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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Fang, Mei Wang, Liqing Wu, Zheng-Guang College of Automation Harbin Engineering University Harbin150001 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China

ISBN: (纸本)9781665424233

This paper deals with h -gain controller synthesis of positive Markov jump systems (PMJSs) with asynchronous modes. Thanks to the hidden Markov model, the closed-loop systems are modeled as hidden Markov jump systems (HMJSs). The definitions of positivity, mean stability, and h -gain are introduced for HMJSs. A necessary and sufficient condition is derived to ensure that the HMJSs are positive and mean stable with h -gain γ that can be solvable by linear programming strategy. Two numerical examples are listed to show the effectiveness of our results. © 2021 IEEE.

关键词： Hidden Markov models

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Research on Short-term Wind Speed Prediction Based on Adaptive Hybrid Neural Network with Error Correction

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第4期50卷 1290页

作者： Long, Hongyu He, Yunlong Xiang, Wei Guan, Zhenqi Tan, Hao Yu, Jianbo Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China

As a clean and efficient renewable energy, wind power is increasingly valued by countries all over the world. However, due to the inherent volatility, intermittency, and uncontrollability of wind power, the secure, stable, and economical operation of the power grid will face unprecedented pressure with wind power integration on a vast scale. Wind power predictions and management are significantly impacted by wind speed. Therefore, it is crucial to propose an accurate, stable, and efficient wind speed prediction method. In this paper, the original wind speed data is denoised using singular spectrum analysis to solve the problem of strong randomness and volatility of wind speed data. Then, the denoised data is processed using wavelet analysis, and the high-frequency and low-frequency sequences are respectively predicted using CNN-BiLSTM and CNN-BP, while the weights are optimized using the PSO algorithm. Finally, the prediction accuracy is further improved by incorporating the error correction module. In the two datasets used for testing, the outcomes of the trial indicate that the wind speed prediction model proposed in this paper performs extremely well in short-term wind speed prediction, superior to other models. The five evaluation indices (MAE, MSE, MAPE, RMSE, and R2) for the first dataset are respectively 0.166, 0.055, 0.05, 0.234, and 0.953. Similar results were obtained for the second dataset. Furthermore, The ablation tests also show that each module suggested in this research is successful in enhancing prediction accuracy. © 2023, International Association of Engineers. All rights reserved.

关键词： Error correction

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Acp-Net: Asymmetric Center Positioning Network for Real-Time Text Detection

SSRN

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SSRN 2024年

作者： Zhu, Boyuan Liu, Fagui Chen, Xi Tang, Quan Chen, C.L. Philip South China University of Technology No.342 Outer Ring East Road Panyu Guangzhou510006 China Peng Cheng Laboratory No. 2 Xingke 1st Street Nanshan Shenzhen518055 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No.95 Zhongguancun East Road Haidian Beijing100190 China

Scene text detection plays a crucial role in numerous application fields. However, despite the varying focus on real-time performance, almost all existing detection models employ the Feature Pyramid Network (FPN) structure for feature extraction, overlooking its inherent limitations. The integration of high-resolution multi-channel features within FPN demands significant computational resources. Although FPN which treats local and global features as equivalent, has been proven to be stable in various fields, it remains to be reconsidered whether this stability strategy is the most suitable for text features. To this end, we propose an Asymmetric Center Positioning Network (ACP-Net), which replaces the FPN structure and achieves precise and real-time text detection in complex scenarios. Specifically, we explore an asymmetric feature structure that includes two independent branches to acquire both global and local information, as well as an adaptive weighted fusion module to effectively capture the long-range dependencies from these two branches. Furthermore, we present a text center positioning module to enhance the understanding of text features by learning the feature centers. Comprehensive assessments conducted across various terminals have demonstrated the superiority of ACP-Net in terms of both accuracy and speed. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

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Science China(Information Sciences) 2021年第7期64卷 132-147页

作者： Lele XI Zhihong PENG Lei JIAO Ben M.CHEN School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System Peng Cheng Laboratory Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view of the onboard sensor in cluttered environments. Compared to existing methods, safe flying zone,vehicle physical limits, and smoothness are fully considered to guarantee flight safety, kinodynamic feasibility, and tracking performance. To tackle the cluttered environments, a parallel particle swarm optimization algorithm is applied to find the feasible waypoints that the generated trajectory should be as close to as possible, with consideration of the target's future state as well as obstacles to trade off the tracking performance and flight safety. Then, a sequential motion planning method, considering the above constraints, is applied and embedded into a cost function for solving the problem of robust tracking trajectory generation for the quadrotor via a convex optimization approach. The feasibility and effectiveness of the proposed method are verified by numerical simulations.

