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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Wei, Feili Gu, Chaochen Wu, Kaijie Cheng, Hao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350327182

The utilization of impedance control is capable of achieving excellent force tracking performance. However, the balance between stability and flexibility is seldom considered in current methods. To address this issue, a novel adaptive variable impedance controller for contact force tracking is proposed in this study. Lyapunov theory is applied to develop an adaptive adjustment law of damping parameter in impedance control to enhance the robot's adaptability to uncertain environment and dynamic desired force. Additionally, a force compensator is designed based on direct adaptive control to expedite the response of force tracking. What sets this proposed method apart is that it does not require an accurate understanding of environmental parameters. Instead, it dynamically modifies the impedance parameters based on the force error to guarantee the stability and coordination of the system. Simulation results indicate that the proposed controller improves the flexibility and robustness of the force tracking capability of the robot system when compared to existing variable impedance control methods and effectively diminishes force overshoot. © 2023 IEEE.

关键词： Uncertainty analysis

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Accelerated Simulation of Master Equation for Open Quantum Systems 42

Accelerated Simulation of Master Equation for Open Quantum S...

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42nd Chinese control Conference, CCC 2023

作者： Yue, Bo Wang, Yanfang Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789887581543

Simulation of open quantum systems is undoubtedly critical to design of quantum devices and applications of quantum technologies since dynamics of open quantum systems cannot be solved analytically in general. However, the dimension of open quantum systems grows exponentially as the number of their subsystems increases or as involving complicated environments, which reduces the computational efficiency of the simulation. In this paper, we boost numerical simulation of master equations of open quantum systems from several aspects with which the total time and the occupied memory in the simulation are significantly reduced without losing precision. Our method can be applied to the simulation of non-Markovian quantum systems described by an augmented system model as well. We conduct numerical experiments on simulation of a non-Markovian spin chain. Compared with the algorithm in a well-known toolbox, QuTiP, our method can take 26.3% time and less memory under fair conditions. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational efficiency

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Chen Zheng Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance.

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Accelerated Simulation of Non-Markovian Quantum Systems Using Quantum Trajectory 42

Accelerated Simulation of Non-Markovian Quantum Systems Usin...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yanfang Yue, Bo Xue, Shibei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789887581543

Quantum trajectory can be viewed as a numerical simulation method for wavefunctions which consume smaller memories compared to density matrices in master equations. In this paper, we adopt quantum trajectory to simulate the evolution of a non-Markovian quantum system modeled by an augmented system and further use parallel computing to improve efficiency. Compared to the Lindblad master equation solver in QuTiP, it can run more efficiently using less memory. In the example of a 8-spin-chain non-Markovian system, it only takes about 0.57% of the memory footprint and 4.12% of the runtime of the solver for the Lindblad master equation. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Quantum chemistry

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HTRNN: A New Protein Nuclear Localization Prediction Tool

HTRNN: A New Protein Nuclear Localization Prediction Tool

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Xinyu Miao Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties.

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GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation 30th

GDTNet: A Synergistic Dilated Transformer and CNN by Gate ...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Can Wang, Zhiqiang Zhang, Yuan Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9783031533013

As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer aided analysis

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Altered spatiotemporal consistency of local neural activities in Parkinson’s disease 3

Altered spatiotemporal consistency of local neural activitie...

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3rd International Conference on Optics and image processing, ICOIP 2023

作者： Jin, Chaoyang Yang, Lei Qi, Shouliang College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

ISBN: (纸本)9781510667426

The present study aims to explore the local spatiotemporal consistency of brain connectivity in PD patients. In total, 30 PD patients and 20 health controls (HC) were collected from the Parkinson’s progression markers initiative (PPMI) and their resting state functional MRI images are acquired. FOur-dimensional Consistency of local neural Activities (FOCA) was employed for the first time to explore local brain activity differences between the two groups. To explore the relationship between FOCA values and neuropsychological indices, the Pearson correlation analysis was performed. And receiver operating characteristic (ROC) analysis was carried out to distinguish PD from HC. Compared with HC, PD patients demonstrated decreased FOCA values in the right inferior temporal gyrus (ITG.R), right middle temporal gyrus (MTG.R), right middle occipital gyrus (MOG.R), right superior occipital gyrus, right inferior occipital gyrus, left precentral gyrus (PreCG.L), left postcentral gyrus (PoCG.L) and left rolandic operculum. The FOCA values in MTG.R and ITG.R were positively related to Montreal Cognitive Assessment (MoCA) values. Negative relationships had been found in FOCA values of PreCG.L and MOG.R and Unified Parkinson’s Disease Rating Scale (UPDRS) III scores. What’s more, the ROC analysis showed that the FOCA values of altered brain regions can be used to distinguish PD patients from HC with high performance. PD patients demonstrated disrupted local brain activity in several brain regions. These brain regions are mainly involved in motor, cognitive and emotional functions. The current study provides important insights into understanding the pathophysiological mechanism of PD symptoms. © 2023 SPIE. All rights reserved.

关键词： Magnetic resonance imaging

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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A Dual-branch Model for Early Detection of Alzheimer’s Disease Using Resting-State fMRI

A Dual-branch Model for Early Detection of Alzheimer’s Dise...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Yixuan Wang Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

Alzheimer’s disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images.

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SAR Scene Classification Based on Self-supervised Jigsaw Puzzles 5th

SAR Scene Classification Based on Self-supervised Jigsaw Puz...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Ren, Zhongle Lu, Yiming Wang, Hanxiao Zhang, Yu Hou, Biao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi’an710071 China

ISBN: (纸本)9783031149023

Scene classification is a hot issue in the field of SAR image interpretation. Many SAR image interpretation tasks can be promoted with the development of highly credible scene classification methods. But the fussy steps of traditional methods and the imperious demands of labeled samples in deep learning-based methods restrict the effective feature learning in SAR scene classification. Hence, a self-supervised learning method based on Jigsaw puzzles is proposed to address the problems. Concretely, the Jigsaw puzzle reassembly of the SAR image block is firstly taken as the upstream task without manual labels. Once the correct spatial arrangement is obtained from it, the learned high-level feature from the upstream task is used as the pre-training model for the downstream task, which is then fine-tuned with only a few labeled samples to enhance the performance of the SAR scene classification task. Experimental results on 25-class real SAR scenes confirm the proposed method can greatly improve the scene classification performance than directly training the network with the same number of labeled samples used in the downstream task. © 2022, IFIP International Federation for Information processing.

关键词： Synthetic aperture radar

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