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27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Rjabtsikov, Viktor Rassolkin, Anton Vaimann, Toomas Kallaste, Ants Lukichev, Dmitry V. Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728141589

The research concentrates on the modernization issues of diesel-electric passenger locomotives TEP70. There is number of TEP70 locomotives in Baltic region that are used very rarely by their direct application but may as well be usable as freight locomotives. The main goal of the study is to change the tractive force of a TEP70 locomotive to be able to transport cargo in the Baltic region. Analysis of TEP70 working principles and load characteristics are presented. Based on the analytical model of locomotive, suggestion to replace the gearbox to increase locomotive pulling capability is proposed. Moreover, the paper presents calculation of the traction effort of TEP70. That kind of calculations are very important for motion planning and it should be done in order to avoid overloading during the working mode of the locomotive. © 2020 IEEE.

关键词： Engines

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Simulation of Wind Turbine Vibrations 27

Simulation of Wind Turbine Vibrations

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27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Andriushchenko, Ekaterina Kallaste, Ants Vaimann, Toomas Rassolkin, Anton Heidari, Hamidreza Demidova, Galina L. Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728141589

In recent years, there has been an increasing interest in renewable energy. Particularly, wind energy is gaining more and more popularity in many countries. However, the main obstacles to wind turbines usage are the vibration and noise close to wind farms. Therefore, this paper is focused on developing a new method for mathematical simulation of vibrations of a wind turbine in order to find out and reduce the vibration level. Particularly, low-frequency vibration of a wind turbine, composed of a tower and rotor in the out-of-balance condition is considered. The proposed method of vibration simulation is based on the theory of elastic beams and the general vibration theory. The calculations are made for a wind turbine VTR 50 Arctic. The results of the calculations are the displacement of the tower top and the stress at the tower foundation. Furthermore, it is worth emphasizing that the method is quite flexible. It allows adding complexity into the system, for instance, external loads such as wind pressure, aerodynamic loads, seismic loads or operational loads. © 2020 IEEE.

关键词： Wind power

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Main Faults and Diagnostic Possibilities of BLDC Motors 27

Main Faults and Diagnostic Possibilities of BLDC Motors

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27th International Workshop on Electric Drives: MPEI department of Electric Drives 90th Anniversary, IWED 2020

作者： Kudelina, Karolina Asad, Bilal Vaimann, Toomas Rassolkin, Anton Kallaste, Ants Lukichev, Dmitri V. Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia ITMO University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728141589

This paper presents the main faults of brushless DC motors and their diagnostic possibilities. Due to the popularity gain of such devices in the past decade, the maintenance and diagnostic of such machines is getting more important. As the machines are used in various applications, they are often critical parts of devices. Due to this, the unexpected failures of such machines are undesired and are to be avoided. The main faults of the machines are listed, some possibilities of their monitoring and diagnostics have been addressed. The necessity of further study has been pointed out. © 2020 IEEE.

关键词： Brushless DC motors

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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Modem mechatronics and robotics education program: Border cooperation between estonia and Russia 11

Modem mechatronics and robotics education program: Border co...

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11th IEEE Global engineering Education Conference, EDUCON 2020

作者： Rassolkin, Anton Vaimann, Toomas Kudelina, Karolina Demidova, Galina L. Lukichev, Dmitry V. Perepelkina, Svetlana Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Itmo University Faculty of Control Systems and Robotics St. Petersburg Russia

ISBN: (纸本)9781728109305

The paper describes the interaction between Tallinn University of Technology (TalTech, Tallinn, Estonia) and ITMO University (St. Petersburg, Russia) based on recently signed agreement. The purpose of the agreement is to record the intention of TalTech and ITMO University to cooperate on implementing a double master's degree program, which involves confirmation of the master's educational level absorption and the award of master's degree and obtention of two diplomas, and to outline the organizational form, principles and terms of the implementation. The double degree Program is a two-year Master's program of 120 ECTS credit points. Its structure and content meets the requirements that both TalTech and ITMO University have established for their master's programs, in accordance with the government standards. Development and approval of the double degree Program is held by the Parties in accordance with the procedures established by them. This paper describes the procedure of implementation of a double-degree master program in mechatronics engineering. © 2020 IEEE.

