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Fused behavior recognition model based on attention mechanism

Visual computing for industry, biomedicine, and art 2020年第1期3卷 7页

作者： Lei Chen Rui Liu Dongsheng Zhou Xin Yang Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. liurui@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China.

With the rapid development of deep learning technology, behavior recognition based on video streams has made great progress in recent years. However, there are also some problems that must be solved: (1) In order to improve behavior recognition performance, the models have tended to become deeper, wider, and more complex. However, some new problems have been introduced also, such as that their real-time performance decreases; (2) Some actions in existing datasets are so similar that they are difficult to distinguish. To solve these problems, the ResNet34-3DRes18 model, which is a lightweight and efficient two-dimensional (2D) and three-dimensional (3D) fused model, is constructed in this study. The model used 2D convolutional neural network (2DCNN) to obtain the feature maps of input images and 3D convolutional neural network (3DCNN) to process the temporal relationships between frames, which made the model not only make use of 3DCNN's advantages on video temporal modeling but reduced model complexity. Compared with state-of-the-art models, this method has shown excellent performance at a faster speed. Furthermore, to distinguish between similar motions in the datasets, an attention gate mechanism is added, and a Res34-SE-IM-Net attention recognition model is constructed. The Res34-SE-IM-Net achieved 71.85%, 92.196%, and 36.5% top-1 accuracy (The predicting label obtained from model is the largest one in the output probability vector. If the label is the same as the target label of the motion, the classification is correct.) respectively on the test sets of the HMDB51, UCF101, and Something-Something v1 datasets.

关键词： Action recognition Attention mechanism Res34-SE-IM-net ResNet34-3DRes18

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Optimal Path Planning for Minimizing Base Disturbance of Space Robot

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International Journal of advanced Robotic Systems 2016年第2期13卷

作者： Xiao-Peng Wei Jian-Xia Zhang Dong-Sheng Zhou Qiang Zhang School of Mechanical Engineering Dalian University of Technology Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

The path planning of free-floating space robot in space on-orbit service has been paid more and more attention. The problem is more complicated because of the interaction between the space robot and base. Therefore, it is necessary to minimize the base position and attitude disturbance to improve the path planning of free-floating space robot, reducing the fuel consumption for the position and attitude maintenance. In this paper, a reasonable path planning method to solve the problem is presented, which is feasible and relatively simple. First, the kinematic model of 6 degrees of freedom free-floating space robot is established. And then the joint angles are parameterized using the 7th order polynomial sine functions. The fitness function is defined according to the position and attitude of minimizing base disturbance and constraints of space robot. Furthermore, an improved chaotic particle swarm optimization (ICPSO) is presented. The proposed algorithm is compared with the standard PSO and CPSO algorithm in the literature by the experimental simulation. The simulation results demonstrate that the proposed algorithm is more effective than the two other approaches, such as easy to find the optimal solution, and this method could provide a satisfactory path for the free-floating space robot.

关键词： ICPSO Path Planning Base Disturbance Space Robot On-orbit Service

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Cell-type composition analysis of scRNA-seq data with deep convolution neural network

Research Square

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Research Square 2020年

作者： Ma, He Fang, Zhihao Liu, Zongbin Chen, Yan College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China College of Engineering Peking University Beijing China Shenzhen Zigzag Biotechnology Ltd Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Background: With the rapid development of single-cell RNA sequencing (scRNA-seq), more large-scale single-cell sequencing data has been generated. Due to the continuous increase of single-cell sequencing data, the analysis of cell-type composition from single-cell transcriptomics has also to face huge challenges. Since the emergence of scRNA-seq technology, the size of sequencing datasets has grown more than 1 million times in just over a decade. Meanwhile, as more gene markers are discovered, the data dimension of single-cell sequencing becomes higher. All of these put forward more stringent requirements on data dimensionality reduction and clustering algorithms. Under the constraints of practical factors such as occurrence of noise and dropouts and the limitation of overhead, it is also required an effective and efficient method that can obtain accurate analysis results in a very short time, and has a competitive algorithm stability. Results: We present scCAE, an effective and efficient method based on convolution autoencoder that can accurately and rapidly analyze cell-type composition from single-cell transcriptomics datasets. Our method achieved the best results in the data sets that simulate the cell differentiation process among existing methods, which achieved the ARI of 69.64% and 68.83% at 10 and 25 clusters tasks. And, in the case of different dropouts, our method also works well. When the sparsity level of data metric is 71%, scCAE can achieved the ARI of 45.29%, which is the highest of the existing methods. In terms of algorithm overhead, our method has also achieved good results by comparing with several existing methods. It takes less time than most methods and takes up much less memory than other algorithms based neural networks. Conclusions: Our method, scCAE, has more accurate and reasonable results in the analysis of cell-types composition. And, because of the design of imputer, it can deal with a large number of dropouts in the data matrix. Becau

关键词： Convolution

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Deep high-order supervised hashing

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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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design and Evaluation of a Novel Self-Adaptive Ankle Rehabilitation Exoskeleton with Elastic Modules

Design and Evaluation of a Novel Self-Adaptive Ankle Rehabil...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： Meng Gao Junpeng Chen Mi Li Jian S. Dai Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen China Centre for Robotics Research King's College London London United Kingdom Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education Centre for Advanced Mechanisms and Robotics Tianjin University Tianjin China Chair Professor of Mechanisms and Robotics Centre for Robotics Research King's College London UK

This paper proposes a novel ankle rehabilitation exoskeleton based on the research of human gait cycle, aiming to assist patients with neurological injury to recover quickly. The mechanism consists of two frames equipped with ankle joints and toe joint. The lower limbs and musculature system are correlated to propose the model. Incorporating spring modules are provided on the instep, the bottom of toes and either sides of the ankles which cushions lower limbs and reduces energy consumption. Kinematics and stress analysis are conducted to verify the feasibility of the model. Besides, the simulation, which incorporates the elastic elements and springs, is presented. Finally, the modular facilitates could easy integration with other rehabilitation modules for a comprehensive system. Overall, this research provides theoretical and engineering technical support for developing lower limb rehabilitation robots with adaptive stability, diversified movement functions, and human-computer interaction safety and health under different conditions.

