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Automation, Robotics and computer Engineering (ICARCE), International Conference on

作者： Feng Yao Huailin Zhao Huaping Liu Graduate School of Control Science and Engineering Shanghai Institute of Technology University Shanghai China Department of Computer Department Tsinghua University Beijing China

ISBN: (纸本)9781665475495

Human-Robot collaboration as a challenging task has received great attention in the academic research field. Many existing search models are aimed at single agent or multi-agent, but there are some defects in the search efficiency of their task targets. Therefore, we propose a human-computer cooperative search algorithm in the indoor scene, where people and agents cooperate to complete the search of related objects. We have developed a platform for human-robot collaboration, and designed a set of algorithms for agent to integrate scene prior knowledge, target recognition, and path planning. The experimental results that the H-R cooperative search model proposed by us shows good efficiency in target search tasks.

关键词： Knowledge engineering Monte Carlo methods Automation Target recognition Collaboration Search problems Path planning

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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Primal-dual Accelerated Mirror-Descent Method for Constrained Bilinear Saddle-Point Problems

arXiv

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arXiv 2024年

作者： Li, Weijian Zeng, Xianlin Pavel, Lacra The Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada The Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

We develop a first-order accelerated algorithm for a class of constrained bilinear saddle-point problems with applications to network systems. The algorithm is a modified time-varying primal-dual version of an accelerated mirror-descent dynamics. It deals with constraints such as simplices and convex set constraints effectively, and converges with a rate of O(1/t2). Furthermore, we employ the acceleration scheme to constrained distributed optimization and bilinear zero-sum games, and obtain two variants of distributed accelerated algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Recognizing Human-object Interactions for a Home Assistive Robot

Recognizing Human-object Interactions for a Home Assistive R...

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Automation, control and Robotics Engineering (CACRE), International Conference on

作者： Liangliang Wang Guanglei Huo Xinwei Chen Chengxi Huang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology Quanzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot – Xiaozhi II verify its robustness.

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Hot Spot Detection of Photovoltaic Module Infrared Near-field Image based on Convolutional Neural Network 4

Hot Spot Detection of Photovoltaic Module Infrared Near-fiel...

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2022 4th International Conference on Energy Systems and Electrical Power, ICESEP 2022

作者： Fan, Fu Na, ZhiXiong Zhang, ChangZhi Li, HaoRan Tong, ChaoLiu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300010 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result. © 2022 institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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Named Entity Recognition for Safety Risks in Electrical Work

Named Entity Recognition for Safety Risks in Electrical Work

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Peng Liu Shanshan Fan Xiaofan Cao Yujiao Jia Jianing Yang Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China School of Electrical Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

The texts of safety risks in eletrical work describe the safety risks involved and management measures. The text records information on potential risks, risk levels, and preventive measures within the electrical work, which is crucial for extracting and mining safety risks. Named Entity Recognition for Safety Risks in Electrical Work (EWSR-NER) is a key technique for information extraction and knowledge discovery. It identifies objects related to safety risks in electrical work, such as risk level, risk factors, risk categories and protective measures, which convey critical meanings within the text. Current Named Entity Recognition (NER) is used to identify generic entities. However, their accuracy is limited by inherent linguistic irregularities in natural language texts, such as nested entities, lengthy entities, and the scarcity of domain-specific annotated corpora. To address this issue, the established standards and principles of safety risk texts in electrical work were followed to analyze the structure and characteristics of the text, developing a dataset of safety risk in electrical work. A new model, RoBERTa-BiLSTM-Efficient GlobalPointer (RoBEGP), was proposed in this paper. It uses the Robertly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach(RoBERTa) pretraining model to learn the characteristics of electrical work content and generate representation vectors. These representation vectors are input into a dynamic fusion layer, which adjusts the weights of each layer's representation vectors based on the specific requirements of the task, reducing the dimensionality from 1024 to 512. The fused vectors are input into a Bidirectional Long Short-Term Memory(BiLSTM) network to generate feature representations. Efficient GlobalPointer decodes these representations to identify nested entities within the text. Experimental results show that the proposed model achieves an F1 score of 92.38% on a self-constructed dataset of safe

关键词： Accuracy Named entity recognition Linguistics Transformers Knowledge discovery Vectors Safety Data mining Object recognition Standards

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Active Wildfires Detection and Dynamic Escape Routes Planning for Humans Through Information Fusion between Drones and Satellites

Active Wildfires Detection and Dynamic Escape Routes Plannin...

引用

International Conference on Intelligent Transportation

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Müegyetem rkp. 3 H-1111 Budapest Hungary and Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest Hungary Faculty of Transportation Engineering and Vehicle Engineering Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest University of Technology and Economics (BME-KJK) Müegyetem rkp. 3 H-1111 Budapest Hungary Budapest Hungary

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction of wildfire areas and real-time dynamic escape route planning for people in distress. Firstly, the fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. Secondly, the road segmentation and the road condition assessment are performed by D-linkNet and NDVI values in the central area of the fire source by UAV. Finally, the dynamic optimal route planning for humans in real time is performed by the weighted A* algorithm in the road network with the dynamic fire spread model. Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.

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Discrete Shuffled Frog Leading Algorithm for Multiple-product Human-robot Collaborative Disassembly Line Balancing Problem

Discrete Shuffled Frog Leading Algorithm for Multiple-produc...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Fan, ChenYang Wang, JiaCun Guo, XiWang Qin, ShuJin Zhou, MengChu Qi, Liang Engineering of Liaoning Petrochemical University School of Information and Control Fushun China Engineering of Monmouth University Dept. Computer Science and Software West Long Branch United States Liaoning Petrochemical University Fushun China Monmouth University West Long Branch United States College of Economics and Management of Shangqiu Normal University Shangqiu China Dept. Electrical and Computer Engineering of New Jersey Institute of Technology Newark United States Tech of Shandong University of Science and Technology Dept. Computer Intelligent Sci. & Qingdao China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM's CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation. © 2022 IEEE.

关键词： Robots

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

引用

International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection

Heterogeneous Graph Convolutional Network for Visual Reinfor...

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Automation, control and Robotics Engineering (CACRE), International Conference on

作者： Liangliang Wang Chengxi Huang Xinwei Chen School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Fujian(Quanzhou)-HIT Research Institute of Engineering and Technology Quanzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method.

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