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检索条件"机构=Tianjin Key Laboratory for Control Theory and Application in Complicated Systems"

共 823 条记录，以下是71-80 订阅

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Multi-class MI-EEG Classification: Using FBCSP and Ensemble Learning Based on Majority Voting

Multi-class MI-EEG Classification: Using FBCSP and Ensemble ...

引用

2021 China Automation Congress, CAC 2021

作者： Wei, Xiaoying Dong, Enzeng Zhu, Lin Tianjin Key Laboratory For Control Theory & Application Complicated Systems Tianjin University of Technology Tianjin China China North Industries Group 210 Reseasrch Institute Beijing China

ISBN: (纸本)9781665426473

How to decode multi-class motor imagery EEG and decipher an individual's intents more accurately has always been a problem worthy of study in biomedical field. In the process of recognition and classification of 2-class motor imagery EEG, the Common Spatial Pattern (CSP) algorithm detects ERD/ERS by calculating the spatial filter and achieves great success. In this paper, we use self-frequency Segmented Filter Bank Common Spatial Patterns (FBCSP) with the one versus one (OVO) multi class extension on the classification of 4-class MI-EEG (BCI Competition IV Datasets 2a), and the Majority Voting strategy is adopted for the individual classifiers we select. We finally get a considerable classification result and the comparison of different methods shows that the performance of ensemble learning based on voting strategy is better than that of using individual classifier. © 2021 IEEE

关键词： Classification (of information)

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Distributed-Observer-Based on Longitudinal Flight control for Hypersonic Vehicle

Distributed-Observer-Based on Longitudinal Flight Control fo...

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Chinese control Conference (CCC)

作者： Yuehui Ji Yiming Zhao Jiaqi Chen Junjie Liu Qiang Gao Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

An output feedback control is investigated to achieve stable tracking of velocity and altitude in hypersonic flight vehicle in the paper. A distributed observer is proposed to estimate the unmeasurable flight path angel and angle of attack through a strongly connected directed network topology. Then, a nonlinear control is addressed on the longitudinal dynamics of a rigid hypersonic flight vehicle (HFV) with partial measurable states and the estimated states. The system dynamic is divided into the altitude subsystem and the velocity subsystem. In the altitude subsystem, the backstepping design is proposed for constructing the controllers utilizing the estimated states. The nonlinear dynamic inversion controller is introduced to accomplish the velocity tracking. Finally, numerical simulations verify the effectiveness of the proposed control scheme in providing favorable tracking performance and exact state estimation ability.

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FATUnetr:fully attention Transformer for 3D medical image segmentation

FATUnetr:fully attention Transformer for 3D medical image se...

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IEEE International Conference on Mechatronics and Automation

作者： QingFeng Li Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China Department of Electrical Engineering Tshwane University of Technology South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Recent studies have explored the applicability of the Transformer model in 3D medical segmentation tasks. The core attention mechanism of transformer, has the property of capturing remote dependencies, which is highly robust when segmenting medical images. Inspired by the emerging visual groupings in Vision Transformers (ViTs), self-attention can be made more robust by improving the intermediate representation. We propose a novel framework called FATUnetr that aims to take advantage of the full attention module to improve accuracy. We also reduce the number of parameters by applying deeply separable convolution at the bottleneck layer. We have verified the effectiveness in the extracranial vault multiple atlas labelling (BTCV) dataset for multi-organ segmentation.

关键词： Image segmentation Visualization Three-dimensional displays Attention mechanisms Accuracy Convolution Transformer cores

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GIB-RRT:a gamma interpolation bidirectional RRT algorithm

GIB-RRT:a gamma interpolation bidirectional RRT algorithm

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第35届中国控制与决策会议

作者： Jianyun Ni Hao Li Tie Wang Helei Du Jie Wu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electronic Engineering and Automation Tianjin University of Technology Tianjin Climate Center

In a complex environment,when B-RRT and RRT algorithms are used for path planning,there will be problems such as long planned paths,large number of iterations,low sampling efficiency and long search *** solve these problems,this paper proposes a gamma interpolation bidirectional RRT algorithm——***,the algorithm uses a bidirectional search strategy to expand two random trees simultaneously to speed up the convergence *** the expansion process,an adaptive goal biasing strategy is introduced to improve the sampling efficiency,and the probability of expansion to the respective target point is continuously changed according to the number of collision detection *** the initial path is obtained,a greedy pruning algorithm is used to simplify the path points and reduce the path *** optimisation method of Gamma interpolation is then devised for the simplified path and combined with cubic uniform B-spline curve to generate shorter and smoothly executable *** proposed algorithm is compared with B-RRT,IB-RRT and B-RRT in different complex environments in simulation experiment,and the results show that the proposed algorithm has better search efficiency and is able to obtain optimal path in the least time and with the most stable efficiency.

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PFSC:Pyramid R-CNN for Point-Voxels with Focal Sparse Convolutional Networks for 3D Object Detection

PFSC:Pyramid R-CNN for Point-Voxels with Focal Sparse Convol...

引用

IEEE International Conference on Mechatronics and Automation

作者： Zhao Su Xuehui Liang Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In the point cloud 3D object detection task, the point cloud data is unevenly distributed in 3D space. Therefore, whether it is regular sparse convolution or submanifold sparse convolution, there is a significant difference in the detection performance when dealing with objects at different positions using sparse convolutional networks. Therefore, this paper proposes an improvement using focal sparse convolution to break the limitations of traditional convolution methods and enhance the representation ability of sparse convolution. Focal sparse convolution is utilized to replace the currently better-performing two-stage object detection network based on the combination of points and voxels, enhancing the end-to-end training effectiveness and achieving better detection results. And an optional detachable auxiliary network is added, which, during the training stage, combines point features to guide the backbone network in learning the structural information of the 3D point cloud, enhancing the localization accuracy of the backbone network. Extensive experiments have been conducted on the KITTI dataset to validate the proposed method, yielding promising detection results and demonstrating the effectiveness of the approach.

