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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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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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Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System 27

Optimizing Forest Fire Prevention: Intelligent Scheduling Al...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Ecole Supérieure des Sciences Economiques et Commerciales de Tunis Université de Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： algorithms drone fire Forest monitoring

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An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI 11

An Explainable Convolutional Neural Networks for Automatic S...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Liu, Jun Deng, Feng Yuan, Geng Lin, Xue Song, Houbing Wang, Yanzhi Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781643682402

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high;Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes;explainable CNNs remains a challenge;explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient. © 2022 The authors and IOS Press.

关键词： Image segmentation

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V2-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Bai, Long Cui, Beilei Wang, Liangyu Li, Yanheng Yao, Shilong Yuan, Sishen Wu, Yanan Zhang, Yang Meng, Max Q.-H. Li, Zhen Ding, Weiping Ren, Hongliang Dept. of Electronic Engineering The Chinese University of Hong Kong Hong Kong City University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence Dept. of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The School of Mechanical Engineering Hubei University of Technology Wuhan China The Dept. of Gastroenterology Qilu Hospital of Shandong University Jinan China The School of Artificial Intelligence and Computer Science Nantong University Nantong China

Deep learning can predict depth maps and capsule ego-motion from capsule endoscopy videos, aiding in 3D scene reconstruction and lesion localization. However, the collisions of the capsule endoscopies within the gastrointestinal tract cause vibration perturbations in the training data. Existing solutions focus solely on vision-based processing, neglecting other auxiliary signals like vibrations that could reduce noise and improve performance. Therefore, we propose V2-SfMLearner, a multimodal approach integrating vibration signals into vision-based depth and capsule motion estimation for monocular capsule endoscopy. We construct a multimodal capsule endoscopy dataset containing vibration and visual signals, and our artificial intelligence solution develops an unsupervised method using vision-vibration signals, effectively eliminating vibration perturbations through multimodal learning. Specifically, we carefully design a vibration network branch and a Fourier fusion module, to detect and mitigate vibration noises. The fusion framework is compatible with popular vision-only algorithms. Extensive validation on the multimodal dataset demonstrates superior performance and robustness against vision-only algorithms. Without the need for large external equipment, our V2-SfMLearner has the potential for integration into clinical capsule robots, providing real-time and dependable digestive examination tools. The findings show promise for practical implementation in clinical settings, enhancing the diagnostic capabilities of doctors. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Conceptual Design and Kinematic Analysis of Hybrid Parallel Robot for Accurate Position and Orientation

Conceptual Design and Kinematic Analysis of Hybrid Parallel ...

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SICE Annual Conference

作者： K. M. Asy Ahmed B. Zaky Haitham El-Hussieny Hiroyuki Ishii Mahmoud Elsamanty Mechatronics and Robotics Department School of Innovative Design Engineering Egypt Japan University for Science and Technology Alexandria Egypt Production and Mechanical Design Engineering Department Faculty of Engineering Menoufia University Computer Science and Information Technology Programs (CSIT) Egypt Japan University for Science and Technology Alexandria Egypt Electrical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt School of Creative Science and Engineering Waseda University Japan Mechanical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt

The proposed work presents an innovative hybrid parallel robot, combining a delta robot with a spherical parallel manipulator (SPM) to achieve 6-DoF motion. The conceptual design was developed using SolidWorks, showcasing agile movement with required angles. A comprehensive electrical layout design was initiated for ensuring efficient system control, integrating various components such as actuators, sensors, and control units for seamless operation. A forward kinematic model was mathematically developed and implemented in MATLAB, generating trained data by simulating the robot’s motion for various input parameters. The trained data represents the robot’s position and orientation for different movement types, including linear, circular, pure, and mixed multiple orientation movements. The robot’s performance was evaluated using metrics such as position accuracy, orientation error, and response time. This study analyzed the robot’s kinematics for precise position and orientation control, conducting tests with square path and helix trajectory movements, as well as sudden and sinusoidal wave motor angle changes. Results demonstrated the robot’s platform moved with a pure roll angle, fixed yaw and pitch angles with repetitive roll angle changes, and a small range of roll and pitch angle with a yaw angle, achieving less than 0.2% error in position accuracy and orientation. These findings offer valuable insights for designing and controlling hybrid parallel robots for high-accuracy positioning, highlighting the importance of a well-designed electrical layout, a thorough understanding of the kinematic model, and rigorous performance evaluation for effective system control.

