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arXiv 2019年

作者： Zhou, Yanlin Lu, Fan Pu, George Ma, Xiyao Sun, Runhan Chen, Hsi-Yuan Li, Xiaolin Wu, Dapeng National Science Foundation Center for Big Learning Large-scale Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States Department of Mechanical and Aerospace Engineering University of Florida GainesvilleFL United States Amazon Robotics North Reading AI Institute Tongdun Technology

We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control into a perception module and a controller module, we can train a DRL agent without sophisticated physics or 3D modeling. In addition, the modular framework averts daunting retrains of an image-to-action end-to-end neural network, and provides flexibility in transferring the controller to different robots. First, we train a convolutional neural network (CNN) to accurately localize in an indoor setting with dynamic foreground/background. Then, we design a new DRL algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. We also show that MPG is robust at tracking varying leader movements and can naturally be extended to problems of formation control. Leveraging reward shaping, features such as collision and obstacle avoidance can be easily integrated into a DRL controller. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan

arXiv

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arXiv 2020年

作者： Do, Ton Duc Gui, Meei Mei Ng, Kok Yew Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Nur-Sultan Kazakhstan School of Chemistry and Chemical Engineering Queen's University Belfast Belfast United Kingdom Engineering Research Institute University of Ulster Belfast United Kingdom Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Bandar Sunway Malaysia

This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations;time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Conjugated Polymer-based Explosives Sensor: Progresses in the Design of a Handheld Device

Conjugated Polymer-based Explosives Sensor: Progresses in th...

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IEEE Sensors

作者： Tiago Neves Lino Marques Liliana Martelo Hugh D. Burrows Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Chemistry Department University of Coimbra Coimbra Portugal

ISBN: (纸本)9781479901609

This work addresses the development of a new handheld explosive sensor based on the AFP (Amplified Fluorescent Polymer) Technology. The polymer is the PF2T Ethyl Cellulose, a blue fluorescent polymer whose fluorescence is attenuated with the presence of nitroaromatics, like the vapours typically released by common explosives. We intend to contribute to the state of the art in the field of landmine odour vapour detection, by developing a simple, economic, portable, highly sensitive and easily calibrated explosives sensor. Preliminary testing results with nitrobenzene show the sensor's ability to detect the presence of that vapour in concentrations much lower than the ones corresponding to its vapour pressure.

关键词： hand-held equipment EXPLOSIVES state of the art Vapors Sensors Nitrobenzenes Vapor Pressure AFP alpha-Fetoproteins Polymer Technology ANTITANK MINES

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BYTECounter: Improving Vehicle Turning-Movement Counting

BYTECounter: Improving Vehicle Turning-Movement Counting

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International Conference on Intelligent Transportation

作者： Mateus Líbano Monteiro Alexandre Bernardino H. Sofia Pinto Computer Science: Artificial Intelligence and Cybersecurity from Instituto Superior Técnico Universidade de Lisboa Portugal Department of Electrical and Computer Engineering Institute for and Systems and Robotics of Instituto Superior Técnico Universidade de Lisboa Portugal INESC-ID and Department of Computer Science and Artificial Intelligence Instituto Superior Técnico Universidade de Lisboa Portugal

This paper proposes a methodology to enhance the accuracy of vehicle turning-movement counting in video footage. The methodology uses a three-component system comprising object detection, object tracking, and object turning-movement counting. To improve vehicle detection, a custom vehicle neural network called YOLOv7-vehicles-0.1 was trained using a unique vehicle dataset comprising COCO, KITTI, and OpenImages datasets. Additionally, a novel tracker named BYTECounter was developed, using two virtual drawing objects, Regions and Lines, to count vehicles' turning movements based on trajectory information. The performance of our system was evaluated using the UA-DETRAC dataset, achieving an overall Movement Counting Accuracy (MCAcc) of 93.5% with a processing rate of 82.86 frames per second. This paper demonstrates that BYTECounter outperforms the state-of-the-art tracker, BYTE, and highlights the superiority, in terms of counting metrics, of YOLOv7-vehicles-0.1 over YOLOv7 models trained on the COCO dataset.

