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Formal framework and models for validation and verification of software-intensive aerospace systems

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Formal framework and models for validation and verification ...

AIAA Information systems-Infotech At Aerospace Conference, 2017

作者： Guarro, Sergio B. Yau, Michael K. Ozguner, Umit Aldemir, Tunc Kurt, Arda Hejase, Mohammad Knudson, Matt D. ASCA Inc. 1720 S. Catalina Avenue Suite 220 Redondo BeachCA90277 United States Department of Electrical and Computer Engineering Ohio State University 2015 Neil Ave ColumbusOH43210 United States Mechanical and Aerospace Engineering Department Ohio State University 201 W 19th Ave. ColumbusOH43210 United States NASA Ames Research Center Intelligent Systems Division Moffett Field Mail Stop 269-2 CA94035 United States

ISBN: (数字)9781624104497

ISBN: (纸本)9781624104497

Model-based control systems (MBCSs) and adaptive control systems (ACSs) represent an emerging class of software-intensive space and aeronautical control applications that bring tangible benefits to design processes and developments, but also challenges to traditional validation and verification (V&V) methods. This paper presents the results produced in the first two years of a three-year NASA project seeking the development of a comprehensive framework for the V&V of MBCSs and ACSs. The framework under development represents a comprehensive V&V methodology that utilizes Dynamic Flowgraph Methodology (DFM) and Markov-CCMT (Cell to Cell Mapping Technique) logic-dynamic model constructs, and associated time-dependent, deductive-inductive multi-valued logic analysis processes. This V&V framework produces formal evidence supporting the generation and documentation of an assurance case for a model-based control system, for the ultimate objective of demonstrating its compliance with design and safety requirements, and applicable software certification standards. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Adaptive control systems

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

Risk informed safety case framework for unmanned aircraft system flight software certification

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Risk informed safety case framework for unmanned aircraft sy...

AIAA Information systems-Infotech At Aerospace Conference, 2017

作者： Guarro, Sergio B. Yau, Michael K. Ozguner, Umit Aldemir, Tunc Kurt, Arda Hejase, Mohammad Knudson, Matt D. ASCA Inc. 1720 S. Catalina Avenue Suite 220 Redondo BeachCA90277 United States Department of Electrical and Computer Engineering Ohio State University 2015 Neil Ave. ColumbusOH43210 United States Mechanical and Aerospace Engineering Department Ohio State University 201 W 19th Ave. ColumbusOH43210 United States Intelligent Systems Division NASA Ames Research Center Moffett Field Mail Stop 269-2 CA94035 United States

ISBN: (数字)9781624104497

ISBN: (纸本)9781624104497

Certification of the rapidly growing unmanned aircraft systems to ensure their safe operation in the National Airspace System represents a significant challenge to the Federal Aviation Administration and the aviation community. The development of a validation and verification (V&V) framework, compatible with an assurance case/safety case argument, for model-based control systems and adaptive control systems is discussed in this paper. The work documented is funded as a 3-year project by the NASA Ames Research center. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Adaptive control systems

A quantitative and risk based framework for UAS control system assurance

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A quantitative and risk based framework for UAS control syst...

AIAA Information systems-Infotech At Aerospace Conference, 2017

作者： Hejase, Mohammad Kurt, Arda Aldemir, Tunc Ozguner, Umit Guarro, Sergio B. Yau, Michael K. Knudson, Matt D. Department of Electrical and Computer Engineering The Ohio State University 2015 Neil Ave ColumbusOH43210 United States Department of Mechanical and Aerospace Engineering The Ohio State University 201 W 19th Ave. ColumbusOH43210 United States ASCA Inc. 1720 S. Catalina Avenue Suite 220. Redondo BeachCA90277 United States NASA Ames Research Center Intelligent Systems Division Moffett Field Mail Stop 269-2. CA94035 United States

ISBN: (数字)9781624104497

ISBN: (纸本)9781624104497

A generic backtracking process algorithm based on the deductive implementation of a probabilistic Markov/Cell-to-Cell Mapping Technique is proposed for risk informed identification of critical scenarios involving control systems of unmanned aerial systems (UAS) operating in the National Airspace (NAS). A discrete-state representation of the UAS control system is utilized which allows tracing fault propagation throughout the system and the quantification of probabilistic system evolution in time. A hybrid state UAS control system composed of a discrete-state flight executive based on a finite state machine (FSM) representation of the control system and a continuous-state autopilot based on linear control methods is used as a case study. The consequence of interest in the case study is taken to be a deviation from the flight plan at the destination in the waypoint following phase of a UAS mission under a ‘link condition’ system configuration. The backtracking process algorithm is implemented to identify the risk significant scenarios for the case study. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Linear control systems

Integrated mechatronics design of flexure joint and controller in dual-drive gantry: a constrained H2 optimization approach

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Integrated mechatronics design of flexure joint and controll...

2017 IEEE International Conference on Advanced intelligent Mechatronics, AIM 2017

作者： Ma, Jun Chen, Si-Lu Kamaldin, Nazir Teo, Chek Sing Tay, Arthur Mamun, Abdullah Al Tan, Kok Kiong Department of Electrical and Computer Engineering SIMTech-NUS Joint Lab on Precision Motion Systems National University of Singapore 117582 Singapore Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Mechatronics Group Singapore Institute of Manufacturing Technology 138634 Singapore Zhejiang Provincial Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo Zhejiang315121 China NUS Grad. School for Inte. Sci. and Eng. and Department of Electrical Computer Engineering National University of Singapore 119077 Singapore

ISBN: (纸本)9781509059980

The dual-drive H gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a novel biaxial gantry with flexure-links is designed to allow a small rotation angle of the cross-Arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-To-point trajectories with same dynamics but different steps. From here, an H2 optimization with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal steps. Comparative experiments results obtained on the testbed are presented to verify the effectiveness of proposed method. © 2017 IEEE.

关键词： Constrained optimization

A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy

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arXiv 2017年

作者： Pawlick, Jeffrey Colbert, Edward Zhu, Quanyan Department of Electrical and Computer Engineering New York University Tandon School of Engineering 5 MetroTech Center BrooklynNY United States Us Army Research Laboratory 2800 Powder Mill Road AdelphiMD United States Us Army Research Laboratory Virgina Tech Intelligent Systems Laboratory Hume Center for National Security and Technology 900 N. Glebe Road ArlingtonVA United States Department of Electrical and Computer Engineering New York University Tandon School of Engineering

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy. Copyright © 2017, The Authors. All rights reserved.

关键词： Surveys

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

Risk-aversemodel predictive control

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arXiv 2017年

作者： Sopasakis, Pantelis Herceg, Domagoj Bemporad, Alberto Patrinos, Panagiotis University of Cyprus Department of Electrical and Computer Engineering KIOS Research Center for Intelligent Systems and Networks 1 Panepistimiou Avenue Aglantzia Nicosia2109 IMT School for Advanced Studies Lucca Piazza San Francesco 19 Lucca55100 Italy STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Kasteelpark Arenberg 10 Leuven3001 Belgium

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for *** Codes 93E20 Copyright © 2017, The Authors. All rights reserved.

关键词： Model predictive control

Robust and Scalable Power System State Estimation via Composite Optimization

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arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center and the Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200072 China

—In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks