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IEEE Annual Conference on Decision and Control

作者： Changming Cheng Er-wei Bai State Key Laboratory of Mechanical System and Vibration Shanghai Jiaotong University Shanghai China Dept. of Electrical and Computer Engineering University of Iowa Iowa City Iowa

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

The importance of discovering significant variables from a large candidate pool is now widely recognized in many fields. There exist a number of algorithms for variable selections in the literature. Some are computationally efficient but only provide a necessary condition, not a sufficient and necessary condition, for testing if a variable contributes or not. The others are computationally expense. The goal of the paper is to develop a directional variable selection algorithm that performs similar to or better than the leading algorithms for variable selections, but under weaker technical assumptions and with a much reduced computational complexity.

关键词： Input variables Random variables Testing Computational complexity Machine learning algorithms Mutual information Machine learning

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Finger Movement Regression with Myoelectric Signal and Deep Neural Network

Finger Movement Regression with Myoelectric Signal and Deep ...

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International Conference on computer Science, Information Technology, and Electrical engineering (ICOMITEE)

作者： Khairul Anam Dwiretno Istiyadi Swasono Aris Zainul Muttaqin Faruq Sandi Hanggara Department of Electrical Engineering University of Jember Jember Indonesia Department of Computer Engineering University of Jember Jember Indonesia Department of Mechanical Engineering University of Jember Jember Indonesia Intelligent System and Robotics Lab University of Jember Jember Indonesia

Research on electromyographic (EMG) signals is intensively carried out to help disabled people to control prosthetic hands. Neural Networks have been widely used in research on the classification of finger movements using EMG. The study of a classification system generally still works on a limited number of movements, even though the human body, especially fingers, has a nearly unlimited combination of movements to help do daily activities. To overcome this, a proportional control system is needed. In its recent development, research on myoelectric control using EMG devices is still in a laboratory environment. Hence, the results obtained in a clinical setting are often different. However, along with technological developments, the emergence of affordable and wearable commercial EMG devices such as Myo Armband, has encouraged this study to develop control systems of prosthetic fingers using regression. One of many options available is neural networks that have been widely used in various fields. By estimating each joint with a different neural network, the result shows the predicted is fitted to the actual angle with R 2 as high as 99%.

关键词： Thumb Electromyography Biological neural networks Feature extraction Muscles

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Application of Data Mining Prediction of Electricity Deviation Flow Using Metode Backpropogation at PLN Binjai Area 1

Application of Data Mining Prediction of Electricity Deviati...

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1st Workshop on Environmental Science, Society, and Technology, WESTECH 2018

作者： Pardede, A.M.H. Andrayani, Febi Wahyu Nurdin Abdullah, Dahlan Erliana, Cut Ita Dana, Raditya Danar Dikananda, Arif Rinaldi Fasya, Firmanda Rahaningsih, Nining Andriani, Meri Subekti, Purwo Elisa, Erlin Sohibun Noor Hasan Siregar, Muhammad Ponidi Winarno, Edy Ketut Sudarsana, I. College of Information and Computer Management Kaputama Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Departement of Information Technology STMIK IKMI Cirebon Indonesia Departement of Accounting Information System STMIK IKMI Cirebon Indonesia Department of Industrial Engineering Universitas Samudra Langsa Indonesia Department of Mechanical Engineering Universitas Pasir Pengaraian Riau Indonesia Department of Information System University of Putera Batam Batam Indonesia Department of Physics Educational Universitas Pasir Pengaraian Riau Indonesia Departement of Management Universitas Graha Nusantara Indonesia Department of Mechanical Engineering Universitas Muhammadiyah Surabaya Surabaya Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Indonesia

PLN is the only state-owned electricity company that provides and regulates electricity to the public. However, in the provision of electricity to every community, there are many frauds or irregularities in utilization of electricity by irresponsible parties was found by PLN. Electricity deviation caused by irresponsible parties, like the theft of electricity can have a tremendous impact due to loss of electrical energy. Electricity Deviation, using Electricity Deviation data for 3 years, from 2012 to 2014. Sales data processed by the network, in the form of amount of Kwh deviation per month. The data needed in the process of analysis of this Neural Network is pure database results by taking the input variable in the form of Amount of Kwh Deviation and Amount of Kwh Deviation for the next Month for the following year as output. Electricity for 3 years is processed into Matlab using GUI facilities, to produce information on the predicted results of the Kwh Deviation in November 2014 to March, May, June, August, November, December 2015 experiencing has increase in Kwh, while in April July, September, and Okrober 2015 decreased. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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A workload adaptive haptic shared control scheme for semi-autonomous driving

arXiv

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arXiv 2020年

作者： Luo, Ruikun Weng, Yifan Wang, Yifan Jayakumar, Paramsothy Brudnak, Mark J. Paul, Victor Desaraju, Vishnu R. Stein, Jeffrey L. Ersal, Tulga Jessie Yang, X. Robotics Institute University of Michigan Ann ArborMI United States Department of Mechanical Engineering University of Michigan Ann ArborMI United States Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States U.S. Army Ground Vehicles System Center WarrenMI United States Toyota Research Institute Ann ArborMI United States Department of Industrial and Operations Engineering University of Michigan Ann ArborMI United States

