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第28届中国控制与决策会议

作者： Pu Wang Cuixia Wu Xuejin Gao College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

In view of the subway passenger flow's random problem,nonlinear problem and so on,in order to predict the subway passenger volume more accurately,this paper designs a kind of parallel variable coefficient weighted combination prediction model based on Radial Basis Function Neural Network and Least Squares Support Vector *** this method,firstly,the original data is ***,this paper respectively sets up RBF Neural Network and LSSVM prediction model for training and calculating the weighting coefficient with the results of the *** this paper separately does two kinds of models' prediction,and weights to get *** article uses 2012 passenger flow data of Beijing DONGZHIMEN Station for experiments,which shows that the result of combination prediction model is more accurate than the result of single prediction model.

关键词： Subway passenger flow Combination prediction Variable coefficient RBF neural network LSSVM

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A federated filtering personal navigation algorithm based on MEMS-INS/GPS integrated

A federated filtering personal navigation algorithm based on...

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第28届中国控制与决策会议

作者： Kai Pan Mingrong Ren Pu Wang Yanhong Liu College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Designed a zero velocity detection algorithm based on adaptive threshold in order to accurately detect the zero velocity moment,and through the kalman zero velocity correction algorithm timely correct the error of inertial navigation system(INS).Because at zero velocity correction time position and heading information is observed,by unscented kalman filter(UKF),INS and GPS are integrated navigation,and compared with extended kalman filter(EKF).Through the experiment under the condition of strong nonlinear,UKF has better filtering performance than EKF,and also better than the EKF on *** kalman zero velocity correction and UKF building federal filtering model can effectively inhibit the INS position drift *** the same time,in straight line phase,the use of GPS location information correct course *** GPS signal loss within a certain time,the integrated navigation system can get the right course ***,the positioning accuracy of the integrated navigation system is obviously improved.

关键词： Zero Velocity Correction EKF UKF Federated Filtering

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Emission Evaluation for Diesel Vehicles under Typical Operating Conditions 8

Emission Evaluation for Diesel Vehicles under Typical Operat...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiaren Xu, Zhenyi Cao, Yang Xia, Xiushan Kang, Yu Anhui University AHU-IAI AI Joint Laboratory Hefei230601 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China

ISBN: (纸本)9798350300178

China's ambient air quality has improved significantly as a result of the formulation and implementation of the Action Plan for the Prevention and control of Air Pollution and the Three-Year Action Plan for Winning the Blue Sky Defense War, but the air pollution problem in key regions remains severe, diesel truck pollution has not been effectively addressed, and mobile sources are an important source of nitrogen oxide emissions. The existing diesel vehicle emission model is primarily divided based on average vehicle speed and VSP binning methods, and it lacks an instantaneous vehicle speed model. This paper collected data from 10 diesel vehicles in order to accurately evaluate driving conditions and promote energy saving and emission reduction of heavy-duty diesel vehicles. Then we extracted 51,925 kinematic fragment data after cleaning abnormal data and built a kinematic feature evaluation system of diesel vehicles closely related to NOx emission. The sample data of kinematic segments were clustered into two groups of low-speed conditions and medium-speed conditions using the standardized mutual information to extract the characteristics with strong correlation with NOx and the vehicle driving conditions assessment model of cluster analysis. From the clustering results, the high NOx emission is completely classified into the low-speed working condition, which matches the conclusion that the high NOx emission region appears in the low-speed acceleration region reached by previous researchers through experimental studies, indicating that the kinematic feature evaluation model of diesel vehicles established in this paper is reasonable and effective. © 2023 IEEE.

关键词： Cluster analysis

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Fan Fault Signal Noise Reduction Based on Wavelet Threshold Function

Fan Fault Signal Noise Reduction Based on Wavelet Threshold ...

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第27届中国过程控制会议（CPCC2016）

作者： WANG Pu LI Tianyao GAO Xuejin WEN Zheng College of Electronic and Control Engineering Beijing University of Technology Beijing Laboratory For Urban Mass Transit Engineering Research Center of Digital Community Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System

Fan fault signal acquisition is usually superimposed fault signal and noise signal in faultdiagnosis;it isnecessary to denoise the original *** purpose of this paper is to introduce a new wavelet threshold function asthe candidate of the current noise reduction threshold function standard for noise reduction of fan bearing *** disadvantage of hard threshold function is the discontinuity at the threshold would cause signal *** of soft threshold function is the large variance in result when wavelet coefficient is *** of this paper are to select appropriate parameters that would enable the threshold function to flexibly choosefrom hard and soft *** benefit of this new threshold function is the improvement in data validity *** advantage has been further proved by comparing the raw data of fan bearing false signal in differentmethods before and after noise reduction *** proposed method is better than aforementioned traditionalmethods.

