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Current reversal and particle separation in Brownian transport

Physical Review Research 2021年第3期3卷 033162-033162页

作者： Hai-Wei Hu Lin Du Liang-Hui Qu Zi-Lu Cao Zi-Chen Deng Ying-Cheng Lai School of Mathematics and Statistics Northwestern Polytechnical University Xi'an 710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an 710072 China School of Aeronautics Northwestern Polytechnical University Xi'an 710072 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

In transport of micro- or nanosized particles through a confined structure driven by thermal fluctuations and external forcing—a situation that arises commonly in a variety of fields in physical and biological sciences, efficient and controllable separation of particles of different sizes is an important but challenging problem. We study, numerically and analytically, the diffusion dynamics of Brownian particles through the biologically relevant setting of a spatially periodic structure, subject to static and temporally periodic forcing. Molecular dynamical simulations reveal that the mean velocity in general depends sensitively on the particle size. The phenomenon of current reversal is uncovered, where particles larger than or smaller than a critical size diffuse in exactly opposite directions. This striking behavior occurs in a wide range of the forcing amplitude and provides a mechanism to separate the Brownian particles of different sizes. Besides the forcing amplitude, other parametric quantities characterizing the forcing profile, such as the temporal asymmetry, can also be exploited to modulate or control the transport dynamics of particles of different sizes. To gain a theoretical understanding, we exploit the Fick-Jacobs approximation to obtain a one-dimensional description of the diffusion problem, which enables key quantities characterizing the diffusion process, such as the mean velocity, to be predicted. In the regime of weak forcing, a reasonable agreement between theory and numerical results is achieved. Beyond the weakly forcing regime, the diffusion approximation breaks down, causing the theoretical predictions to deviate from the numerical results, into which we provide physical insights. Our findings have potential applications in optimizing transport in microfluidic devices or through biological channels.

关键词： Brownian motion Classical transport Brownian dynamics Langevin equation Stochastic differential equations

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Gene Expression Data for Gene Selection Using Ensemble Based Feature Selection 9

Gene Expression Data for Gene Selection Using Ensemble Based...

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9th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2019

作者： Aouf, Mohamad Sharawi, Amr Samir, Khaled Almotatiri, Sultan Bajahzar, Abdulla Kareem, Ghada Higher Technological Institute 10th of Ramadan City Systems and Biomedical Engineering Department Cairo Egypt Faculty of Engineering Cairo Univ. Systems and Biomedical Engineering Department Cairo Egypt Faculty of Community College Majmaah University Natural and Applied Sciences Department Zulfi11932 Saudi Arabia Computer and Control Engineering Department Higher Technological Institute 10th of Ramadan City Cairo Egypt

ISBN: (纸本)9781728139951

The technology of next generation sequencing brought about evolution in research that based on sequence which replaces the microarray due to its advantages. The RNA-Seq is a high-throughput gene expression data that uses NGS technologies. The problem of high-throughput RNA-Seq datasets is the high of dimensionally (variables > observations) that need to reduce their dimensions to predict and classify. In this work, ensemble-based on the approach of selection the feature is proposed to choose small optimal genes from various RNA-Seq cancer datasets. The approach combines two filters to select the features (SNR and t-test). In addition, SVM-RFE is used as an embedded feature selection. The SVM classifier used for validating and testing the selected genes using the accuracy measure. The results show the ability to select small optimal genes with high accuracy. Consequently, the genes which are selected will be used as biomarkers to diagnose cancer. © 2019 IEEE.

关键词： RNA

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Compositional Visible Bisimulation Abstraction Applied to Opacity Verification

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IFAC-PapersOnLine 2018年第7期51卷 434-441页

作者： Noori-Hosseini, Mona Lennartson, Bengt Hadjicostis, Christoforos Division of Systems and Control Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In this paper, an alternative equivalence based definition of bisimulation is proposed, called visible bisimulation equivalence. It includes both state and transition labels and therefore unifies stuttering and branching bisimulation. Furthermore, it is equivalent to a temporal logic called ECTL* where CTL* is extended with events. The presented bisimulation abstraction is applied to a set of synchronized submodels, where local events are identified incrementally and abstracted after each synchronization. Since the bisimulation reduction is applied after each synchronization, a significant part of the state space explosion in ordinary synchronization is avoided. This compositional abstraction is used for opacity verification, where it is shown that local observers can be generated before they are synchronized, a key factor to be able to apply compositional opacity verification. The efficiency of this method is illustrated on a modular opacity problem with mutual exclusion of moving agents. © 2018

关键词： Temporal logic Abstracting computer circuits Opacity Synchronization abstraction Bisimulations Logic verification Modular system Transition system

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Prediction of fitness in bacteria with causal jump dynamic mode decomposition

Prediction of fitness in bacteria with causal jump dynamic m...

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American control Conference

作者： Shara Balakrishnan Aqib Hasnain Nibodh Boddupalli Dennis M. Joshy Robert G. Egbert Enoch Yeung Department of Electrical and Computer Engineering University of California Santa Barbara Department of Mechanical Engineering University of California Santa Barbara Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory Richland WA Mechanical Engineering Center for Control Dynamical Systems and Computation and Biomolecular Science and Engineering University of California Santa Barbara

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy.

关键词： health Growth curves SOLUTIONS(GENERAL) model identification bacteria data driven Pseudomonas putida

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Single-shot fringe projection profilometry based on deep learning and computer graphics

arXiv

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arXiv 2021年

作者： Wang, Fanzhou Wang, Chenxing Guan, Qingze School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Computer Science University College London Gower St Bloomsbury LondonWC1E 6BT United Kingdom

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications. Copyright © 2021, The Authors. All rights reserved.

