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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Braunisch, Nico Lehmann, Robert Wollschlaeger, Martin Ristin, Marko Van De Venn, Hans Wernher Otto, Björn Kleinert, Tobias TU Dresden Institute of Applied Computer Science Dresden Germany Institute of Mechatronic Systems Zurich Switzerland Otto von Guericke University Institute for Automation & Communication Magdeburg Germany RWTH Aachen University Information and Automation Systems for Process and Material Technology Aachen Germany

ISBN: (纸本)9798350339918

Digital twins, the virtual representations of physical assets, processes or systems, are becoming increasingly important in cyber-physical systems. They are a foundational block of Industry 4.0, the movement to digitalize industrial *** Asset Administration Shell (AAS) has been established as the preferable model to capture interoperable digital twins in the context of Industry 4.0. As asset administration shells become more prevalent, so does the need for specialized software development kits (SDKs) to manage *** intertwined Industry 4.0 value chains are highly dependent on interoperability. The SDKs for AAS are often the direct interface between the components, and the malfunctioning of the SDKs leads to the breakage of the value chain. It is therefore important that we have the tools to determine how individual SDKs behave, and detect when they malfunction. In this work, we implement an approach to assessing the maturity of the SDKs for managing asset administration shells, and perform a thorough survey and evaluation of the existing SDKs. © 2023 IEEE.

关键词： Shells (structures)

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A frequency-domain index for quickly selecting optimal down-sampling factor in photoacoustic imaging 17

A frequency-domain index for quickly selecting optimal down-...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Tang, Shihao Wan, Min Li, Jiani Zhang, Yameng Tao, Ling Li, Weitao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Department of Computer Engineering Nanjing Institute of Technology No.1 Hongjing Avenue Jiangsu Nanjing211167 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Photoacoustic microscopy (PAM) has emerged as a rapidly advancing non-invasive medical imaging method in recent years. However, slow imaging speed has impeded its widespread adoption in clinical applications. In certain scenarios, sparse spatial sampling is essential for PAM so there is a trade-off between spatial resolution and imaging speed. To address this limitation, we propose a frequency domain index based on cumulative power difference (CPD) to determine rapidly the optimal down-sampling factors. In this study, the structural images of mice ears were acquired by the PAM system. Subsequently, the optimal down-sampling factor was determined through CPD analysis of these images via interpolation. Finally, the correlation between cumulative power difference and the image quality loss curve were analyzed. The results indicate that the quality of the reconstructed images decreases with the increasing down-sampling factor. Moreover, the cumulative power difference is an effective tool for rapid assessment of reconstruction image quality degradation due to varying down-sampling factors. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Research on the Transient Potential Rise Characteristics of High - speed Railway Rail Lightning Strike and Its Impact on Trackside Signal Equipment 37

Research on the Transient Potential Rise Characteristics of ...

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37th International Conference on Lightning Protection, ICLP 2024

作者： Wang, Shulai Xiang, Nianwen Wang, Chaoqun Lyu, Zengwei Chen, Zhiwei School of Electrical Engineering and Automation Hefei University of Technology Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783800764174

Lightning strikes on the catenary will cause a sharp rise in rail potential, which directly threatens the safety of the signal equipment connected to the rail along the trackside. It is urgent to carry out research on the propagation law of transient overvoltage on rails caused by lightning strikes on the catenary. A rail lightning electromagnetic transient calculation model considering the influence of integrated grounding system and catenary is established, and the influence law of different lightning strike point positions, different pier heights, and different lightning current waveforms on the distribution of rail potential rise is calculated. The specific conclusions are as follows: when lightning strikes different positions within a catenary span, the rail potential within 700m near the lightning strike point will exceed the lightning strike tolerance level of the trackside signal equipment, causing equipment damage. The potential rise increases by about 9 kV;for every 5 m increase in pier height, the rail potential rise increases by about 16%. Based on the law of the rail potential rise, the common-mode protection methods for the trackside signal equipment are proposed by appropriately reducing the horizontal connection distance of uplink and downlink ground wires to decrease the rail potential and installing common-mode protection devices. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Signaling equipment

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Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation

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IEEE Transactions on Image Processing 2023年 32卷 6183-6194页

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue Institute of Automation State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Tsinghua University Beijing100190 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. © 1992-2012 IEEE.

关键词： Semantic Segmentation

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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Improving Surveillance Object Detection with Adaptive Omni-Attention over Both Inter-frame and Intra-frame Context 1

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16th Asian Conference on computer Vision, ACCV 2022

作者： Yu, Tingting Chen, Chen Zhou, Yichao Hu, Xiyuan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031262845

ISBN: (纸本)9783031262838

Surveillance object detection is a challenging and practical sub-branch of object detection. Factors such as lighting variations, smaller objects, and motion blur in video frames affect detection results, but on the other hand, the temporal information and stable background of a surveillance video are major advantages that does not exist in generic object detection. In this paper, we propose an adaptive omni-attention model for surveillance object detection, which effectively and efficiently integrates inter-frame contextual information to improve the detection of low-quality frames and intra-frame attention to suppress false positive detections in the background regions. In addition, the training of the proposed network can converge quickly with less epochs because during multi-frame fusion stage, the pre-trained weights of the single-frame network can be used to update simultaneously in reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed. (Code is available at https://***/Yubzsz/Omni-Attention-VOD.) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Structural Dependence Learning Based on Self-attention for Face Alignment

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Machine Intelligence Research 2024年第3期21卷 514-525页

作者： Biying Li Zhiwei Liu Wei Zhou Haiyun Guo Xin Wen Min Huang Jinqiao Wang Foundation Model Research Center Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100083China Alpha(Beijing)Private Equity Beijing100083China School of Computer Science National University of Defense TechnologyChangsha410073China

Self-attention aggregates similar feature information to enhance the features. However, the attention covers nonface areas in face alignment, which may be disturbed in challenging cases, such as occlusions, and fails to predict landmarks. In addition, the learned feature similarity variance is not large enough in the experiment. To this end, we propose structural dependence learning based on self-attention for face alignment (SSFA). It limits the self-attention learning to the facial range and adaptively builds the significant landmark structure dependency. Compared with other state-of-the-art methods, SSFA effectively improves the performance on several standard facial landmark detection benchmarks and adapts more in challenging cases.

