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International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)

作者： G. Martinov A. Lyubimov A.A. Khoury Institute of Automation and Robotics dept. Computer control systems MSTU STANKIN Moscow Russia

All types of servo step drives motion controllers should guarantee the accuracy of control at all possible rotation speeds taking into consideration the application field of servo drive. Depending on the implementation of servo drives and dividing rotation speed into two ranges low speeds and high speeds, when it's important to have higher rotation speeds (using servo drive as spindle) the motion controller will use speed controlling mode but when it's important to use small values of rotation speeds (using servo drive as feed drive) then it's better to use position controlling mode to ensure the accuracy of the position and minimize the value of errors. Also, it is evidently that the traffic load on CAN bus will affect the stability and the accuracy of controlling CAN servo step drives. The aim of this work is to develop a motion controller that supports dynamically change of controlling strategies of CAN servo step drives depending on rotation speed, additionally will clarify how to reduce CAN bus traffic load.

关键词： Servomotors Kernel Protocols Microcomputers Drives TCPIP Industrial engineering

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Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

arXiv

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

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Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models

SSRN

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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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Dense r-robust formations on lattices

Dense r-robust formations on lattices

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IEEE International Conference on robotics and Automation (ICRA)

作者： Luis Guerrero-Bonilla David Saldaña Vijay Kumar Division of Decision and Control Systems in the School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Autonomous and Intelligent Robotics Laboratory (AIRLab) Lehigh University Bethlehem PA USA GRASP Laboratory University of Pennsylvania

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Robot networks are susceptible to fail under the presence of malicious or defective robots. Resilient networks in the literature require high connectivity and large communication ranges, leading to high energy consumption in the communication network. This paper presents robot formations with guaranteed resiliency that use smaller communication ranges than previous results in the literature. The formations can be built on triangular and square lattices in the plane, and cubic lattices in the three-dimensional space. We support our theoretical framework with simulations.

关键词： Lattices Robot kinematics Communication networks Robustness Robot sensing systems Energy consumption

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

引用

arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

引用

arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Analytical Risks Prediction. Rationale of System Preventive Measures for Solving Quality and Safety Problems 13th

Analytical Risks Prediction. Rationale of System Preventive ...

引用

13th International Conference on Modern Information Technology and IT Education, SITITO 2018

作者： Kostogryzov, Andrey Nistratov, Andrey Nistratov, George Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vavilova Street 44/2 Moscow119333 Russia Main Research Scientific and Probatory Center of Robotics at the Ministry of Defense of Russian Federation Seregin Street 5/1 Moscow125167 Russia The Russian Power Agency of Ministry for the Power Generating Industry Shchepkin Street 40 Moscow1129110 Russia Research Institute of Applied Mathematics and Certification Myasnikovskaya 1-ya Street 3 Moscow1075641 Russia

ISBN: (纸本)9783030468941

The approach for building new probabilistic models which is intended to predict risks for complex intellectual structures under different threats and to solve quality and safety problems in system life cycle, is proposed. The approach includes: selection of models for every system element allowing to estimate probability to lose integrity during given prognostic period;the approach to build probability distribution functions (PDF) by the models selected to predict probability and risks to lose integrity for different scenarios of threats;the approach to integrate PDF for structure from two elements united by serial and/or parallel connection and generalization for complex structure;the approach to form input for modeling and to solve quality and safety problems in application to complex intellectual structures;applications, which can prove efficiency in different areas. The approach is applicable for the analysis of the reliability of complex systems built from unreliable components, estimations of increasing expected reliability and safety for intelligent manufacturing, modeling of robotic and automated systems operating in cosmic space, optimization of a centralized heat supply system, estimating the mean residual time before the next parameters abnormalities for monitored critical systems, control of information quality, estimation of human factor, analysis of vulnerability of sea oil and gas systems etc. Effects are demonstrated by practical examples. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Construction of a specialized CNC system for thread grinding machines

Construction of a specialized CNC system for thread grinding...

引用

2018 International Russian Automation Conference, RusAutoCon 2018

作者： Martinov, Georgi Kovalev, Ilya Khoury, Akram Al MSTU STANKIN Robotics Dept. Computer Control Systems Institute of Automation Moscow Russia

ISBN: (纸本)9781538649381

The aim of this research is to construct a specialized system of thread grinding machines considering the features of thread grinding and the requirements to increase accuracy, the original structure of the control system for a thread grinder is proposed. This research will present an example of programming a software-implemented programmable logic controller to process the key press of operator's panel. © 2018 IEEE.

关键词： Grinding (machining)

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Implementation of vibration signals receiving unit on Raspberry single-board computers

引用

IOP Conference Series: Materials Science and Engineering 2021年第1期1019卷

作者： V A Faerman A V Tsavnin S A Andreev Institute of System Integration and Security Tomsk State University of Control Systems and Radioelectronics Tomsk Russia School of Computer Science & Robotics Tomsk Polytechnic University Tomsk Russia

The paper dedicated to vibration measurement system development on the Raspberry Pi basis. Main features of the solution are low-cost, easy access to components and functionality and flexibility provided by Raspberry Pi usage. Circuit solution and configuring procedures are presented and described. In particular, block diagram of the vibration measurement system with piezo-electric sensors. Two variants of the vibration measurement system, on the Raspberry Pi B+ and Raspberry Pi 3B correspondingly, were implemented and tested as well. In addition, two variants of impedance-matching device were implemented. Conducted and described experiments confirm performance both separate components and the whole solution. Presented results are applicable for correlation leak detectors new algorithmic solutions and in educational process.

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IoT enabled temperature sensing system of molten metal in welding

引用

Materials Today: Proceedings 2021年 45卷 2514-2517页

作者： S. Sanjith N. Balaji L.D. Vijay Anand W. Regis Anne P. Shanmugapriya S. Sheeba Rani Department of Computer Science St. Alphonsa College of Arts and Science Soosaipuram 629157 Tamilnadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai 600044 Tamilnadu India Department of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore 641114 Tamilnadu India Department of Computer Applications Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai 600044 Tamilnadu India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India

The control of temperature in welding is essential to maintain the quality of metal joints. Due to current fluctuations and instrument errors, the quality of joint may be affected. The excess of temperature or molten stage may leads to change the effect of micro structure. An automatic temperature sensing system is needed to eliminate this problem. It is fulfilled through Internet of Things (IoT) based temperature sensing on molten metal in welding. The temperature sensors, micro controller, LAN/Wireless communications and server are used to enhance the automatic temperature sensing on molten metal in welding process. The experimentation is conducted on arc welding of mild steel. The quality characteristics and optimization studied through Taguchi method.

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