关键词： quadrotor target tracking trajectory generation B-spline convex optimization

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Hydrodynamic Modeling and Parameter Identification of a Bionic Underwater Vehicle:RobDact

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Cyborg and Bionic systems 2022年第1期2022卷 164-176页

作者： Qiyuan Cao Rui Wang Tiandong Zhang Yu Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of A utomationChinese Academy of SciencesBeijingChina School of Artific ial Itelligence University of Chinese Academy of Sciences BeijingChina Center for Excellence in Brain Science and Itelligence Technology Chinese Academy of SciencesShanghaiChina

In this paper,the hydrodynamic modeling and parameter identifcation of the RobDact,a bionic underwater vehide inspired byDactylopteridae,are carried out based on computational fluid dynamics(CFD)and force measurement ***,thePaper briely describes the RobDact,then establishes the kinematis model and rigid body dynamics model of the RobDactaccording to the hydrodynamic force and moment *** CFD simulations,the hydrodynamic force of theRobDact at diferent speeds is obtained,and then,the hydrodynamic model parameters are ***,themeasurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the inputfluctuation parameters,Finally,combining the rigid body dynamics model and the fin'thrust mapping model,thehydrodynamic model of the RobDact at diferent motion states is constructed.

关键词： dynamics(CFD) equations hydrodynamic

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PROTOTYPICAL CONTEXT-AWARE DYNAMICS GENERALIZATION FOR HIGH-DIMENSIONAL MODEL-BASED REINFORCEMENT LEARNING

arXiv

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arXiv 2022年

作者： Wang, Junjie Mu, Yao Li, Dong Zhang, Qichao Zhao, Dongbin Zhuang, Yuzheng Luo, Ping Wang, Bin Hao, Jianye The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Hong Kong Hong Kong Huawei Noah's Ark Lab

The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging. Although the recurrent structure utilized in current advances helps to capture local dynamics, modeling only state transitions without an explicit understanding of environmental context limits the generalization ability of the dynamics model. To address this issue, we propose a Prototypical Context-Aware Dynamics (ProtoCAD) model, which captures the local dynamics by time consistent latent context and enables dynamics generalization in high-dimensional control tasks. ProtoCAD extracts useful contextual information with the help of the prototypes clustered over batch and benefits model-based RL in two folds: 1) It utilizes a temporally consistent prototypical regularizer that encourages the prototype assignments produced for different time parts of the same latent trajectory to be temporally consistent instead of comparing the features;2) A context representation is designed which combines both the projection embedding of latent states and aggregated prototypes and can significantly improve the dynamics generalization ability. Extensive experiments show that ProtoCAD surpasses existing methods in terms of dynamics generalization. Compared with the recurrent-based model RSSM, ProtoCAD delivers 13.2% and 26.7% better mean and median performance across all dynamics generalization tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamics

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A Stacking Ensemble Learning Method for Non-Intrusive Appliance Load Monitoring

A Stacking Ensemble Learning Method for Non-Intrusive Applia...

引用

Chinese control and Decision Conference, CCDC

作者： Xudong Cheng Ding Li Wenkai Hu Jinghao Xu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Non-Intrusive Load Monitoring (NILM) collects the overall power consumption information from the power panel with advanced metering infrastructure, and obtains the operating states and power consumption information of each electrical appliance through load decomposition algorithms, so as to come up with appropriate strategies to reduce energy consumption and improve energy efficiency, which is of great significance for energy conservation and reduction. In recent years, NILM methods based on deep learning algorithms have received much attention and achieved good results, but the type of features extracted by a single deep learning model is limited, resulting in the model’s inability to overcome the bottleneck of accurate decomposition. To address the above problems, this paper proposes a deep ensemble learning framework for NILM, which takes the total active power sequence as input and synthesizes the advantages of multiple deep learning neural network models to further improve the performance of appliance identification and power disaggregation. Experiments are designed and conducted on the UK-DALE dataset to verify the effectiveness of the proposed deep ensemble learning method for NILM. The experimental results show that the method proposed in this paper has more advantages and is more promising in terms of various metrics compared to single deep learning approaches.

关键词： Deep learning Training Load monitoring Power demand Accuracy Stacking Neural networks

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OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification 5th

OSTER: An Orientation Sensitive Scene Text Recognizer with C...