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A Tendon-Driven Actuator with Cantilever Initiated Variable Stiffness Used for Robotic Fingers

SSRN

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SSRN 2024年

作者： Li, Chenning Yang, Shangkui Branson, David T. Song, Zhibin Sun, Tao Dai, Jiansheng Kang, Rongjie Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin300072 China Advanced Manufacturing Technology Research Group Faculty of Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Beijing Institute of Technology China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Variable stiffness actuators (VSAs) have emerged as a key actuation technology known for their bionic performance and task adaptability. However, current VSAs often exhibit relatively large sizes, making them possible for use in robotic arms and legs but less convenient for integrations into robotic hands. This paper introduces a compact design of a tendon-driven variable stiffness actuator (TVSA) based on an adjustable cantilever mechanism, which can be embedded into a robotic finger. This implementation endows the robotic finger with the decoupled regulation of joint position and stiffness. A concise and computationally efficient stiffness mapping model from the TVSA to the finger joints is then established, providing a theoretical foundation for the stiffness regulation of the tendon-driven fingers. A prototype of a robotic hand equipped with the presented TVSA demonstrates safe interactions with various objects with diverse shapes, weights and stiffness. © 2024, The Authors. All rights reserved.

关键词： Stiffness

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Graph classification based on skeleton and component features

arXiv

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arXiv 2021年

作者： Liu, Xue Wei, Wei Feng, Xiangnan Cao, Xiaobo Sun, Dan Beijing System Design Institute of Electro-Mechanic Engineering Beijing100854 China School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen Guangdong518066 China

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification based on skeleton information using fixed-order structures learned in anonymous random walks manner, and component information using different size subgraphs. Two graphs are similar if their skeletons and components are both similar, thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks. © 2021, CC BY.

关键词： Musculoskeletal system

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Using entropy-driven amplifier circuit response to build nonlinear model under the influence of Lévy jump

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BMC bioinformatics 2022年第SUPPL 12期22卷 437页

作者： Hao Fu Hui Lv Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. lh8481@***. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China. lh8481@***. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. zhangq@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China. zhangq@***.

BACKGROUND:Bioinformatics is a subject produced by the combination of life science and computer science. It mainly uses computer technology to study the laws of biological systems. The design and realization of DNA circuit reaction is one of the important contents of bioinformatics. RESULTS:In this paper, nonlinear dynamic system model with Lévy jump based on entropy-driven amplifier (EDA) circuit response is studied. Firstly, nonlinear biochemical reaction system model is established based on EDA circuit response. Considering the influence of disturbance factors on the system, nonlinear biochemical reaction system with Lévy jump is built. Secondly, in order to prove that the constructed system conforms to the actual meaning, the existence and uniqueness of the system solution is analyzed. Next, the sufficient conditions for the end and continuation of EDA circuit reaction are certified. Finally, the correctness of the theoretical results is proved by numerical simulation, and the reactivity of THTSignal in EDA circuit under different noise intensity is verified. CONCLUSIONS:In EDA circuit reaction, the intensity of external noise has a significant impact on the system. The end of EDA circuit reaction is closely related to the intensity of Lévy noise, and Lévy jump has a significant impact on the nature of biochemical reaction system.

关键词： Entropy-driven amplifier Lyapunov function Lévy jump Nonlinear system model

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Thermal Error Prediction for Electrical Spindles Based on GA-GRNN

SSRN

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SSRN 2024年

作者： Li, Yuan Liu, Xiao Dai, Ye Zhan, ShiQiang Wang, JianHui Wang, ZiJian Wan, ShiMing School of Mechanical Engineering and Automation University of Science and Technology Liaoning Anshan114051 China Key Laboratory of Advanced Manufacturing Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Institute of Digital Design and Automatic Machinery Product Development Harbin University of Science and Technology Harbin150080 China Beijing Institute of Technology China

A significant way to increase the spindle's machining precision is by real-time compensation of thermal error in machine coordinates. The accuracy and versatility of the thermal error prediction model, which serves as the foundation for compensation, are very crucial. This paper aims to increase the accuracy of the thermal error prediction model by first calculating and analyzing the thermal characteristics of the electrical spindle, and then using the finite element method to obtain the distribution law of the temperature field of the electrical spindle;the number of temperature measurement points is optimized to three in order to reduce redundancy in the thermal error modeling data;finally, the hybrid algorithm thermal error prediction model is created with the temperature data of the electrical spindle as the input variable and the thermal error data as the output variable. The hybrid algorithm prediction model offers a new reference technique for the study of thermal error compensation of electrical spindles and has the advantages of high accuracy, high speed, and good generalization ability when compared to the radial basis function neural network algorithm. © 2024, The Authors. All rights reserved.

关键词： Temperature measurement

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