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Characterizing Coated Objects with Radar Identification System using Short-time Matrix Pencil Method

Characterizing Coated Objects with Radar Identification Syst...

International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Lakkhana Bannawat Santana Burintramart Akkarat Boonpoonga Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Thailand Nakhon Pathom Defence Technology Institute (Public Organization) Ministry of Defence Nonthaburi Thailand Industrial Electric and Control System Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

This paper presents a radar target identification of a coated object. Based on the singularity expansion method, poles extracted from the received backscattering signal by using a short-time matrix pencil method were used as the object's signatures for identification. Simulation examples were illustrated in order to study on identification of a coated object. The antenna effect was taken into account and then mitigated by using a calibration technique. In simulations, the PEC sphere object was coated with dielectrics whose thickness was varied in order to investigate the behavior of extracted pole and the commencements of the late-time response of each object's layer. Simulation results showed that extracted poles obtained from the use of a short-time matrix pencil method can identify the PEC object coated with the dielectrics.

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ONLINE REVENUE MAXIMIZATION FOR SERVER PRICING

arXiv

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arXiv 2019年

作者： Boodaghians, Shant Fusco, Federico Leonardi, Stefano Mansour, Yishay Mehta, Ruta University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Control and Management Engineering Sapienza University Rome Italy Tel Aviv University P.O. Box 39040 Tel Aviv6997801 Israel

Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic, depending only on the length of the allotted time interval and on the earliest time the server is available. We also prove that the proposed pricing strategy is robust to imprecise knowledge of the job distribution and that a distribution learned from polynomially many samples is sufficient to obtain a near-optimal truthful pricing strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Verification code recognition based on active and deep learning

arXiv

引用

arXiv 2019年

作者： Xu, Dongliang Wang, Bailing Du, XiaoJiang Zhu, Xiaoyan Guan, Zhitao Yu, Xiaoyan Liu, Jingyu School of Computer Science and Technology Shandong University Weihai China School of Computer Science and Technology Harbin Institute of Technology Weihai China Department of Computer and Information Sciences Temple University PhiladelphiaPA United States Computer information center Xidian University Xi'an China School of Control and Computer Engineering North China Electric Power University Beijing China Dept. of Computer Science and Technology Capital Normal University Beijing China Computer information center Intermediate People's Court Weihai China

A verification code is an automated test method used to distinguish between humans and computers. Humans can easily identify verification codes, whereas machines cannot. With the development of convolutional neural networks, automatically recognizing a verification code is now possible for machines. However, the advantages of convolutional neural networks depend on the data used by the training classifier, particularly the size of the training set. Therefore, identifying a verification code using a convolutional neural network is difficult when training data are insufficient. This study proposes an active and deep learning strategy to obtain new training data on a special verification code set without manual intervention. A feature learning model for a scene with less training data is presented in this work, and the verification code is identified by the designed convolutional neural network. Experiments show that the method can considerably improve the recognition accuracy of a neural network when the amount of initial training data is small. Copyright © 2019, The Authors. All rights reserved.

关键词： Testing

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Solving Lur'e equations through zeroing neural networks

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Information Sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

关键词： Dynamic system Linear-quadratic optimal control Lur'e equations Singular optimal control Zeroing neural network

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Scaling associative classification for very large datasets

引用

Journal of Big Data 2017年第1期4卷 1页

作者： Venturini, Luca Baralis, Elena Garza, Paolo Department of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Torino Italy

Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datasets with a number of large-domain categorical features are a difficult challenge for any classifier. Most off-the-shelf solutions cannot cope with this problem. In this work we introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. Furthermore, it adopts several novel techniques to reach high scalability without sacrificing quality, among which a preventive pruning of classification rules in the extraction phase based on Gini impurity. We ran experiments on Apache Spark, on a real large-scale dataset with more than 4 billion records and 800 million distinct categories. The results showed that DAC improves on a state-of-the-art solution in both prediction quality and execution time. Since the generated model is human-readable, it can not only classify new records, but also allow understanding both the logic behind the prediction and the properties of the model, becoming a useful aid for decision makers. © 2017, The Author(s).

关键词： Big data

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Modeling the temporal population distribution of ae. aegypti mosquito using big earth observation data.