引用

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Feng, Zipeng Du, Chen Wang, Yanna Xiao, Baihua The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030414030

Scene texts in China are always arbitrarily arranged in two forms: horizontally and vertically. These two forms of texts exhibit distinctive features, making it difficult to recognize them simultaneously. Besides, recognizing irregular scene texts is still a challenging task due to their various shapes and distorted patterns. In this paper, we propose an orientation sensitive network aiming at distinguishing between Chinese horizontal and vertical texts. The learned orientation is then passed into an attention selective network to adjust the attention maps of the sequence recognition model, leading it working for each type of texts respectively. In addition, a lightweight centerline rectification network is adopted, which enables the irregular texts more readable while no redundant labels are needed. A synthetic dataset named SCTD is released to support our training and evaluate the proposed model. Extensive experiments show that the proposed method is capable of recognizing arbitrarily-aligned scene texts accurately and efficiently, achieving state-of-the-art performance over a number of public datasets. © Springer Nature Switzerland AG 2020.

关键词： Character recognition

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Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

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Chinese Journal of Aeronautics 2021年第10期34卷 166-176页

作者： Hao ZHANG Zongxia JIAO Yaoxing SHANG Xiaochao LIU Pengyuan QI Shuai WU School of Automation Science and Electrical Engineering Bcihcmg UniversityBeijing 100083China Research Institute for Frontier Science Beihang UniversityBeijing 1000H3China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Key Laboratory of Advanced Aircraft Systems(Beihang University) Ministry of Industry and Information TechnologyBeijing 100083China Ningbo Institute of Technology Beihang UniversityNingbo 315800China The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University.Hangzhou 310027China

The maneuvering time on the ground accounts for 10%–30%of their flight time,and it always exceeds 50%for short-haul aircraft when the ground traffic is *** also contribute significantly to emissions,fuel burn,and noise when taxiing on the ground at *** is an urgent need to reduce aircraft taxiing time on the ***,it is too expensive for airports and aircraft carriers to build and maintain more runways,and it is space-limited to tow the aircraft fast using *** drive capability is currently the best solution for aircraft,which can save the maneuver time for *** idea is proposed that the wheels are driven by APU-powered(auxiliary power unit)motors,APU is working on its efficient point;consequently,the emissions,fuel burn,and noise will be reduced *** Front-wheel drive aircraft,the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a *** traction effects the aircraft’s maximum turning ability,which is difficult to be modeled to guide the controller *** reinforcement learning provides a powerful tool to help us design controllers for black-box models;however,the models of related works are always simplified,fixed,or not easily modified,but that is what we care about *** with complex models can the trained controller be ***-fidelity models that can easily modified are necessary for aircraft ground maneuver controller *** paper focuses on the maneuvering problem of front-wheel drive aircraft,a high-fidelity aircraft taxiing dynamic model is established,including the 6-DOF airframe,landing gears,and nonlinear tire force model.A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive *** is proved that in some conditions,the DRL based controller outperformed conventional look-ahead controllers.

关键词： Front-wheel drive aircraft Ground maneuver Nose wheel steering Tire model Magic equation Deep reinforcement learning

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Beyond efficiency: molecular data pruning for enhanced generalization 24

Beyond efficiency: molecular data pruning for enhanced gener...

引用

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Dingshuo Chen Zhixun Li Yuyan Ni Guibin Zhang Ding Wang Qiang Liu Shu Wu Jeffrey XuYu Liang Wang New Laboratory of Pattern Recognition State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences Tongji University

ISBN: (纸本)9798331314385

With the emergence of various molecular tasks and massive datasets, how to perform efficient training has become an urgent yet under-explored issue in the area. Data pruning (DP), as an oft-stated approach to saving training burdens, filters out less influential samples to form a coreset for training. However, the increasing reliance on pretrained models for molecular tasks renders traditional in-domain DP methods incompatible. Therefore, we propose a Molecular data Pruning framework for enhanced Generalization (MolPeg), which focuses on the source-free data pruning scenario, where data pruning is applied with pretrained models. By maintaining two models with different updating paces during training, we introduce a novel scoring function to measure the informativeness of samples based on the loss discrepancy. As a plug-and-play framework, MolPeg realizes the perception of both source and target domain and consistently outperforms existing DP methods across four downstream tasks. Remarkably, it can surpass the performance obtained from full-dataset training, even when pruning up to 60-70% of the data on HIV and PCBA dataset. Our work suggests that the discovery of effective data-pruning metrics could provide a viable path to both enhanced efficiency and superior generalization in transfer learning.

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