structure-exploiting forward and backward recursions. To account for the limited computation time allowed within each sampling interval, the proposed algorithm does not fully solve the receding horizon optimization problem over each sliding window, but performs fixed number of QP iterations. The simulation results show that compared to the existing fully solved MHE applied to SOC estimation, the proposed real-time MHE algorithm significantly saves computation time at the cost of a slight loss of estimation performance.

关键词： Computational modeling Simulation Estimation Real-time systems Time measurement Computational efficiency State of charge

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Modeling collective behavior of posting microblog by stochastic differential equation with jump

arXiv

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arXiv 2017年

作者： Pan, Jun-Shan Li, Yuan-Qi Liu, Xiang Hu, Han-Ping Hu, Yong School of Automation and School of Physics Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China

The characterization and understanding of online social network behavior is of importance from both the points of view of fundamental research and realistic utilization. In this manuscript, we propose a stochastic differential equation to describe the online microblogging behavior. Our analysis is based on the microblog data collected from Sina Weibo which is one of the most popular microblogging platforms in China. Especially, we focus on the collective nature of the microblogging behavior reflecting itself in the analyzed data as the characters of the periodic pattern, the stochastic fluctuation around the baseline, and the extraordinary jumps. Compared with existing works, we use in our model time dependent parameters to facilitate the periodic feature of the microblogging behavior and incorporate a compound Poisson process to describe the extraordinary spikes in the Sina Weibo volume. Such distinct merits lead to significant improvement in the prediction performance, thus justifying the validity of our model. This work may offer potential application in the future detection of the anomalous behavior in online social network platforms. Copyright © 2017, The Authors. All rights reserved.

关键词： Differential equations

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DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers

DAFormer: Depth-aware 3D Object Detection Guided by Camera M...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Junbin Gao Hao Ruan Bingrong Xu Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Automation Wuhan University of Technology Wuhan China

3D object detection plays a critical role in autonomous driving perception. Although multi-view-based perception solutions have made significant progress, their performance is still far from being ready for practical use. The estimation of pixel depth is dependent on camera intrinsic properties, which led us to explore a depth-aware model guided by camera parameters. Our contribution in this paper is the DAFormer, which incorporates camera parameters and position-aware image features to detect 3D objects. The depthaware module uses camera parameters to reweight image features and estimate depth, enhancing and offsetting the 3D position embedding. The object query uses depth information enhanced features for end-to-end 3D detection by Attention layers. DAFormer achieves impressive results on the standard nuScenes dataset without any additional embellishments.

关键词： Solid modeling Three-dimensional displays Estimation Object detection Detectors Cameras Feature extraction

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Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)

arXiv

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arXiv 2020年

作者： Liu, Ziang Jiang, Xue Luo, Hanbin Fang, Weili Liu, Jiajing Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Civil Engineering and Mechanics Huazhong University of Science and Technology China

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 12 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Analysis of Government-Taxation Methods and Maritime Companies’ Strategies Under Emission-Reduction Incentives

SSRN

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SSRN 2023年

作者： Wang, Jian Wenbo, Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

The current emission-reduction trend in the shipping industry is inevitable. This study uses a multi-stage Stackelberg game to establish a low-carbon maritime supply chain model that includes a government, a carrier, and a port. The government can choose to impose carbon taxes only on the port or on both the port and carrier, while there are different power structures: port-led or carrier-led. Equilibrium solutions in different scenarios indicate that the leading port’s profit is not affected by the implementation of a carbon tax, while the carrier, surprisingly, hopes to join the carbon-tax policy. Both the government and the carrier prefer a bilateral carbon-tax scenario led by the carrier, while the port has the opposite preference. In the process of seeking coordination opportunities, we find that, driven by shippers’ low-carbon preferences, the maritime supply chain exhibits a "triple increasing" phenomenon, which means higher cargo volume, more emission reduction, and higher freight prices. The government and carrier can control the occurrence of this phenomenon and promote an all-win scenario through the use of carbon taxes and subsidies. We provide coordination strategies for unilateral carbon taxes in the maritime supply chain under different power structures and demonstrate the important role of the government in coordination. This provides a new direction for the implementation of carbon taxes in the maritime supply chain. © 2023, The Authors. All rights reserved.

关键词： Taxation

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Multi-view fuzzy logic system with the cooperation between visible and hidden views

arXiv

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arXiv 2018年

作者： Zhang, Te Deng, Zhaohong Wu, Dongrui Wang, Shitong School of Digital Media Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multi-view dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multi-view learning algorithm can achieve better performance by cooperative learning on the multi-view data. However, existing multi-view approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multi-view data, or vice versa. To address this problem, this paper proposes a multi-view fuzzy logic system, which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Adaptive control for power and thermal management of an integrated 5kW solid oxide fuel cell system

Adaptive control for power and thermal management of an inte...

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第十五届全国氢能会议暨第7届两岸三地氢能研讨会

作者： Lin Zhang Jianhua Jiang Xi Li Huan Cheng Jie Yang Jian Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan430074China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould TechnologyHuazhong University of Science and TechnologyWuhan 430074China

A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a... 详细信息

关键词： Solid oxide fuel cell (SOFC) Optimal set-points Load transitions RBF network based direct adaptive controller

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Distributed control of heterogeneous linear multi-agent systems by intermittent event-triggered control

Distributed control of heterogeneous linear multi-agent syst...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yan Lei Yan-Wu Wang Hua Chen Xiao-Kang Liu Huazhong University of Science and Technology Wuhan Hubei CN Mathematics and Physics Department Hohai Universtiy Changzhou China Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

ISBN: (纸本)9781538629024

In this paper, the leader-following output consensus problem of heterogeneous linear systems(MASs) is addressed. A distributed intermittent event-triggered protocol is designed, where the information interaction among agents only occurs under a local triggering condition within some certain time intervals. As a result, it saves the energy for communication and reduces the number of control updates. In aid of the Lyapunov functional method, some sufficient conditions are derived to achieve the leader-following consensus and the result is further extended to the formation control problem. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.

关键词： Multi-agent systems Linear systems Laplace equations Eigenvalues and eigenfunctions Riccati equations Decentralized control Automation

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Short-Term Residential Load Forecasting Via Pooling-Ensemble Model with Smoothing Clustering

SSRN

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SSRN 2023年

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this paper proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole shortterm load forecasting framework in this paper contains three steps. Specifically and firstly, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Secondly, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Thirdly, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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