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Trajectory Planning of 7-DOF Space Manipulator for Minimizing Base Disturbance

International Journal of advanced Robotic Systems 2016年第2期13卷 44-44页

作者： Qiang Zhang Lan Wang Dongsheng Zhou Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

In the free-floating mode, there is intense dynamic coupling existing between the space manipulator and the base, and the base attitude may change while performing a motion with its manipulator. Therefore, it is necessary to reduce the interference that resulted from the manipulator movement. For planning trajectories of the space manipulator with 7 degrees of freedom (7-DOF), simulated annealing particle swarm optimization (SAPSO) algorithm is presented in the paper. Firstly, kinematics equations are setup. Secondly, the joint functions are parameterized by sinusoidal functions, and the objective function is defined according to the motion constraints of manipulator and accuracy requirements of the base attitude. Finally, SAPSO algorithm is used to search the optimal trajectory. The simulation results verify the proposed method.

关键词： Space Manipulator 7-DOF Base Attitude SAPSO Algorithm

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A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Xingchen Xie Xiaoting Wu Xiaopeng Wei Chao Che Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian Liaoning 116622 China

In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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Wind Tunnel Mach Number Prediction Based on the Pruned Feature Subsets Ensemble Method

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Journal of Aerospace Engineering 2017年第4期30卷 1-8页

作者： Wang, Xiaojun Yuan, Ping Mao, Zhizhong Rui, Wei University Lctr. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian Univ. Dalian116622 China Institute of Automatization College of Information Science and Engineering Northeastern Univ. Shenyang110004 China Institute of High Speed Aerodynamics China Aerodynamics Research and Development Center Mianyang Sichuan621000 China

In a wind tunnel, the Mach number in the test section is an important parameter that should be predicted quickly and accurately. In building a Mach number prediction model, large-scale and high-dimensional data is the main issue. To solve the issue, the feature subsets ensemble (FSE) method has been proposed. However, a major drawback of the FSE method is that a large number of submodels are necessarily combined. In this paper, the maximum entropy pruning (MEP) method is proposed to overcome this drawback in the FSE Mach number prediction model. The MEP method refers to finding a subset of submodels that best approximates the entire submodels, while maximizing the quadratic Rényi entropy criterion. Experiments demonstrate that, with much fewer submodels than the FSE and other Mach number models, the MEP-FSE Mach number model can improve the prediction performance (i.e., the generalization), and meet the requirements of the forecasting speed and the root mean square error (less than 0.002). © 2017 American Society of Civil Engineers.

关键词： Wind tunnels

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keliang Zhao Jovial Niyogisubizo Linxia Xiao Yi Pan Dongqing Wei Didi Rosiyadi Yanjie Wei Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Bandung Indonesia

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks

A Heuristic Sparsification Method to Improve the Performance...

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International Conference on Information Science and Technology (ICIST)

作者： Fangyu Zhang Yi Pan Yanjie Wei Jin Liu Zhenli Lin Centre for High Performance Computing Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China School of Computer Science and Engineering Central South University Changsha China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced.

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LCFNet: Loss Compensation Fusion Network for Real-Time Semantic Segmentation of Urban Road Scenes

LCFNet: Loss Compensation Fusion Network for Real-Time Seman...

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International Conference on intelligent Transportation

作者： Lu Yang Yiwen Bai Fenglei Ren Shiyu Zhang Chongke Bi Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

Semantic segmentation is used by intelligent transportation systems to understand and sense the traffic environment. However, achieving semantic segmentation in real-time is a challenge due to the necessity of both high accuracy and fast processing. This is especially valuable for applications such as autonomous driving and industrial robotics. In this paper, we propose a real-time semantic segmentation network, called LCFNet, which makes use of three-branch structure. The LCFNet consists of Lightweight Detail Guidance Fusion (L-DGF) and Lightweight Semantic Guidance Fusion (L-SGF) modules. Both modules aggregate information from various network layers. In the termination of network, a Total Guidance Fusion (TGF) module is proposed for processing information from all three branches. Depth-wise Convolution Pyramid Pooling (DCPP) module is also included to optimize accuracy and simplify computation. The effectiveness of LCFNet is demonstrated on two typical semantic segmentation datasets, Cityscapes and CamVid. On a single NVIDIA GeForce GTX 2080 Ti GPU, LCFNet reaches 77.02% mIoU at 95.97 FPS and 81.17% mIoU at 204.82 FPS, respectively.

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Explore Contextual Information for 3D Scene Graph Generation

arXiv

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arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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