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International Conference on control, engineering & Information Technology (CEIT)

作者： Ertugrul Ates Ilker Ustoglu Computer Engineering Department Nigde Omer Halisdemir University Nigde Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781538676424

This study represents a moving block signaling system model. In this system, different from the conventional railway systems, there are access points near the railway and moving trains communicates via them. In this way, not only railway traffic flow is increased but also waiting time for the passengers is decreased, especially in metro lines. In this work, representing a multi-carriage train, multi-degree of freedom mass-spring-damper models are used. Then, a tractive force and braking force are applied to the system as an input and location is obtained from this output. In addition, a limit movement authority is defined for control algorithm. So, trains move according to this authority. Finally, for the safe stop of trains, brake distance value is calculated. This equation is solved depending on the speed of the rear train. So, if the rear train accelerates, the distance will increase according to the square of the speed of the rear train. Besides, depending on the speed of the rear train and travel time, a safety margin distance is obtained. The proposed technic is simulated by using Matlab-Simulink™ and the results including train models, speed profiles, locations, accelerations etc. are shown in Figures.

关键词： Mathematical model Acceleration Block signalling Tracking Aerodynamics

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A Study of Filters Selectivity with Maximally Flat Responses with Respect to Hausdorff Distance 9

A Study of Filters Selectivity with Maximally Flat Responses...

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9th National Conference with International Participation, ELECTRONICA 2018

作者： Apostolov, Peter Stefanov, Alexey Apostolov, Stoyan Department of Communication and Computer Engineering South-West University Ivan Mihaylov str. 66 Blagoevgrad2700 Bulgaria Department Systems and Control Technical University of Sofia 8 Kl. Ohridski Blvd. Bl. 1 Sofia1000 Bulgaria

ISBN: (纸本)9781538658017

This article examines the approximations of the ideal transfer function of a low pass filter with sigmoidal functions using Hausdorff distance. A relation between the filter selectivity and the Hausdorff distance is established. The approximation parameters are defined. Analytical dependencies of transfer functions and their respective Hausdorff distances are derived. An analysis of the results is made. © 2018 IEEE.

关键词： Transfer functions

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A Projection-Based Approach for Memory Leak Detection

A Projection-Based Approach for Memory Leak Detection

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Xiaohui Sun Sihan Xu Chenkai Guo Jing Xu Naipeng Dong Xiujuan Ji Sen Zhang Department of Computer and Control Engineering Nankai University School of Computing National University of Singapore Department of Binhai Nankai University

One of the major software safety issues is memory leak. Moreover, detecting memory leak vulnerabilities is challenging in static analysis. Existing static detection tools find bugs by collecting programs' information in the process of scanning source code. However, the current detection tools are weak in efficiency and accuracy, especially when the targeted program contains complex branches. This paper proposes a projection-based approach to detect memory leaks in C source code with complex control flows. According to the features of memory allocation and deallocation in C source code, this approach projects the original control flow graph of a program to a simpler one, and it reduces the analysis complexity. Besides, this paper implements a memory-leak detection tool-PML_Checker, and evaluates the tool by comparing with three open-source static detection tools on both public benchmarks and study test cases. The experimental results show that PML_Checker reports the most memory leak vulnerabilities among the four existing tools with complex control flows and complex data types, and PML_Checker obtains higher efficiency and accuracy on public benchmarks.

关键词： Aerospace electronics Resource management Tools Memory management Complexity theory Projection algorithms Software

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

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Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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Ten quick tips for deep learning in biology

arXiv

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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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A Decade of Soft Computing Approaches in Power System Investment Planning

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Journal of Physics: Conference Series 2021年第1期1878卷

作者： S B Yaakob A S F Rahman N M Mukhtar M N Yaakob N Arbaiy P-C Lin Control System Group (CSG) Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Kampus Alam UniMAP Pauh Putra 02600 Arau Perlis Malaysia Institut Pendidikan Guru Malaysia Kampus Darul Aman Bandar Darul Aman Kedah 06000 Jitra Kedah Malaysia Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia Department of Information Engineering and Computer Science Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan

This paper opens up an issue regarding to power system investment planning. In order to obtain a sustainable electricity supply, the power outage should be minimized. Maintenance cost plays as an essential element in investment planning, therefore misdirect investment should be avoided. Since soft computing can solved this problem, this paper offers a review in solving investment planning problem through such approaches. The main objective of this paper is to examine the concept and findings by the researchers in power system investment planning problem. Besides, this paper will highlight the most relevant contributions and limitations that can be improve in the other research. Finally, this paper will be pointed out some trends in power system investment planning problem.