关键词： Point cloud compression Training Location awareness Three-dimensional displays Accuracy Mechatronics Convolution

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Linear active disturbance rejection control of phase-shifted full-bridge converter based on improved particle swarm optimization algorithm

Linear active disturbance rejection control of phase-shifted...

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IEEE International Conference on Mechatronics and Automation

作者： HongSen Wang YanJuan Wu Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Tianjin China

In order to improve the dynamic performance and disturbance suppression ability of conventional PID control phase shift full-bridge converter, LADRC based on improved particle swarm optimization is proposed. The voltage loop transfer function of phase-shifted full-bridge converter is derived based on the state space average method. Secondly, LESO and LSEF are designed to estimate and compensate the external disturbance and internal uncertainty in real time. Finally, the advantages of linear active disturbance rejection controller are verified by simulation experiments. The experimental results show that the controller has better disturbance suppression performance and dynamic performance than the traditional PID controller.

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Real-time scheduling strategy for electric vehicles based on sliding window transmission optimization

Real-time scheduling strategy for electric vehicles based on...

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IEEE International Conference on Mechatronics and Automation

作者： Peizhi Cong Yanjuan Wu Caiwei Wang Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Tianjin China

In order to reduce the impact of electric vehicle(EV) charging load on the power grid, a real-time optimal scheduling scheme for charging and discharging Evs is proposed. Firstly, the objective is to reduce charging costs and network loss costs, aiming to minimize the total cost of EVs at the current time. An EV dispatching model is established, the grid node acceptance capacity performance is analyzed by constructing a network loss sensitivity index, and the EV charging and discharging strategy is obtained in real time based on the time-of-use tariff of the grid load by means of a tide calculation and a sliding window variable speed optimization algorithm as well as a second-order convex optimization calculation method. Finally, the proposed strategy is verified to be effective in reducing the charging cost and network loss cost, and has good real-time flexible dispatching performance, using an improved IEEE 39-node distribution network as an example.

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Named Entity Recognition in Chinese Medical Texts Based on RoBERTa-WWM-IDCNN-CRF

Named Entity Recognition in Chinese Medical Texts Based on R...

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System Simulation Technology and its application (CCSSTA), IEEE China Conference on

作者： Lan Tang Jie Kong Liang Xu School of Computer Xi‘an Shiyou University Xi‘an China Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Electronic medical records and doctor-patient conversations contain a wealth of useful information, such as disease symptoms, drug names, and cure cycles. Traditional deep learning approaches utilize bidirectional recurrent neural networks to encode text, which cannot effectively capture long-distance dependencies in text. In order to identify medical entity types in various texts more accurately, a named entity recognition approach based on RoBERTa-WWM-IDCNN-CRF is proposed. This approach introduces the RoBERTa-WWM pre-trained model, in order to improve the overall understanding of long Chinese texts for the first time. The model generates semantic representations containing prior knowledge. Then, the generated semantic representations are feed to the IDCNN to extract sentence level characteristics. Finally, the global optimal solution is obtained and the results of the named entity are produced through the CRF. The experiment indicates that the improvement of $F_{1}$ score, accuracy, and recall of RoBERTa-WWM-IDCNN-CRF model are 7.94%, 4.52% and 10.02% comparing with the classical BiLSTM-CRF model.

关键词： Drugs Recurrent neural networks Semantics Named entity recognition Oral communication Medical services systems simulation Feeds Electronic medical records Diseases

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Based on Hybrid Densenet-121 with Support Vector Machine Algorithm for Lettuce and Chili

Based on Hybrid Densenet-121 with Support Vector Machine Alg...

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IEEE International Conference on Mechatronics and Automation

作者： Yuzhe He Yanjuan Wu Caiwei Wang Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Tianjin China

In this paper, to improve the recognition accuracy of Densenet-121, we combine Densenet-121 with SVM to generate a new network Densenet-121-SVM. In this paper, the two datasets are combined to generate a new dataset named Mixed-dataset, which has four plants, namely: Lettuce, Chili, Chinee apple, and Lantana, and this dataset is used to train the network. Finally, the recognition accuracies of SVM, Densenet121 and Densenet-121-SVM are 84.144%, 96.211% and 98.181%, respectively, and it can be seen that the accuracy of Densenet-121-SVM is improved by 14% and 1.97% compared to SVM and Densenet-121, respectively.

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Uncertainty Treatment of Integrated Energy System Based on Scenario Reduction Method

Uncertainty Treatment of Integrated Energy System Based on S...

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IEEE International Conference on Mechatronics and Automation

作者： Caiwei Wang Yanjuan Wu Peizhi Cong Yuzhe He Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin University of Technology Binshui Xidao 391 Tianjin China

In the context of high permeability of renewable energy, a large number of uncertain energy sources, such as scenery, have brought a huge impact on the security and stability of the comprehensive energy system. In order to reduce the impact of renewable energy on the new power system and improve the system stability, In this paper, a comprehensive energy system of the park is set up, which consists of three kinds of energy loads including source side electricity, heat, gas and scenery, and electric heat. On this basis, Monte Carlo method is adopted to simulate multiple sets of scenes respectively for the scenery output. On this basis, scene reduction method is adopted to reduce them to obtain the final equivalent force diagram, so as to reduce the influence of the uncertainty of scenery on the system stability. Finally, the effectiveness of the proposed method is verified by simulation analysis.

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