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Advances in Industrial Control

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Preface

Advances in Industrial control 2019年 vii-xi页

作者： Xia, Yuanqing Zhang, Jinhui Lu, Kunfeng Zhou, Ning School of Automation Beijing Institute of Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China College of Computer and Information Sciences Fujian Agriculture and Forestry University FuzhouFujian China

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

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arXiv 2021年

作者： Liu, Wanli Li, Chen Rahaman, Md Mamunur Jiang, Tao Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Shengjing Hospital China Medical University Shenyang110001 China Suzhou Ruiguan Technology Company Ltd. Suzhou215000 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Aspect ratio

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

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arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression

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V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection ...

IEEE Symposium on Intelligent Vehicle

作者： Caiji Zhang Bin Tian Shi Meng Shuangying Qi Yang Sun Yunfeng Ai Long Chen School of Artificial Intelligence University of Chinese Academy of Sciences and With the National Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Bin Tian is With the National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chongqing Iron and Steel Group Mining Co. Ltd. Chongqing China School of Mechanical and Equipment Engineering Hebei University of Engineering Handan China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Waytous Inc. Qingdao China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Waytous Ltd. Qingdao China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.

关键词： Point cloud compression Visualization Three-dimensional displays Laser radar Roads Collaboration Object detection

Segmented Model-Based Hydrogen Delivery control for PEM Fuel Cells: A Port-Hamiltonian Approach

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arXiv 2024年

作者： Kumar, Lalitesh Chen, Jian Wu, Chengshuai Chen, Yuzhu van der Schaft, Arjan The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Fluid Power and Mechatronic Systems College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Engineering Xi'an Jiaotong University Xian710049 China The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands

This paper proposes an extended interconnection and damping assignment passivity-based control technique (IDA-PBC) to control the pressure dynamics in the fuel delivery subsystem (FDS) of proton exchange membrane fuel cells. The fuel cell stack is a distributed parameter model which can be modeled by partial differential equations (PDEs). In this paper, the segmentation concept is used to approximate the PDEs model by ordinary differential equations (ODEs) model. Therefore, each segments are having multiple ODEs to obtain the lump-sum model of the segments. Subsequently, a generalized multi-input multi-output lumped parameters model is developed in port-Hamiltonian framework based on mass balance to minimize the modeling error. The modeling errors arises due to the difference between spatially distributed pressures in FDS segments, and also due to the difference between the actual stack pressure and the measured output pressure of the anode. The segments interconnection feasibilities are ensured by maintaining passivity of each segment. With consideration of re-circulation and bleeding of the anode in the modeling, an extended energy-shaping and output tracking IDA-PBC based state-feedback controller is proposed to control the spatially distributed pressure dynamics in the anode. Furthermore, a sliding mode observer of high order is designed to estimate the unmeasurable pressures in FDS with known disturbances. Performance recovery of output feedback control is accomplished with explicit stability analysis. The effectiveness of the proposed IDA-PBC approach is validated by the simulation results. © 2024, CC BY.

关键词： Anodes

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

A registration-aided domain adaptation network for 3D Point cloud based place recognition

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

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arXiv 2024年

作者： Zhao, Yong Zhu, Zhengqiu Gao, Chen Wang, En Huang, Jincai Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers’ limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails

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arXiv 2021年

作者： Zhang, Yang Yu, Xin Lu, Xiaobo Liu, Ping The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Australian Institute of Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia The School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Singapore138634 Singapore

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8×) the occluded and tiny faces (16×16 pixels). Pro-UIGAN iteratively (1) estimates facial geometry priors for low-resolution (LR) faces and (2) acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network upsamples and inpaints occluded LR faces via a coarse-to-fine fashion, significantly reducing undesirable artifacts and blurriness. Specifically, we design a novel cross-modal attention module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutually beneficial manner. Extensive experiments show that our Pro-UIGAN attains visually pleasing completed HR faces, thus facilitating downstream tasks, i.e., face alignment, face parsing, face recognition as well as expression classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks