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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Tsukasa Aso Hiroki Oke Teiji Nishio Department of Electronics and Computer Engineering National Institute of Technology Toyama College Japan Control Information System Engineering Program Advanced Course National Institute of Technology Toyama College Japan Department of Medical Physics Tokyo Women’s Medical University Japan

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Range verification of therapeutic proton beam is crucial in proton therapy to confirm the irradiation field for ensuring effects and safety of the treatment. The irradiation field reconstruction using prompt gamma-rays generated from excited nuclides by proton beam has being studied for the purpose of precision medicine. A simulation tool based on fundamental physics processes employs an important role to understand intermediate relationship between physics interactions and detected observables, and to optimize a detector system with its analysis method. Previously, we reported on the simulation study about an irradiation field reconstruction by using prompt gamma-rays of whole energy range. In this study, we analyzed the energy spectrum and reconstructed the image of irradiation field by using prompt gamma-rays corresponding to a particular energy peak. The simulation was performed for proton beam with a target placed at the isocenter by using Geant4 based particle therapy system simulation framework. A simple tubular detector was placed around the target, in which the kinetic energy and the detected position of each gamma-ray were recorded. The irradiation field was then reconstructed in the ordered subset expectation maximization method. The reconstructed irradiation fields were compared with the dose distribution in the target. The peak positions of these depth distributions were compared each other and discussed about the range verification of therapeutic proton beam.

关键词： Image reconstruction Radiation effects Gamma-rays Particle beams Medical treatment Detectors

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Anti-PT -symmetry-broken nonreciprocity in a linear resonator

arXiv

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arXiv 2020年

作者： Zhang, Huilai Huang, Ran Zhang, Sheng-Dian Li, Ying Qiu, Cheng-Wei Nori, Franco Jing, Hui Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama351-0198 Japan Physics Department University of Michigan Ann ArborMI48109-1040 United States

Entirely real eigenvalues emerge for non-Hermitian systems if their Hamiltonians are symmetric or antisymmetric under the parity-time (PT ) operations. This fact has led to many unconventional discoveries such as loss-induced lasing, sensor response enhancement and topological energy transfer. One merit of anti-PT systems is free of any gain, but nonlinear medium is still required in recent efforts on making and utilizing anti-PT devices. Here, counterintuitively, we propose to achieve a linear anti-PT device by combining dissipative optical coupling and the Sagnac frequency shifts of counterpropagating lights in a spinning resonator. Anti-PT symmetry and its spontaneous breaking can be well controlled by tuning the spinning velocity of the resonator. Compared with its Hermitian counterpart, 6 times enhancement of optical isolation and 2 orders enhancement of sensing response can be achieved by breaking the anti-PT symmetry. Configured as a new platform to study enhanced light-matter interactions, anti-PT resonators can also be used in such a wide range of fields as anti-PT lasers, anti-PT gyroscopes and anti-PT topological or chiral photonics. Copyright © 2020, The Authors. All rights reserved.

关键词： Resonators

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A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming

A Robust and Efficient Algorithm for Coprime Array Adaptive ...

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作者： Zhou, Chengwei Gu, Yujie He, Shibo Shi, Zhiguo College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Temple University PhiladelphiaPA19122 United States State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou Zhejiang University 310027 China

Coprime array offers a larger array aperture than uniform linear array with the same number of physical sensors, and has a better spatial resolution with increased degrees of freedom. However, when it comes to the problem of adaptive beamforming, the existing adaptive beamforming algorithms designed for the general array cannot take full advantage of coprime feature offered by the coprime array. In this paper, we propose a novel coprime array adaptive beamforming algorithm, where both robustness and efficiency are well balanced. Specifically, we first decompose the coprime array into a pair of sparse uniform linear subarrays and process their received signals separately. According to the property of coprime integers, the direction-of-arrival (DOA) can be uniquely estimated for each source by matching the super-resolution spatial spectra of the pair of sparse uniform linear subarrays. Further, a joint covariance matrix optimization problem is formulated to estimate the power of each source. The estimated DOAs and their corresponding power are utilized to reconstruct the interference-plus-noise covariance matrix and estimate the signal steering vector. Theoretical analyses are presented in terms of robustness and efficiency, and simulation results demonstrate the effectiveness of the proposed coprime array adaptive beamforming algorithm. © 1967-2012 IEEE.

