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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Dong Yang Xiao Xu Mengchen Xiong Edwin Babaians Zican Wang Fanle Meng Eckehard Steinbach Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology (LMT) and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany Information Science Academy of China Electronics Technology Group Corporation

We propose a novel interactive semantic shared control framework that exploits an active high-level communication loop between the human operator and the robot for time-efficient teleoperation. In shared control approaches, accurate prediction of the operator’s intention is crucial to enable the robot to provide meaningful assistance. Incorrect intention prediction (e.g., target objects to be interacted with) increases the task duration due to conflicts between human behaviors and robot guidance. Unlike existing methods, our approach not only passively observes and predicts the human operator’s input in the haptic control loop, but also actively communicates with the human operator in an additional semantic loop in the form of a speech user interface to optimize the effectiveness of assistance. We evaluate our ISSC framework for a pegin-hole teleoperation task. The experimental results show that the proposed framework significantly outperforms teleoperation without assistance and conventional shared control paradigms regarding task execution efficiency and user control quality, and reduces task completion time by up to 26.68% and 39.00%, respectively.

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep Learning

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： H Aditya Pai Piyush Kumar Pareek M S Guruprasad S Chandrappa Krishna Bhargava MIT School of Computing MIT Art Design and Technology University Pune India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Graphic Era (Deemed to-be University) Dehradun India School of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings.

关键词： Deep learning Pneumonia Accuracy Pollution Transfer learning Lung cancer Lung X-ray imaging Neoplasms Biomedical imaging

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Unsupervised Video Anomaly Detection with Self-Attention Based Feature Aggregating

Unsupervised Video Anomaly Detection with Self-Attention Bas...

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International Conference on Intelligent Transportation

作者： Zhenhao Ye Yanlong Li Zhichao Cui Yuehu Liu Li Li Le Wang Chi Zhang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China School of Software Engineering Xi'an Jiaotong University Xi'an Shaanxi China School of Electronics and Control Engineering Chang'an University Department of Automation BNRist Tsinghua University Beijing China

Anomaly detection in surveillance videos is a crucial and challenging task in the intelligent transportation systems. Previous methods utilize a memory module to store prototypical feature embeddings as normal patterns learned from normal training data. However, due to the complexity of real-world scenarios, it is difficult to choose an appropriate size of memory module which can not only memorize normal patterns comprehensively, but also capable of dealing with unseen normal scenarios. To tackle this problem, we learn normal video patterns by constructing and exploring correlations between visual semantics. In the training stage, we act the self-attention map between embeddings as a description of information association between different visual semantics. A self-attention based feature aggregating module is designed to regenerate a feature map through aggregating embeddings with similar information based on the attention map. By decoding the generated feature map instead of the original one to predict the future frame of the input video clip, the model learns to build strong information association between normal visual semantics. Moreover, we observe that abnormal embeddings hardly build strong association with others. Thus, we design a feature-level anomaly criterion referred as prior deviation to increase the difference between attention maps generated by normal and abnormal frames. In the inferring stage, the proposed prior deviation jointly detects anomalies with pixel-level frame prediction error. Experiment results and ablation studies on mainstream benchmarks demonstrate the effectiveness of our design.

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PointHGSA: Efficient Point Cloud Understanding with Hypergraph-Based Self-Attention Network

PointHGSA: Efficient Point Cloud Understanding with Hypergra...

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International Conference on Intelligent Transportation

作者： Zhou Jiang Jing Yang Jie Li Dong Zhang Shaoyi Du School of Software Engineering. Faculty of Eletronic and Information Engineering Xi'an Jiaotong University Xi'an China School of Automation Science and Engineering. Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

With the extensive research on autonomous driving and intelligent transportation technologies, the analysis and understanding of 3D point cloud data have become increasingly important. In this paper, we propose a point cloud understanding network called PointHGSA, targeting semantic segmentation and object classification tasks on point cloud data. The network introduces an innovative representation using a hypergraph structure, enabling a more flexible capture of complex relationships within the point cloud. This hypergraph structure considers relationships between multiple attributes simultaneously, resulting in a more accurate representation of semantic information in the point cloud. PointHGSA combines hyperedge convolution and self-attention mechanisms for feature encoding. Hyperedge convolution effectively considers the global relationships between points during feature propagation, providing a more comprehensive feature representation. The self-attention mechanism adaptively learns contextual information and captures the correlation between hyperedge and vertex features during feature adjustment, offering a more comprehensive modeling capability. Extensive experiments are conducted on the S3DIS, Semantic3D, and ModelNet40 datasets. The experimental results demonstrate that PointHGSA outperforms mainstream models on multiple datasets, particularly excelling in complex structures and objects with rich semantic information.

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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of machines in Manufacturing Industry

Systematic Literature Review on Industry Revolution 4.0 to P...

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Artificial intelligence and Smart Systems (ICAIS), International Conference on

作者： Sowmya P Sathish Kumar Ravichandran Rakshitha Department of Robotics and Artificial Intelligence NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India Department of Computer Science School of Engineering & Technology Christ University Bangalore Karnataka India Department of Artificial Intelligence and Machine Learning NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance.