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Decentralized event-triggered consensus of autonomous agents over unreliable communication networks

Decentralized event-triggered consensus of autonomous agents...

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AIAA Guidance, Navigation, and control Conference, 2018

作者： Garcia, Eloy Cao, Yongcan Casbeer, David W. Control Science Center of Excellence Air Force Research Laboratory Wright-Patterson AFB OH45433 United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (数字)9781624105265

ISBN: (纸本)9781624105265

This paper presents distributed event-triggered control and communication strategies for a group of autonomous agents to achieve consensus over unreliable communication channels. Communication is unreliable in the sense that the broadcast channel from one agent to its neighbors can drop the event-triggered packets of information, where the transmitting agent is unaware that the packet was not received and the receiving agents have no knowledge of the transmitted packet. Additionally, packets that successfully arrive at their destinations may suffer from time-varying communication delays. In this paper, we consider directed graphs, and also consider the general case of relaxed consistency on the packet dropouts and the delays. Relaxing consistency means that the delays and dropouts for a packet broadcast by one agent can be different for each receiving node. We show that even under this challenging scenario, agents can reach consensus asymptotically while reducing transmissions of measurements based on the proposed event-triggered consensus protocol. In addition, positive inter-event times are obtained which guarantee that Zeno behavior does not occur. © 2018 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Multi agent systems

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A note on the stability analysis of a class of nonlinear systems - AN LMI approach

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2018年第4期80卷 3-10页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

The main goal of the paper is to study the equilibria of a nonlinear system, proving the existence and uniqueness of an equilibrium point in the positive ortant. We also provide numerically tractable conditions (by using Linear Matrix Inequalities techniques) to check the asymptotic stability of the equilibrium point. An illustrative numerical example is closing the paper along with some conclusions. © 2018, Politechnica University of Bucharest. All rights reserved.

关键词： Nonlinear systems

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A Uniform approach to maximal permissiveness in modular control of discrete-event systems

arXiv

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arXiv 2019年

作者： Komenda, Jan Lin, Feng Schuppen, Jan H. Institute of Mathematics Academy of Sciences of the Czech Republic Zizkova 22 Brno616 62 Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Van Schuppen Control Research Gouden Leeuw 143 Amsterdam1103 KB

In this paper, a uniform approach to maximal permissiveness in mod-ular control of discrete-event systems is proposed. It is based on three important concepts of modular closed-loops: monotonicity, distributiv-ity, and exchangeability. Monotonicity of various closed-loops satisfying a given property considered in this paper holds whenever the underlying property is preserved under language unions. Distributivity holds if the inverse projections of local plants satisfy the given property with respect to each other. Among new results, suficient conditions are proposed for distributed computation of supremal relatively observable *** Codes 93C65 Copyright © 2019, The Authors. All rights reserved.

关键词： Discrete event simulation

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A New Direct Heart Sound Segmentation Approach using Bi-directional GRU

A New Direct Heart Sound Segmentation Approach using Bi-dire...

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International Conference on Automation and Computing (ICAC)

作者： Tianqi Fan Jin Zhu Yongqiang Cheng Qingde Li Dongfei Xue Robert Munnoch Department of Control Science and Engineering Tongji University Shanghai China School of Engineering and Computer Science University of Hull Hull UK

ISBN: (纸本)9781538648919

Heart sound segmentation is a key step in automatic analysis of phonocardiogram (PCG) for early pathology detection. In this paper, we propose a novel method inspired by the Part of Speech (POS) tagging problem for heart sound segmentation. We use a Bi-directional Gated Recurrent Unit (GRU) to predict the state of the heart sound cycles directly, steering away from the traditionally used envelopes and time-frequency based features. Our method is evaluated on a large dataset using a 10-fold cross-validation. The proposed method has achieved overall 96.86% accuracy and the F1 score is 98.40% on the test sets. The proposed method has outperformed other existing state of the art methods by 1-3 percentage in terms of accuracy and F1.

关键词： Heart Bidirectional control Training Neural networks Databases Turning Phonocardiography

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