关键词： Covariance matrix

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Deep learning for signal demodulation in physical layer wireless communications: Prototype platform, open dataset, and analytics

arXiv

引用

arXiv 2019年

作者： Wang, Hongmei Wu, Zhenzhen Ma, Shuai Lu, Songtao Zhang, Han Ding, Guoru Li, Shiyin Information and Control Engineering China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of California DavisCA95616 United States College of Communications Engineering Army Engineering University Nanjing210007 China

In this paper, we investigate deep learning (DL)- enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBNSVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the k-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator. Copyright © 2019, The Authors. All rights reserved.

关键词： Demodulators

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The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

arXiv

引用

arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

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Distributed estimation and control of node centrality in undirected asymmetric networks

arXiv

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arXiv 2019年

作者： Montijano, Eduardo Oliva, Gabriele Gasparri, Andrea Universidad de Zaragoza Zaragoza Spain Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Computer Science and Automation University of "Roma Tre" Rome Italy

Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problems of distributed estimation and control of node centrality in undirected graphs with asymmetric weight values. In particular, we focus our attention on α-centrality, which can be seen as a generalization of eigenvector centrality. In this setting, we first consider a distributed protocol where agents compute their α-centrality, focusing on the convergence properties of the method;then, we combine the estimation method with a consensus algorithm to achieve a consensus value weighted by the influence of each node in the network. Finally, we formulate an α-centrality control problem which is naturally decoupled and, thus, suitable for a distributed setting and we apply this formulation to protect the most valuable nodes in a network against a targeted attack, by making every node in the network equally important in terms of α-centrality. Simulations results are provided to corroborate the theoretical findings. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A Lyapunov approach to stability analysis of partial synchronization in delay-coupled networks⁎

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IFAC-PapersOnLine 2018年第33期51卷 198-204页

作者： Su, Libo Wei, Yanling Michiels, Wim Steur, Erik Nijmeijer, Henk Department of Computer Science KU Leuven Celestijnenlaan 200A box 2402 Leuven3001 Belgium Delft Center for Systems and Control Mekelweg 2 DelftCD2628 Netherlands Department of Mechanical Engineering P.O. Box 513 EindhovenMB5600 Netherlands

Networks of interconnected dynamical systems may exhibit a so-called partial synchronization phenomenon, which refers to synchronous behaviors of some but not all of the systems. The patterns of partial synchronization are often characterized by partial synchronization manifolds, which are linear invariant subspace of the state space of the network dynamics. Here, we propose a Lyapunov-Krasovskii approach to analyze the stability of partial synchronization manifolds in delay-coupled networks. First, the synchronization error dynamics are isolated from the network dynamics in a systematic way. Second, we use a parameter-dependent Lyapunov-Krasovskii functional to assess the local stability of the manifold, by employing techniques originally developed for linear parameter-varying (LPV) time-delay systems. The stability conditions are formulated in the form of linear matrix inequalities (LMIs) which can be solved by several available tools. © 2018

关键词： Linear matrix inequalities Delay control systems Dynamical systems Dynamics Linear systems Lyapunov functions Synchronization Time delay Linear Matrix Inequalities (LMIs) Linear parameter varying Linear parameter varying systems Lyapunov Krasovskii approach Lyapunov Krasovskii functionals Partial synchronization Synchronization error Time delay systems

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An integral sliding mode control for the magnetic levitation system based on backstepping approach

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IOP Conference Series: Materials Science and engineering 2020年第1期881卷

作者： Shibly Ahmed Al-Samarraie Ivan Isho Gorial Mustafa Hussein Mshari Assistant Prof. Control & Systems Engineering Department University of Technology Baghdad Iraq Lecturer Control & Systems Engineering Department University of Technology Baghdad Iraq Computer Eng. Dept. AL-Esraa University College Baghdad-Iraq