关键词： Manufacturing industries Productivity Production systems Systematics Companies Life estimation Market research

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Robust Tensor Ring-Based Graph Completion for Incomplete Multi-View Clustering

SSRN

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SSRN 2024年

作者： Xing, Lei Chen, Badong Yu, Changyuan Qin, Jing National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hong Kong Photonics Research Center Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

Incomplete multi-view clustering (IMVC) aims to enhance clustering performance by leveraging complementary information from multi-view data, even in the presence of missing instances. This is challenging due to the interference caused by these missing data points. Current IMVC algorithms generally adopt two main strategies: either disregarding the missing instances and focusing on observable data for clustering, or complementing the missing instances within each view to improve the clustering results. However, effectively leveraging the latent information within these missing instances remains a challenge. In response, we propose a novel IMVC framework that complements the tensor consisting of similarity matrices learned from the available instances to enhance the relationships among samples from different views. In addition, we integrate tensor ring completion and M-estimator-based methods into the IMVC approach. This promotes the integration of inter-view information and mitigates errors introduced by missing instances in the model, respectively. Furthermore, we develop two efficient half-quadratic (HQ) based iterative algorithms with soft/hard threshold or multivariate generalization of minimax-concave (GMC) penalty to regularize the low-rank property. Comprehensive evaluations on seven benchmark datasets demonstrate that our method outperforms state-of-the-art IMVC approaches across various metrics. © 2024, The Authors. All rights reserved.

关键词： Tensors

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Incorporating Feature Signal Transmission with Block-based Haptic Data Reduction for Time-delayed Teleoperation

arXiv

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arXiv 2023年

作者： Wu, Hongjun Xu, Xiao Jin, Zhi Meng, Fanle School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China Media Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Information Science Academy of China Electronics Technology China

This paper presents an innovative feature signal transmission approach incorporating block-based haptic data reduction to address time-delayed teleoperation. Numerous data reduction techniques rely on perceptual deadband (DB). In the preceding block-based approaches, the whole block within the DB is discarded. However, disregarding all signals within the DB loses too much information and hinders effective haptic signal tracking, as these signals contain valuable information for signal reconstruction. Consequently, we propose a feature signal transmission approach based on the block algorithm that aggregates samples as a unit, enabling high-quality haptic data reduction. In our proposed approach, we employ max-pooling to extract feature signals from the signals within the DB. These feature signals are then transmitted by adjusting the content of the transmission block. This methodology enables the transmission of more useful information without introducing additional delay, aside from the inherent algorithmic delay. Experimental results demonstrate the superiority of our approach over other state-of-the-art (SOTA) methods on various assessment measures under distinct channel delays. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote control

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Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study Assembling a Human Hand Injury Protection Database

arXiv

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arXiv 2024年

作者： Kirschner, Robin Jeanne Micheler, Carina M. Zhou, Yangcan Siegner, Sebastian Hamad, Mazin Glowalla, Claudio Neumann, Jan Rajaei, Nader Burgkart, Rainer Haddadin, Sami Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80992 Germany Department of Orthopaedics and Sports Orthopaedics Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching near Munich85748 Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau82418 Germany Department of Diagnostic and Interventional Radiology Klinikum rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany

The use of pointed or edged tools or objects is one of the most challenging aspects of today’s application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries. © 2024, CC BY.

关键词： Risk assessment

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An Improved A-star Algorithm for Path Planning Based on Ant Colony Optimization

An Improved A-star Algorithm for Path Planning Based on Ant ...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Yingbo Luo Meibao Yao Xueming Xiao Bo Zheng Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR Lab Changchun University of Science and Technology Changchun China Shanghai Aerospace Control Technology Institute Shanghai China

Mobile robots are widely used in industrial manufacturing, agricultural production, health care, home services, transportation, and other fields. As the most basic and important research topic in the field of mobile robots, path planning technology has always been the focus of attention. To solve the problems caused by the traditional A* algorithm that the path length is too long and the trajectory is not smooth, This paper proposes an improved A* algorithm based on Ant Colony Optimization. Firstly, use the A* algorithm to roughly find a constrained shortest path. Secondly, extract the key nodes of the shortest path and calculate the distribution of obstacles between these key nodes. Finally, use the improved ant colony algorithm to optimize the searched path to generate the final path. Simulation experiments show that the time complexity and space complexity of the improved A* algorithm are reasonable. The generated path length is shorter, the total turning angle is smaller, the trajectory is smoother, and it is more suitable for the real environment. Compared with the traditional A* algorithm, it has obvious advantages.

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Sparse Pedestrian Character Learning for Trajectory Prediction

arXiv

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arXiv 2023年

作者： Dong, Yonghao Wang, Le Zhou, Sanpin Hua, Gang Sun, Changyin The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Wormpex AI Research BellevueWA98004 United States School of Artificial Intelligence Anhui University Anhui Hefei230039 China

Pedestrian trajectory prediction in a first-person view has recently attracted much attention due to its importance in autonomous driving. Recent work utilizes pedestrian character information, i.e., action and appearance, to improve the learned trajectory embedding and achieves state-of-the-art performance. However, it neglects the invalid and negative pedestrian character information, which is harmful to trajectory representation and thus leads to performance degradation. To address this issue, we present a two-stream sparse-character-based network (TSNet) for pedestrian trajectory prediction. Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream. Moreover, to model the negative-removed characters, we propose a novel sparse character graph, including the sparse category and sparse temporal character graphs, to learn the different effects of various characters in category and temporal dimensions, respectively. Extensive experiments on two first-person view datasets, PIE and JAAD, show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate different effects of various characters and prove that TSNet outperforms approaches without eliminating negative characters. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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