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PR2: A Physics- and Photo-realistic Humanoid Testbed with Pilot Study in Competition

arXiv

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arXiv 2024年

作者： Liu, Hangxin Xie, Qi Zhang, Zeyu Yuan, Tao Leng, Xiaokun Sun, Lining Zhu, Song-Chun Zhang, Jingwen He, Zhicheng Su, Yao Beijing100080 China Institute for Artificial Intelligence School of Artificial Intelligence Peking University Beijing100871 China Department of Automation Tsinghua University Beijing100084 China Department of Computer Science Harbin Institute of Technology Harbin150001 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215000 China

This paper presents the development of a Physicsrealistic and Photo-realistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 offers high-quality scene rendering and robot dynamic simulation, enabling (i) the creation of diverse scenes using various digital assets, (ii) the integration of advanced perception or foundation models, and (iii) the implementation of planning and control algorithms for dynamic humanoid robot behaviors based on environmental feedback. The beta version of PR2 has been deployed for the simulation track of a nationwide full-size humanoid robot competition for college students, attracting 137 teams and over 400 participants within four months. This competition covered traditional tasks in bipedal walking, as well as novel challenges in loco-manipulation and language-instruction-based object search, marking a first for public college robotics competitions. A retrospective analysis of the competition suggests that future events should emphasize the integration of locomotion with manipulation and perception. By making the PR2 testbed publicly available at https://***/pr2-humanoid/PR2-Platform, we aim to further advance education and training in humanoid robotics. © 2024, CC BY.

关键词： Anthropomorphic robots

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Simulation of Optical Tactile Sensors Supporting Slip and Rotation using Path Tracing and IMPM

arXiv

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arXiv 2024年

作者： Shen, Zirong Sun, Yuhao Zhang, Shixin Chen, Zixi Sun, Heyi Sun, Fuchun Fang, Bin The Zhili College Tsinghua University Beijing100084 China The school of Artificial Intelligence Beijing University of Posts and Telecommunications 100876 China The Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing100083 China The BioRobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy

Optical tactile sensors are extensively utilized in intelligent robot manipulation due to their ability to acquire high-resolution tactile information at a lower cost. However, achieving adequate reality and versatility in simulating optical tactile sensors is challenging. In this paper, we propose a simulation method and validate its effectiveness through experiments. We utilize path tracing for image rendering, achieving higher similarity to real data than the baseline method in simulating pressing scenarios. Additionally, we apply the improved Material Point Method(IMPM) algorithm to simulate the relative rest between the object and the elastomer surface when the object is in motion, enabling more accurate simulation of complex manipulations such as slip and rotation. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent robots

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Modelling and Control of a Trailer Sprayer for Precision Spraying

Modelling and Control of a Trailer Sprayer for Precision Spr...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： André Baltazar Filipe N. Santos Antόnio Paulo Moreira Salviano Pinto Soares M. J. C. S. Reis José Boaventura Cunha CRIIS-Centre for Robotics in Industry and Intelligent Systems INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal ECT—School of Science and Technology UTAD—University of Trás-os-Montes and Alto Douro Vila Real Portugal FEUP—Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Portugal Intelligent Systems Associate Laboratory (LASI) Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

关键词： Adaptation models Spraying Predictive models Valves Real-time systems Agriculture Sensors

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Neural Gradient Learning and Optimization for Oriented Point Normal Estimation

arXiv

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arXiv 2023年

作者： Li, Qing Feng, Huifang Shi, Kanle Fang, Yi Liu, Yu-Shen Han, Zhizhong School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi United Arab Emirates Department of Computer Science Wayne State University Detroit United States

We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of the data. We utilize a simple neural network to parameterize the objective function to produce gradients at points using a global implicit representation. However, the derived gradients usually drift away from the ground-truth oriented normals due to the lack of local detail descriptions. Therefore, we introduce Gradient Vector Optimization (GVO) to learn an angular distance field based on local plane geometry to refine the coarse gradient vectors. Finally, we formulate our method with a two-phase pipeline of coarse estimation followed by refinement. Moreover, we integrate two weighting functions, i.e., anisotropic kernel and inlier score, into the optimization to improve the robust and detail-preserving performance. Our method efficiently conducts global gradient approximation while achieving better accuracy and generalization ability of local feature description. This leads to a state-of-the-art normal estimator that is robust to noise, outliers and point density variations. Extensive evaluations show that our method outperforms previous works in both unoriented and oriented normal estimation on widely used benchmarks. The source code and pre-trained models are available at https://***/LeoQLi/NGLO. © 2023, CC BY-NC-SA.

关键词： Multiobjective optimization

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