Due to their reduced maintenance costs, increased power efficiency and reduced power consumption, the Magnetic Levitation (Maglev) system make a significant contribution to the industrial application. Maglev's production of electricity (e.g. wind turbines), maglev trains and medical devices (e.g. artificial heart pump magnetically suspended) are typical applications. This paper suggests designing a nonlinear control for the Maglev system model which represented by a third-order model consists of the mechanical (ball position and velocity) and electrical (the current) subsystems. The controller is designed utilizing the Integral Sliding Mode control (ISMC) and based on the Backstepping approach. The tracking accuracy of the ball position to the desired reference is determined by computing the ultimate boundedness as a function to the controller parameters and that using the Lyapunov function. The numerical simulation results showed the robustness and the efficiency of the proposed controller where the tracking error limited by the computed bound.

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A study for Motion-Planning Method Resident-tracking Robot based on Reinforcement Learning 30

A study for Motion-Planning Method Resident-tracking Robot b...

引用

30th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2019

作者： Sugimoto, Masashi Yoshioka, Takashi Ishii, Kohei Nonaka, Shogo Deguchi, Mikio Tsuzuki, Shinji Hiran, Masatsugu Electrical and Electronic Engineering Course Ehime University Division of Electrical and Electronic Engineering and Computer Science 3 Bunkyo-cho Matsuyama City Ehime Pref.790-8577 Japan National Institute of Technology Kagawa College Department of Electrical and Computer Engineering 355 Chokushi-cho Takamatsu City Kagawa Pref.761-8058 Japan National Institute of Technology Kagawa College. Department of Electro-Mechanical Systems Engineering 355 Chokushi-cho Takamatsu City Kagawa Pref.761-8058 Japan National Institute of Technology Tsuyama College Department of Integrated Science and Technology 624-1 Numa Tsuyama City Okayama Pref.790-8577 Japan National Institute of Technology Niihama College Department of Electronics and Control Engineering 7-1 Yakumo-cho Niihama City Ehime Pref.790-8580 Japan National Institute of Technology Niihama College Department of Electrical Engineering and Information Science 7-1 Yakumo-cho Niihama City Ehime Pref.792-8580 Japan

ISBN: (纸本)9781728126746

Among the advanced countries, especially in Japan, we are facing on the decreasing birthrate and aging population ahead of the world. In recent, population of single-living elder, and elderly people have been taking care by same-aged person, have been actualized. Unfortunately, the population of caregivers is decreasing according to year. In this situation, the Elderly Person Watching System has been focused on. Especially, Robotics, Internet of Things, and Artificial Intelligence technology have been having high affinity with this system. In this study, firstly, the person watching robot system will be developed. In detail, in this paper, the flexible-analyze system will be focused on. In the algorithm, a resident-tracking task and an energy-saving task are holding. The whole tasks are working based on Reinforcement Learning. Thus, the behavior algorithm of the person watching robot system will be developed. In detail, in this paper, the flexible-analyze system will be focused on. In the algorithm, a resident-tracking task and an energy-saving task are holding. From the verification experiment, the simulation results showed the proposed method has been acquired actions to switch the desired task. © 2019 IEEE.

关键词： Reinforcement learning

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Power Grid Fault Diagnosis Method Based on Stacked Sparse Denoising Auto-Encoder and GRU Network

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Ling Zheng Panfei Yan Fei Chen Department of Computer Technology Faculty of Control and Computer Engineering North China Electric Power University No.2 Beinong Road Huilongguan Town Changping District Beijing China

This research aims to solve the problems of feature extraction, data dimension reduction, gradient dissipation and low training efficiency in power grid fault classification. Aiming at PMU measurement data with high dimensionality and strong real-time characteristics, a fault diagnosis method based on Stacked Sparse Denoising Auto-Encoder and GRU network is proposed. The Stacked Sparse Denoising Auto-Encoder is used to reduce the sequence dimension to obtain the sparse feature expression of the data, and then the time-dependent features of the data extracted by the GRU are used to obtain the fault type. Simulations and experiments show that compared with the traditional neural network algorithm, the proposed method can effectively extract high-dimensional data features, reduce data dimensions, improve the efficiency of GRU network classification, accelerate the convergence speed and reduce the training time, and has better stability and higher accuracy.

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