Haptic shared control is used to manage the control authority allocation between a human and an autonomous agent in semi-autonomous driving. Existing haptic shared control schemes, however, do not take full consideration of the human agent. To fill this research gap, this study presents a haptic shared control scheme that adapts to a human operator’s workload, eyes on road and input torque in real-time. We conducted human-in-the-loop experiments with 24 participants. In the experiment, a human operator and an autonomy module for navigation shared the control of a simulated notional High Mobility Multipurpose Wheeled Vehicle (HMMWV) at a fixed speed. At the same time, the human operator performed a target detection task for surveillance. The autonomy could be either adaptive or non-adaptive to the above-mentioned human factors. Results indicate that the adaptive haptic control scheme resulted in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optimal Kernel Classifier in Mobile Robots for Determining Gases Type

Optimal Kernel Classifier in Mobile Robots for Determining G...

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2018 International Conference on Electrical engineering and computer Science, ICECOS 2018

作者： Husni, Nyayu Latifah Muhaajir, M Al Prihatini, Ekawati Silvia, Ade Nurmaini, Siti Yani, Irsyadi Electrical Engineering Politeknik Negeri Sriwijaya Palembang Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Mechanical Engineering Department Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9781538657201

The use of TGS sensor and Arduino could create a robot to be capable of detecting and classifying some gases. In this research, 3 kinds of SVM Kernel Classifiers are investigated. Robots equipped with 3 TGS sensors are used to classify methanol and acetone. Xbee modul is used as a communication medium between robots and server. The robots are run in the experimental environment. When they detect the gas, they will get closer to the source and classify the gas type. The classified gas data is then sent to the server. From this research, it can concluded that polynomial and RBF have better performance in classifying methanol and acetone. © 2018 IEEE.

关键词： Methanol

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Optimal Gas Sensors Arrangement in Odor Searching Robot

Optimal Gas Sensors Arrangement in Odor Searching Robot

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2018 International Conference on Electrical engineering and computer Science, ICECOS 2018

作者： Husni, Nyayu Latifah Silvia, Ade Nurmaini, Siti Yani, Rsyadi Electrical Engineering Politeknik Negeri Sriwijaya Palembang Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Mechanical Engineering Department Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9781538657201

This paper presents an analysis of an optimal sensor arrangement in Odor Searching Robot (OSR). 5 gas sensors integrated in OSR can help the OSR to navigate to the source. Since low cost, low computation and robust robot is preferred in swarm robot application, the OSR, as an individual robot of swarm in this study, is designed to be able to switch into the mode 3 or the mode 5 in order to analyze the optimal distance of the gas sensors arrangement that can be integrated in the OSR. By knowing the optimal sensor arrangement, the low cost and or the low computation OSR can be established. Algorithms of fuzzy logic for 3 and 5 gas sensors are tested in open environment. The concentration of gas is used as the input of the fuzzy logic. The robot uses the concentration, as its parameters in determining which way that it should take. From this research, it can be concluded that there was no significant difference between using 3 gas sensors or 5 gas sensors. © 2018 IEEE.

关键词： Mobile robots

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LIC-Fusion: LiDAR-Inertial-Camera Odometry

LIC-Fusion: LiDAR-Inertial-Camera Odometry

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xingxing Zuo Patrick Geneva Woosik Lee Yong Liu Guoquan Huang Institute of Cyber-System and Control Zhejiang University Hangzhou China Department of Computer and Information Sciences University of Delaware Newark DE USA Department of Mechanical Engineering University of Delaware Newark DE USA

This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Lei An Jiajia Zhao Huijun Di School of Computer Science & Technology Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset.

关键词： Image translation Infrared image Generative adversarial networks Infrared imaging Visible Imagery images Infrared radiation Information fusion

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Sensitivity analysis of linear programming optimization of a manufacturing business

Sensitivity analysis of linear programming optimization of a...

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2018 World Congress on engineering and computer Science, WCECS 2018

作者： Agarana, Michael C. Omogbadegun, Z.O. Bishop, S. Mark, O. Covenant University Nigeria Department of Mechanical Engineering Science University of Johannesburg South Africa Mathematics Department Department of computer science and management information System Covenant University Nigeria

ISBN: (纸本)9789881404909

Approximations are usually made when Mathematical model is used to describe reality. A major challenge encountered by most business enterprises/ companies is how value change effect can be determined on optimal decisions. Linearity assumptions usually are significant approximations. Another important approximation comes because one cannot be sure of the data that one puts into the model. Knowledge of the relevant technology may be imprecise, forcing one to approximate values;moreover, information may change. In this paper the sensitivity analysis of the optimal solution of linear programming model of a business enterprise is investigated. computer software - LINDO, was used to solve the resulting Linear programming model formed from a real-life business situation. The sensitivity analysis carried out on the optimal solutions obtained, show that an addition of a new type of plastic in the production line improved the optimal profit. A reduction in the time resources used in producing type B plastic reduced the optimal profit. © 2018 Newswood Limited. All rights reserved.

关键词： Sensitivity analysis

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