关键词： computer vision face alignment self-attention facial structure contextual information

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DAPSPNet: Deep Aggregation Pyramid Strip Pooling Network for Real-time and Accurate Segmentation 3

DAPSPNet: Deep Aggregation Pyramid Strip Pooling Network for...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Wang, Lin Xiong, Gang Zhu, Fenghua Zhang, Hui Chen, Dewang Fujian University of Technology School of Transportation Fuzhou China Institute of Automation Chinese Academy of Sciences Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350318470

This paper introduces an efficient Convolutional Neural Networks (CNN) architecture named DAPSPNet for Real-time semantic segmentation. We propose a novel dual-resolution network, DAPSPNet, and augment it with strip pooling in the multi-scale feature extraction module to extract strip-shaped features more effectively. The convolution kernels have lengths of 5, 9, and 17, with a width of 1. We chose strip pooling as the supplement for two reasons. First, strip pooling is a lightweight technique that reduces the number of parameters and computations involved in pooling operations. Second, there are also some strip-shaped features in the contextual information, which are in line with the needs of real-time semantic segmentation of road scenes. Extensive experimental evaluations on the Cityscapes dataset demonstrate the competitive performance of DAPSPNet compared to several state-of-the-art methods in most scenarios. © 2023 IEEE.

关键词： Semantic Segmentation

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A Unified Deep Reinforcement Learning Approach for Constructing Rectilinear and Octilinear Steiner Minimum Tree

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2711-2724页

作者： Lin, Zhenkun Liu, Genggeng Huang, Xing Lin, Yibo Zhang, Jixin Liu, Wen-Hao Wang, Ting-Chi Fuzhou University College of Computer and Data Science Fuzhou350100 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China Peking University Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Beijing China Peking University Institute of Electronic Design Automation Wuxi China Hubei University of Technology School of Computer Science Wuhan China Nvidia Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

The Steiner Minimum Tree (SMT) serves as an optimal connection model for multi-terminal nets in Very Large Scale Integration (VLSI). Constructing both Rectilinear Steiner Minimum Tree (RSMT) and Octilinear Steiner Minimum Tree (OSMT) are known to be NP-hard problems. Simultaneously, constructing multiple topologies of SMTs for a given net holds significant importance in alleviating routing constraints such as alleviating congestion and ensuring timing convergence. However, existing efforts predominantly focus on designing specialized methods to construct a specifically structured SMT for a given net, making it challenging to extend to different structures or topologies of SMTs, while also exhibiting insufficient optimization capabilities. In this work, we propose a unified approach based on Deep Reinforcement Learning (DRL) to address both RSMT and OSMT problems while generating diverse routing topologies. First, we design an Edge Point Sequence (EPS) that leverages the structural characteristics of SMT to connect the output of the deep learning model with the SMT structure. Second, we propose a deep learning model tailored for EPS, employing the negative wirelength of SMT as a reward to train the model using DRL. Third, we provide a corresponding rapid and accurate wirelength computation algorithm for evaluating the quality of the construction solution to expedite model training. Finally, we leverage the stochastic nature of machine learning to construct diverse SMT construction solutions. To the best of our knowledge, this is the first unified approach capable of simultaneously addressing both RSMT and OSMT problems while generating diverse solutions. The proposed unified approach demonstrates superior solution quality and higher efficiency compared to specifically designed algorithms. © 1982-2012 IEEE.

关键词： Stochastic systems

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Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug

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Data Intelligence 2022年第1期4卷 134-148页

作者： Ting Jia Yuxia Yang Xi Lu Qiang Zhu Kuo Yang Xuezhong Zhou Institute of Medical Intelligence School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China BNRIST/Department of Automation Tsinghua UniversityBeijing 100084China

Due to the large-scale spread of COVID-19,which has a significant impact on human health and social economy,developing effective antiviral drugs for COVID-19 is vital to saving human *** biomedical associations,e.g.,drug-virus and viral protein-host protein interactions,can be used for building biomedical knowledge *** on these sources,large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and *** utilize the various heterogeneous biomedical associations,we proposed a fusion strategy to integrate the results of two tensor decomposition-based models(i.e.,CP-N3 and Compl Ex-N3).Sufficient experiments indicated that our method obtained high performance(MRR=0.2328).Compared with CP-N3,the mean reciprocal rank(MRR)is increased by 3.3%and compared with Compl Ex-N3,the MRR is increased by 3.5%.Meanwhile,we explored the relationship between the performance and relationship types,which indicated that there is a negative correlation(PCC=0.446,P-value=2.26 e-194)between the performance of triples predicted by our method and edge betweenness.

关键词： Link prediction Knowledge graph COVID-19 Antiviral drug prediction Tensor decomposition

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