TechRxiv

引用

TechRxiv 2019年

作者： Mudele, Oladimeji Bayer, Fábio M. Zanandrez, Lucas Eiras, Alvaro E. Gamba, Paolo Telecommunications and Remote Sensing Laboratory Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Statistics and LACESM Federal University of Santa Maria Brazil Ecovec company Belo Horizonte Brazil Laboratory of Technological Innovation and Entrepreneurship in Vector Control Department of Parasitology Federal University of Minas Gerais Brazil

Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be e

关键词： Vectors

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A New FEM-based Brain Tissue Model for Neurosurgical Simulation Using the Optimization Implicit Euler Method

A New FEM-based Brain Tissue Model for Neurosurgical Simulat...

引用

第37届中国控制会议

作者： Wenguo Hou Peter X.Liu Minhua Zheng Shichao Liu School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University Department of System and Computer Engineering Carleton University

An accurate and computationally efficient model for the deformation of brain tissue is very important in virtual neurosurgical simulation. In this paper, we introduced a new Finite Element Method(FEM) model, which is based on optimization implicit Euler method, for brain tissue deformation. Specifically, both the anisotropic and viscoelastic properties of brain tissue are incorporated into the model, providing more realistic and accurate description of the mechanical features of brain tissue. In the meantime, the model is particularly suitable for GPU-based computing, making it possible to achieve real-time performance for neurosurgical simulation. Simulation results show that the deformation model exhibits the behaviors of anisotropy and viscoelasticity. The proposed model was implemented on a neurosurgical simulator and it showed that the deformation of brain tissue can be rendered with a relatively high degree of visual realism at a refreshment rate of 23 frames per second in a normal PC.

关键词： Brain Tissue Deformation Model Neurosurgical Simulation Optimization Implicit Euler

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Modeling the temporal population distribution of Ae. aegypti mosquito using big earth observation data

arXiv

引用

arXiv 2019年

作者： Mudele, Oladimeji Bayer, Fábio M. Zanandrez, Lucas Eiras, Alvaro E. Gamba, Paolo Telecommunications and Remote Sensing Laboratory Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Statistics and LACESM Federal University of Santa Maria Brazil Ecovec company Belo Horizonte Brazil Laboratory of Technological Innovation Entrepreneurship in Vector Control Department of Parasitology Federal University of Minas Gerais Brazil

关键词： Vectors

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Method for pests detecting in stored grain based on spectral residual saliency edge detection

引用

Grain & Oil Science and Technology 2019年第2期2卷 33-38页

作者： Yao Qin Yanli Wu Qifu Wang Suping Yu College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China Key Laboratory of Grain Information Processing and Control Henan University of TechnologyMinistry of EducationZhengzhou 450001China Henan Academy of Science Applied Physics Institute Co.LtdZhengzhou 450001China Luoyang Institute of Science and Technology Department of Computer and Information EngineeringLuoyang 471000China

Pests detecting is an important research subject in grain storage *** the past decades,many edge detection methods have been applied to the edge detection of stored grain *** some of them can realize the stored grain pests detecting,precision and robustness are not good *** residual(SR)saliency edge detection defines the logarithmic spectrumof image as novelty part of the image *** remaining spectrumis converted to the airspace to obtain edge detection *** algorithm is completely based on frequency domain *** not only can effectively simplify the target detection algorithm,but also can improve the effectiveness of target *** experimental results show that the edge results of stored grain pests detected by SR method are effective and stable.

关键词： Stored grain pests Saliency detection Spectral residual (SR) Edge detection

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A Situation-Aware Collision Avoidance Strategy for Car-Following

引用

IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 1012-1016页

作者： Li Li Fellow IEEE Xinyu Peng Fei-Yue Wang ieee department of automation bnristtsinghua universityBeijing l00084China the department of automation tsinghua universityBeijing l00084China state key laboratory of management and control for complex systems institute of automationchinese academy of sciencesBeijing 100080China the driver cognition and automated driving laboratory university of waterlooWaterloo N2L 3GlCanada the department of electrical and computer engineering purdue school of engineering and technologyindiana university-purdue university indianapolisIndiana 46202-5l32USA

Abstract--In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position/speed perception/measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy. Index Terms--Collision avoidance, safety, traffic efficiency, uncertainty.

关键词： Collision avoidance safety traffic efficiency uncertainty

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