关键词： computer graphics

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Surface Charging Phenomena on HVDC Spacers for Compressed SF_(6) Insulation and Charge Tailoring Strategies

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CSEE Journal of Power and Energy systems 2020年第1期6卷 83-99页

作者： Chuanyang Li Yuan Xu Chuanjie Lin Geng Chen Youping Tu Yao Zhou Zhipeng Lei Tao Han Simone Vincenzo Suraci Jian Wang Weidong Liu M.Tariq Nazir Shun He Andrea Cavallini Giovanni Mazzanti Davide Fabiani Jinliang He Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA. China Electric Power Research Institute Beijing 100192China Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”University of BolognaViale Risorgimento 2Bologna40136Italy State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206China Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control National and Provincial Joint EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Smart Grid of Education Ministry School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Mechanical and Manufacturing Engineering University of New South Wales SydneyNSW 2052Australia Electric Power Research Institute Yunnan Electric Power Grid Co.ltd.KunmingYunnan 650217China

Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover *** paper discusses the laws regarding distribution patterns of surface charges and the related *** field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous *** addition,the main surface charge control methods for epoxy resin are summarized and *** potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.

关键词： GIL HVDC insulator SF6 surface charge accumulation surface flashover

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control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2019年第10期139卷 1159-1166页

作者： Urasaki, Shinpachiro Kobayashi, Yoshimitsu Kami, Yasushi Mitani, Yuichiroh Nobuyama, Eitaku Department of Electronic Control Engineering National Institute of Technology Gifu College 2236-2. Kamimakuwa. Motosu. Gifu 501-0495 Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3. Nishioka. Uozumi Akashi. Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600. Ooka. Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4.Kawazu. Iizuka Fukuoka820-8502 Japan

This paper describes control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback. The position of the levitated object is calculated from the hall voltage and current of the electromagnet. We show the uncertainty for magnetic flux characteristic and design the stabilizing controller by proportional feedback with the hall voltage and current. Moreover, we tried to design servo controller by two-degree-of-freedom control by using a finite number of frequency response (FNFR) model including the uncertainty and confirmed the validity of the proposed method by experiment results. © 2019 The Institute of Eleclrical Engineers of Japan.

关键词： Frequency response

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Retraction notice to "Load frequency control of multi-area power system incorporated renewable energy considering electrical vehicle effect using modified cascaded controller tuned by BESSO algorithm" [Heliyon 10 (2024) e31840]

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Heliyon 2025年第8期11卷

作者： Rehana Ghafoor Lyu Guanghua Muhammad Majid Gulzar Rasmia Irfan Mohammed Alqahtani Muhammad Khalid Department of Electrical Engineering University of Engineering & Technology Lahore Pakistan. Power China Hua Dong Engineering Corporation Ltd. Hangzhou 311100 2019 China. Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia. Interdisciplinary Research Center for Sustainability Energy Systems King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia. Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan. Department of Industrial Engineering King Khalid University Abha 62529 Saudi Arabia. Center for Engineering and Technology Innovations King Khalid University Abha 62529 Saudi Arabia. Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia.

[This retracts the article DOI: 10.1016/***.2024.e31840.].

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Reliability, fault tolerance and other critical components for survivability in information warfare 1

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14th International Joint Conference on e-Business and Telecommunications, ICETE 2017

作者： Stavroulakis, Peter Kolisnyk, Maryna Kharchenko, Vyacheslav Doukas, Nikolaos Markovskyi, Oleksandr P. Bardis, Nikolaos G. Chania Greece Department of Computer Systems and Networks National Aerospace University "KhAI" Kharkiv Ukraine Hellenic Army Academy Varis – Koropiou Avenue Vari 116 72 Greece Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kiev Ukraine Department of Automation and Control in Technical Systems National Technical University "KPI" Kharkiv Ukraine

ISBN: (数字)9783030110390

ISBN: (纸本)9783030110383

The information revolution has caused many aspects of human activity to critically depend on a wide variety of physically existing or virtual technological achievements such as electronic devices, computer systems, algorithms, cloud resources, artificial intelligence hardware and software entities etc. Many of these systems are used in highly sensitive contexts, such as military applications. This implies the existence of an increasing number of unintentional disturbances or malicious attacks. Successful operation requires qualities such as robustness, fault tolerance, reliability, availability and security. All these may be summarized by the title of survivability. Survivability of critical systems working for sensitive applications involves the ability to provide uninterrupted operation under severe disturbances, gracefully degrade when limiting conditions are reached and maintain the ability to resume normal service once the disturbances have been removed. Survivability is an important, even - though non – functional, lifecycle property of many engineering systems. Further desirable elements of survivability include the ability of systems to recognize and resist attacks or accidents, adapt in order to avoid them and modify their behavior in order to diminish the effects of similar future occurrences. This chapter presents a quantitative approach to assessing survivability and an account of survivability in military systems. A scheme for survivability via replica diversity in the implementation of the AES algorithm is then presented. Following that, an algorithm for adaptive attack aversion in user authentication systems is presented that is based on Boolean transformations. An approach for increased survivability in Internet of Things (IoT) systems is then presented. Finally, an algorithm for secure data storage in cloud resources is presented that allows attack detection and avoidance. © Springer Nature Switzerland AG 2019.

关键词： Fault tolerance

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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