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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Cooperative opinion dynamics on multiple interdependent topics: Modeling and analysis

arXiv

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arXiv 2018年

作者： Ahn, Hyo-Sung Van Tran, Quoc Trinh, Minh Hoang Moore, Kevin L. Ye, Mengbin Liu, Ji Gwangju Korea Republic of Department of Electrical Engineering Colorado School of Mines GoldenCO United States Systems and Control Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Electrical and Computer Engineering 211 Light Engineering Stony Brook University Stony BrookNY United States

To model the interdependent couplings of multiple topics, we develop a set of rules for opinion updates of a group of agents. The rules are used to design or assign values to the elements of interdependent weighting matrices. The cooperative and anti-cooperative couplings are modeled in both the inverse-proportional and proportional feedbacks. The behaviors of cooperative opinion dynamics are analyzed using a null space property of state-dependent matrix-weighted Laplacian matrices and a Lyapunov candidate. Various consensus properties of state-dependent matrix-weighted Laplacian matrices are predicted according to the intra-agent network topology and interdependency topical coupling topologies. Copyright © 2018, The Authors. All rights reserved.

关键词： Laplace equation

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Introduction

Advances in Industrial Control

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Advances in Industrial control 2018年第9783319610207期 1-18页

作者： Orsag, Matko Korpela, Christopher Oh, Paul Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering and Computer Science United States Military Academy West PointNY United States Department of Mechanical Engineering University of Nevada Las Vegas Las VegasNV United States

Aerial robotics has reached significant maturity in the last few years. Thus, the number of new unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS), applications, and companies producing them has increased in a very important way. The number of publications and presentations in conferences has also experienced a large increase. Moreover, almost every day we can find articles, videos, and news in the general media related to drones. © Springer International Publishing AG 2018.

关键词： Antennas

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Sensorless nonlinear control of fed-batch Escherichia coli cultivation bioprocess using the state-dependent approach 12th

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12th Portuguese Conference on Automatic control, controlO 2016

作者： Iratni, Abdelhamid Araújo, Rui Rastegar, Saeid Mostefai, Mohammed Faculty of Science and Technology Electrical Engineering Department University of Bordj Bou Arreridj El Anasser Algeria Department of Electrical and Computer Engineering Institute for Systems and Robotics University of Coimbra Coimbra Portugal Automatic Control Laboratory of Setif Electrical Engineering Department Faculty of Technology University of Setif1 Sétif Algeria

ISBN: (纸本)9783319436708

The cultivation of Escherichia coli bacteria is widely used by geneticists and biopharmaceuticals to produce medicines and vaccines. In such industries, Bioprocesses are known by their harsh environment and very expensive physical sensors. This paper deals with nonlinear estimation and control of fed-batch Escherichia coli bioprocess. Enhancing the process performance and maximizing its efficiency by optimizing the control of Escherichia coli cultures depends on the availability of appropriate online sensors for the main culture components. In this study, an innovative method for nonlinear estimation and control of bioprocesses is designed and assessed. The new algorithm for control and estimation of Escherichia coli bioprocess states, based on the State-Dependent Riccati Equation technique, is validated by numerical simulation. The results obtained clearly demonstrate the effectiveness of the proposed technique for Biosystems in measuring, monitoring, and control of their natural nonlinear dynamics. © Springer International Publishing Switzerland 2017.

关键词： Extended Kalman filters

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Implementation and benchmarking of a crosstalk-free method for wavefront Zernike coefficients reconstruction using Shack-Hartmann sensor data

arXiv

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arXiv 2018年

作者： Biesheuvel, R.S. Janssen, A.J.E.M. Pozzi, P. Pereira, S.F. Optics Research Group ImPhys Department Faculty of Applied Sciences Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology P.O.Box 513 Eindhoven5600 MB Netherlands Delft Center of Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

In wavefront characterization, often the combination of a Shack-Hartmann sensor and a reconstruction method utilizing the Cartesian derivatives of Zernike circle polynomials (the least-squares method, to be called here Method A) is used, which is known to introduce crosstalk. In (Janssen, 2014) a crosstalk-free analytic expression of the LMS estimator of the wavefront Zernike coefficients is given in terms of wavefront partial derivatives (leading to what we call Method B). Here, we show an implementation of this analytic result where the derivative data are obtained using the Shack-Hartmann sensor and compare it with the conventional least-squares method. Copyright © 2018, The Authors. All rights reserved.

关键词： Wavefronts

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Magnetic field gradients and their effects on the diffusion tensor derivate measures 5

Magnetic field gradients and their effects on the diffusion ...

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5th International Conference on Radiation and Applications in Various Fields of Research, RAD 2017

作者： Moraru, Luminita Dimitrievici, Lucian Traian Ene, Antoaneta Moldovanu, Simona Department of Chemistry Physics and Environment Faculty of Sciences and Environment University Dunarea de Jos of Galati Galati Romania Mihail Kogalniceanu High School Galati Romania Department of Computer Science and Engineering Electrical and Electronics Engineering Faculty of Control Systems Computers University Dunarea de Jos of Galati Galati Romania Dumitru Motoc High School Galati Romania

Diffusion tensor imaging (DTI) and the degree of diffusion weighting of the sequence, expressed as the b-factor, are used to investigate the effect of the magnetic field gradients on the integrity of white matter in patients with temporal intracerebral hemorrhage. The healthy patients are the gold standard. The present study investigated the changes of mean diffusivity (MD) and fractional anisotropy (FA) in a brain hemisphere approach to understand the effect of magnetic field gradients on the brain hemorrhage investigation. The artifacts induced by diffusion gradients in diffusion tensor imaging affect the accuracy of the investigation, and in order to achieve the optimal image quality, strong magnetic field gradients are recommended. The artifact effect of higher magnetic field gradients is analyzed by means of the root-mean-square FA and MD difference between left and right brain hemispheres. © RAD Conference Proceedings. All rights reserved.

关键词： Diffusion tensor imaging

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PID2018 benchmark challenge: Model-based feedforward compensator with a conditional integrator ??

arXiv

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arXiv 2018年

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the effciency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Disturbance rejection

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2018年 620卷 v-vi页

作者： Omatu, Sigeru Rodríguez, Sara Villarrubia, Gabriel Faria, Pedro Sitek, Pawel Prieto, Javier Faculty of Engineering Department of Electronics Information and Communication Engineering Osaka Institute of Technology OsakaOsaka Japan Faculty of Science Department of Computer Science and Automation Control University of Salamanca Salamanca Spain GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Instituto Superior de Engenharia do Porto Porto Portugal Faculty of Electrical Engineering and Computer Science Department of Control and Management Systems Department of Information Systems Kielce University of Technology Kielce Poland

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Collaboration with employers in IT training

Collaboration with employers in IT training

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Strategic Partnership of Universities and Enterprises of Hi-Tech Branches (science. Education. Innovations), IV Forum

作者： Alexandra V. Loginova Eduard K. Algazinov Veronika V. Garshina Alexander V. Sychev Elena N. Desyatirikova Andrey V. Smoljyaninov Highest School of Cyberphysic Systems and Control Institute of Computer Sciences and Technology Peter the Great St. Petersburg State Polytechnic University St. Petersburg Russia Computer Science Faculty Voronezh State University Voronezh Russia Department of Automatic Control Systems Voronezh State Technical University Voronezh Russia

A systematic approach to the management of the IT training system is applied. The role and place of the Training Center as a mechanism for the participation of the partner company in the educational process are shown.

ISBN: (纸本)9781538625729

关键词： Training Companies Production computer science Mathematical model Collaboration

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