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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery Data mining Compounds

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator System

Switched High-Gain Based Interval Observer Design for Harmon...

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Chinese control Conference (CCC)

作者： Tianhang Ming Xiaoling Wang Bo Liu Housheng Su College of Automation & AI Nanjing University of Posts and Telecommunications Nanjing P. R. China Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing China College of Information Engineering Minzu University of China Beijing P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results.

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Joint Optimization of Energy Consumption and Truck Transfer by Stack Allocation in Steel Warehouses

Joint Optimization of Energy Consumption and Truck Transfer ...

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Chinese Automation Congress (CAC)

作者： Hongwei Wang Zhezhuang Xu Jinlong Wang Boyu Chen Qingdong Zhang Xiuren Wu Yuxiong Xia Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Sanming China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Due to the continuous growth in steel market demand and the shift towards small batches and multiple varieties, efficient storage management has become a key factor in increasing the profitability of logistics parks. However, the current storage methods, due to the unreasonable allocation of stack positions, still result in energy waste during the dispatch phase and the need for trucks to pick up goods across multiple yards. To address these challenges, this paper proposes a comprehensive optimization scheme to reduce truck transfers and energy consumption in steel product dispatch. First, we constructed an optimization problem model that integrates the spatial layout of the logistics park and order data, aiming to maximize the correlation between steel products within the same yard and reduce the total energy consumption during the dispatch phase. Subsequently, a new algorithm called Enhanced Greedy and Tabu-Assisted Adaptive Large Neighborhood Search (EGTA-ALNS) is introduced. Experimental results demonstrate that the proposed algorithm outperforms existing ones in terms of solving speed and scale. It effectively optimizes energy consumption during steel dispatch and reduces the number of truck transfers.

关键词： Lead acid batteries Energy consumption Profitability Storage management Loading Layout Steel Resource management Optimization Logistics

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Sensor-based Multi-Robot Coverage control with Spatial Separation in Unstructured Environments

Sensor-based Multi-Robot Coverage Control with Spatial Separ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xinyi Wang Jiwen Xu Chuanxiang Gao Yizhou Chen Jihan Zhang Chenggang Wang Yulong Ding Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others.

关键词： Point cloud compression System recovery Robot sensing systems Sensors Safety Multi-robot systems Task analysis

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SUNet: Scale-aware Unified Network for Panoptic Segmentation

arXiv

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arXiv 2022年

作者： Yan, Weihao Qian, Yeqiang Wang, Chunxiang Yang, Ming The 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 University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for intelligent vehicles. However, it is challenged with segmenting objects of various scales, especially on extremely large and small ones. In this work, we propose two lightweight modules to mitigate this problem. First, Pixel-relation Block is designed to model global context information for large-scale things, which is based on a query-independent formulation and brings small parameter increments. Then, Convectional Network is constructed to collect extra high-resolution information for small-scale stuff, supplying more appropriate semantic features for the downstream segmentation branches. Based on these two modules, we present an end-to-end Scale-aware Unified Network (SUNet), which is more adaptable to multi-scale objects. Extensive experiments on Cityscapes and COCO demonstrate the effectiveness of the proposed methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Inverse linear-quadratic discrete-time finite-horizon optimal control for indistinguishable homogeneous agents: a convex optimization approach

arXiv

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arXiv 2021年

作者： Zhang, Han Ringh, Axel Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 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 Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Gothenburg41296 Sweden

The inverse linear-quadratic optimal control problem is a system identification problem whose aim is to recover the quadratic cost function and hence the closed-loop system matrices based on observations of optimal trajectories. In this paper, the discrete-time, finite-horizon case is considered, where the agents are also assumed to be homogeneous and indistinguishable. The latter means that the agents all have the same dynamics and objective functions and the observations are in terms of "snap shots" of all agents at different time instants, but what is not known is "which agent moved where" for consecutive observations. This absence of linked optimal trajectories makes the problem challenging. We first show that this problem is globally identifiable. Then, for the case of noiseless observations, we show that the true cost matrix, and hence the closed-loop system matrices, can be recovered as the unique global optimal solution to a convex optimization problem. Next, for the case of noisy observations, we formulate an estimator as the unique global optimal solution to a modified convex optimization problem. Moreover, the statistical consistency of this estimator is shown. Finally, the performance of the proposed method is demonstrated by a number of numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

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Optimizing Neural Network image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： image classification

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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Chinese Automation Congress (CAC)

作者： Jiang Xiang Zhezhuang Xu Jinlong Wang Boyu Chen Qingdong Zhang Xiuren Wu Yuxiong Xia College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China Sansteel Minguang Co. Ltd. Sanming China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range.

关键词： Backtracking Lower bound Cranes Heuristic algorithms Optimal scheduling Search problems Dynamic scheduling Dynamic programming Steel Decision trees

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Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain Stimulation Electrode Implantation

Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain...

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IEEE International Conference on Robotics and Biomimetics

作者： Weihang Huang Shenghao Dai Anze Xu Xianyi He Shaoping Huang Anzhu Gao School of Mechanical Engineering Shanghai Jiao Tong University Shanghai Jiao Tong University Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665405362

Deep brain stimulation (DBS) has been widely used in clinical practice as an effective therapy for neurological disorders. MR-conditional robot-assisted electrode implantation has attracted more attention for DBS because of its great accuracy and high-quality guiding images by using MRI. This paper develops a pneumatically actuated MR-safe parallel robot for deep brain electrode implantation. Its fully plastic components and MR-safe pneumatic actuators enable the robotic system to be MR-safe, posing no known hazards in MR environment. A 6-DOF parallel robot is developed using six slider-crank mechanisms for prismatic motions. Inverse kinematics is built to investigate its workspace and achieve the precise control. Experiments are carried out to validate the feasibility, and results shows that the average error of the motion is 0.11 mm. The developed parallel robot shows the potential advantages in the automatic and precise robot-assisted neurosurgical surgery in future.

关键词： Electrodes Pneumatic actuators Neurological diseases Parallel robots Magnetic resonance imaging Satellite broadcasting Deep brain stimulation

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Direction-Constrained control for Efficient Physical Human-Robot Interaction under Hierarchical Tasks

arXiv

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arXiv 2024年

作者： Xu, Mengxin Wan, Weiwei Wang, Hesheng Harada, Kensuke Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of System Innovation Graduate School of Engineering Science Osaka University Toyonaka Osaka560-0043 Japan Department of Automation the Key Laboratory of System Control and Information Processing of Ministry of Education the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustments. To resolve this limitation, we introduce the concept of directional constraints and develop a direction-constrained optimization algorithm to handle the nonlinearities induced by these constraints. The algorithm solves two sub-problems, minimizing the error and minimizing the deviation angle, in parallel, and combines the results of the two sub-problems to produce a final optimal outcome. The mutual influence between these two sub-problems is analyzed to determine the best parameter for combination. Additionally, the velocity objective in our control framework is computed using a variable admittance controller. Traditional admittance control does not account for constraints. To address this issue, we propose a variable admittance control method to adjust control objectives dynamically. The method helps reduce the deviation between robot velocity and human intention at the constraint boundaries, thereby enhancing interaction efficiency. We evaluate the proposed method in scenarios where a human operator physically interacts with a 7-degree-of-freedom robotic arm. The results highlight the importance of incorporating directional constraints in pHRI for hierarchical tasks. Compared to existing methods, our approach generates smoother robotic trajectories during interaction while avoiding interaction delays at the constraint boundaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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