关键词： Wavelet Fan Fault Signal Noise Reduction Based on Wavelet Threshold Function

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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Design of echo state network with multi-objective optimization algorithm and l1 regularization

Design of echo state network with multi-objective optimizati...

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Chinese control and Decision Conference, CCDC

作者： Zhanhong Wu Cuili Yang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing University of Technology Beijing

ISBN: (纸本)9781665478977

In this paper, a new approach for optimizing the structure and prediction error of echo state network (ESN) is proposed. ESN is a kind of recurrent neural network with simple training and strong generalization ability. Reservoir is an important structure of ESN, which determine network performance. Thus, multi-objective optimization algorithm is used to optimize network structure and training error simultaneously. Moreover, a local search algorithm based on l 1 regularization is used to accelerate convergence. The experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.

关键词： Training Recurrent neural networks Time series analysis Benchmark testing Prediction algorithms Reservoirs Classification algorithms

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Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn machines

Natural scene recognition based on Convolutional Neural Netw...

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IEEE International Conference on Mechatronics and Automation

作者： Jingyu Gao Jinfu Yang Jizhao Zhang Mingai Li Department of Control & Engineering Beijing University of Technology Beijing P.R. China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing P.R. China

ISBN: (纸本)9781479970995

Scene recognition is a significant topic in computer vision, and Deep Boltzmann machines (DBM) is a state-of-the-art deep learning model which has been widely applied in object and hand written digit recognition. However, when the DBM is used in scene recognition, it is difficult to handle large images due to its computational complexity. In this paper, we present a deep learning method based on Convolutional Neural Networks (CNN) and DBM for scene image recognition. First, in order to categorize large images, the CNN is utilized to preprocess images for dimensional reduction. Then, regarding the preprocessed images as the input of the visible layer, the DBM model is trained using Contrastive Divergence (CD) algorithm. Finally, after extracting features by the DBM, the softmax regression is employed to perform scene recognition tasks. Since the CNN can reduce effectively image size, the proposed method can improve the computational efficiency and becomes more suitable for large image recognition. Experimental evaluations using SIFT Flow dataset and fifteen-scene dataset demonstrate that the proposed method can obtain promising results.

关键词： Convolution Feature extraction Sensitivity Computational modeling Training Mathematical model Kernel

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Frequency Perception Network for Camouflaged Object Detection

arXiv

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arXiv 2023年

作者： Cong, Runmin Sun, Mengyao Zhang, Sanyi Zhou, Xiaofei Zhang, Wei Zhao, Yao School of Control Science and Engineering Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong University Jinan Shandong China School of Automation Hangzhou Dianzi University Zhejiang Hangzhou China Institute of Information Science Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However, the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively. The code will be released at https://***/rmcong/FPNet_ACMMM23. © 2023, CC BY-NC-SA.

关键词： Frequency domain analysis

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RDNet: A reinforced-deformable convolutional network for crowd counting

RDNet: A reinforced-deformable convolutional network for cro...

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IEEE International Conference on Robotics and Biomimetics

作者： Qingzhen Shang Yidi Zhuo Jiahui Zhang Jinfu Yang Control engineering from Beijing University of Technology Beijing China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665481106

Crowd counting is still a challenging task in crowded scenario due to heavy occlusions, appearance variations and perspective distortions. Thanks to the development of deep convolutional neural networks, especially the deformable convo-lution neurral networks, great progress has been made for crowd counting. However, existing deformable convolution networks cannot get satisfactory performance due to limited receptive field that is not conducive to crowded object detection. In this paper, a novel end-to-end Reinforced-Deformable convolutional neural Network (RDNet) is proposed. First, a multi-scale context information fusion strategy with skipping connection and shortcut connection is utilized to obtain rich semantic features and local details of the crowd distribution. Then a reinforced-deformable convolution module is designed as the back-end of the network, which can sample with adaptive offsets and scalars to enlarge the receptive field for more accurate edge information and crowd distribution localization. Finally, an impulse function convolved with cuphead Gaussian kernel is applied to generate density map for counting. Extensive experimental results on four popular datasets, namely, Shangha-itech, UCF _CC_50, WorldEXPO'10, and UCF-QNRF, show that our proposed approach outperforms other state-of-the-art methods.

关键词： Location awareness Convolution Sociology Semantics Object detection Convolutional neural networks Task analysis

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Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement

Self-supervised Multi-frame Monocular Depth Estimation with ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenhua Wu Guangming Wang Jiquan Zhong Hesheng Wang Zhe Liu MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Insititute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://***/IRMVLabIPLPE-Depth.

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