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Robust Control Design for the Mass-Damper Type Anti-Sway System

Robust Control Design for the Mass-Damper Type Anti-Sway Sys...

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SICE Annual Conference

作者： Young-bok Kim Masao Ikeda Jin-ho Suh Guisheng Zhai Seong-hoon Han Faculty of Mechanical Engineering Pukyong National University Busan South Korea Department of Computer-Controlled Mechanical Systems Osaka University Osaka Japan Pohang Institute of Intelligent Robotics PIRO Pohang South Korea Department of Mechanical Engineering Osaka Prefecture University Osaka Japan Department of Electrical Engineering Graduate School Dong-A University Busan South Korea

In the previous studies, we suggested a new type of swing motion control system for the crane system in which a small auxiliary mass is installed on the spreader. The actuator reacting against the auxiliary mass applies inertial control forces to the spreader of the container crane to reduce the swing motion in the desired manner. In the papers, to identify the usefulness and validity of the proposed system, we applied several control strategies. In the results, good control performance and valuable results had been achieved. In this paper, we evaluate and check the system in the point of more real application. For an example, the transportation process of RTG to handle the cargos is considered. In this case, undesirable sway motion might be occurred. To suppress this motion, a pursueable strategy (approach) is introduced. And, it will be shown that the proposed control strategy is useful and it can be easily applicable to the real world

关键词： Robust control Cranes Motion control Containers Control systems Mechanical engineering Intelligent robots Marine vehicles Transportation Mechanical systems

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Rewinding Strategy Aided by FEA as a Solution to Increase Efficiency of Industrial Motors

Rewinding Strategy Aided by FEA as a Solution to Increase Ef...

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International Conference on electrical Machines

作者： V. P. B. Aguiar R. S. T. Pontes T. R. Fernandes Neto F. J. T. E. Ferreira Motor Driven Systems Laboratory Department of Electrical Engineering Federal University of Ceará Fortaleza CE 60440554 Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9781509025398

This paper presents a proper model and methodology to improve the efficiency class of a squirrel-cage induction motor during rewinding. The modeling approach is based on the finite element model, i.e. two-dimensional model coupled with the rotor equivalent circuit (first stage) and the stator equivalent circuit (second stage). Results obtained with finite element analysis are compared with those obtained with analytical calculations. It is demonstrated that, during rewinding, if an improved winding configuration is used, efficiency gains can be obtained, allowing to improve the efficiency class of the motor, from IE2 to IE3, respecting the specified limits of NEMA & IEC performance indicators, namely, breakdown torque, locked-rotor torque, and locked-rotor current. In 79% of the simulated cases the efficiency gain allowed the studied motor to reach the IE3 class.

关键词： Finite element analysis Equivalent circuits IEEE standards IEC standards ANSI standards Stator Induction motors Electromagnetic modeling Frequency-domain analysis

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Multispectral Vineyard Segmentation: A Deep Learning Comparison Study

arXiv

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arXiv 2021年

作者： Barros, Tiago Conde, P. Gonçalves, G. Premebida, C. Monteiro, M. Ferreira, C.S.S. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal University of Coimbra Institute for Systems Engineering and Computers at Coimbra Department of Mathematics Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Bolin Centre for Climate Research Department of Physical Geography Stockholm University Stockholm Sweden Navarino Environmental Observatory Navarino Dunes Messinia Greece

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://***/Cybonic/***. © 2021, CC BY.

关键词： Precision agriculture

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DECOMPOSITION OF INDUSTRIAL systems FOR ENERGY EFFICIENCY OPTIMIZATION WITH OPTTOPO

arXiv

引用

arXiv 2022年

作者： Thiele, Gregor Johanni, Theresa Sommer, David Krüger, Jörg Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Institute for Machine Tools and Factory Management Technical University of Berlin Berlin Germany

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system. © 2022, CC BY.

关键词： Optimization

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Automatic face region tracking for highly accurate face recognition in unconstrained environments

Automatic face region tracking for highly accurate face reco...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Y.-O. Kim J. Paik Jingu Heo A. Koschan B. Abidi M. Abidi Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Image Processing Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville USA

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

关键词： Face recognition Surveillance Real time systems Humans Cameras Intelligent systems Security Image quality Layout Low pass filters

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