关键词： engineering education

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A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

A Haptic Exploration and Surface Classification of Objects w...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Peng Qi Yunfeng Wu Tianliang Yao Bo Lu Yi Sun Jian S. Dai Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai P. R. China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou Jiangsu China Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical and Energy Engineering College of Engineering Southern University of Science and Technology Shenzhen P. R. China School of Natural Mathematical and Engineering Sciences King's College London

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index $K$ is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Effects of a Memory-Engaging Secondary Task in a Virtual Setting for Stimulating the Embodiment of an Artificial Upper Limb

Effects of a Memory-Engaging Secondary Task in a Virtual Set...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Andrea Lucaroni Andrea Bottino Fabrizio Lamberti Giulia Mariani Andrea Marinelli Nicolò Boccardo Emanuele Gruppioni Lorenzo De Michieli Matteo Laffranchi Giacinto Barresi Rehab Technologies Lab Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Department of Control and Computer Engineering Politecnico di Torino Turin Italy Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT) Genoa Italy Centro Protesi INAIL Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The embodiment (the sensation that an external object is part of one’s body scheme) is one of the processes targeted by the research in bionic prosthetics as a potential strategy to increase the acceptance of artificial limbs. This study investigates how one of the components of the embodiment, the body ownership (the sensation of possessing the external object), may be affected by a secondary task (possibly competing for cognitive resources requested for the embodiment itself) in a virtual exergame for prosthetic training. In this preliminary study participants, without impairments, are asked to perform activities of pick-and-place in two conditions by using a digital model of the Hannes bionic hand: (i) one is cognitively challenging, requiring to memorize the sequence of objects to move before it disappears; (ii) the other is non-challenging for the subject’s memory, with a constantly visible sequence. In both conditions, each subject is asked to catch objects according to the presented sequence. According to questionnaire data, we noticed how this procedure is feasible. Interestingly, the proprioceptive drift (the person is asked to indicate where they perceive their own real limb without looking at it) in a subsequent Rubber Hand Illusion session with the real bionic hand seems highlighting that the working memory-engaging secondary task can impair the embodiment process in terms of body ownership of the robotic hand. Probably, the memory task actually requires a type of cognitive resources that are necessary for the body ownership process itself during the multisensory stimulation. This allows for envisioning further investigations and novel design concepts for prosthetic training paradigms.

关键词： Training Solid modeling Visualization Biological system modeling Memory management Propioception Virtual environments

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Liu, Yu Zhang, Yuexin Li, Kunming Qiao, Yongliang Worrall, Stewart Li, You-Fu Kong, He Shenzhen518055 China Department of Mechanical Engineering City University of Hong Kong Hong Kong School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Australian Centre for Field Robotics The University of Sydney NSW2006 Australia Australian Institute for Machine Learning The University of Adelaide SA5005 Australia Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering crossdataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2024, CC BY.

关键词： Trajectories

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A novel underwater image synthesis method based on a pixel-level self-supervised training strategy

A novel underwater image synthesis method based on a pixel-l...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wu, Zhiheng Wu, Zhengxing Lu, Yue Wang, Jian Yu, Junzhi Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781665436786

With the rapid development of deep neural networks, underwater vision plays an increasingly important role in the underwater robotic operation. However, the scarce underwater datasets greatly limit the performance of deep learning on underwater visual tasks, further hindering the applications of underwater operation. To solve this problem, we propose an underwater image synthesis method, which can directly convert the natural light image into the synthetic underwater image end-to-end. Particularly, a pixel-level self-supervised training strategy is designed to maximize the structural similarity between the synthesized and real images, through training the real underwater images. Finally, extensive experiments are carried out, and the obtained results demonstrate the effectiveness and superiority of our methods by quantitative and qualitative comparisons. The proposed underwater image synthesis method offers a valuable sight for underwater vision and manipulating control. © 2021 IEEE.

关键词： Deep neural networks

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