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An approach for activity-based DEVS model specification

An approach for activity-based DEVS model specification

2016 TMS/DEVS Symposium on Theory of Modeling and Simulation, TMS/DEVS 2016, Part of the 2016 Spring Simulation Multiconference, SpringSim 2016

作者： Alshareef, Abdurrahman Sarjoughian, Hessam S. Zarrin, Bahram Arizona Center for Integrative Modeling and Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S. Mill Avenue TempeAZ85281 United States DTU Compute Technical University of Denmark Kgs Lyngby2800 Denmark

Creation of DEVS models has been advanced through Model Driven Architecture and its frameworks. The overarching role of the frameworks has been to help develop model specifications in a disciplined fashion. Frameworks can provide intermediary layers between the higher level mathematical models and their corresponding software specifications from both structural and behavioral aspects. Unlike structural modeling, developing models to specify behavior of systems is known to be harder and more complex, particularly when operations with non-trivial control schemes are required. In this paper, we propose specifying activity-based behavior modeling of parallel DEVS atomic models. We consider UML activities and actions as fundamental units of behavior modeling, especially in the presence of recent advances in the UML 2.5 specifications. We describe in detail how to approach activity modeling with a set of elemental behavioral constructs for atomic DEVS model. We show how Activity models correspond to the atomic DEVS model using an exemplar. We also highlight the complementary roles of Activity and Statecharts models. © 2016 Society for Modeling & Simulation International (SCS).

关键词： Specifications

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Timestamp Temporal Logic (TTL) for testing the timing of Cyber-Physical systems

Timestamp Temporal Logic (TTL) for testing the timing of Cyb...

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作者： Mehrabian, Mohammadreza Khayatian, Mohammad Shrivastava, Aviral Eidson, John C. Derler, Patricia Andrade, Hugo A. Li-Baboud, Ya-Shian Griffor, Edward Weiss, Marc Stanton, Kevin Arizona State University School of Computing Informatics and Decision Systems Engineering 699 S Mill Ave TempeAZ85281 United States University of California Berkeley BerkeleyCA94720 United States National Instruments corporation 11500 N Mopac Expy AustinTX78759 United States University of Texas at Austin 787 Balra Dr. El CerritoCA94530 United States NIST 100 Bureau Drive GaithersburgMD20899 United States Marc Weiss Consulting AptosCA95003 United States Intel corporation 2111 NE 25th Ave. HillsboroOR97124 United States

In order to test the performance and verify the correctness of Cyber-Physical systems (CPS), the timing constraints on the system behavior must be met. Signal Temporal Logic (STL) can efficiently and succinctly capture the timing constraints of a given system model. However, many timing constraints on CPS are more naturally expressed in terms of events on signals. While it is possible to specify event-based timing constraints in STL, such statements can quickly become long and arcane in even simple systems. Timing constraints for CPS, which can be large and complex systems, are often associated with tolerances, the expression of which can make the timing constraints even more cumbersome using STL. This paper proposes a new logic, Timestamp Temporal Logic (TTL), to provide a definitional extension of STL that more intuitively expresses the timing constraints of distributed CPS. TTL also allows for a more natural expression of timing tolerances. Additionally, this paper outlines a methodology to automatically generate logic code and programs to monitor the expressed timing constraints. Since our TTL monitoring logic evaluates the timing constraints using only the timestamps of the required events on the signal, the TTL monitoring logic has significantly less memory footprint when compared to traditional STL monitoring logic, which stores the signal value at the required sampling frequency. The key contribution of this paper is a scalable approach for online monitoring of the timing constraints. We demonstrate the capabilities of TTL and our methodology for online monitoring of TTL constraints on two case studies: 1) Synchronization and phase control of two generators and, 2) Simultaneous image capture using distributed cameras for 3D image reconstruction. © 2017 ACM.

关键词： Real time systems

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A coalition formation approach to coordinated task allocation in heterogeneous uav networks

arXiv

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arXiv 2017年

作者： Afghah, Fatemeh Zaeri-Amirani, Mohammad Razi, Abolfazl Chakareski, Jacob Bentley, Elizabeth School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Department of Electrical & Computer Engineering University of Alabama TuscaloosaAL United States Air Force Research Laboratory RomeNY United States

The problem of adversary target detection and the subsequent task completion using a heterogeneous network of resource-constrained UAVs is considered. No prior knowledge about locations and required resources to identify these targets is available to the UAVs. In the proposed leader-follower coalition formation model, the UAV that first locates a target serves as the coalition leader and selects a group of follower UAVs to complete the task associated with the identified target. The goal of the coalition formation is to complete the designated tasks with minimal resource utilization. Another role of coalition members is to make the ground station aware of the detected adversary target by forwarding its signal to the station via a distributed cooperative relaying scheme. We also propose a reputation-based mechanism for coalition formation to monitor the cooperative behavior of the UAVs over the course of time and exclude potentially untrustworthy UAVs. Simulation results show the efficiency of the proposed method in forming optimal coalitions compared to alternative methods.1. Copyright © 2017, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Experience-based torque estimation for an industrial robot

Experience-based torque estimation for an industrial robot

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2016 IEEE International Conference on Robotics and Automation, ICRA 2016

作者： Berger, Erik Grehl, Steve Vogt, David Jung, Bernhard Amor, Heni Ben Institute of Computer Science Technical University Bergakademie Freiberg Bernhard-von-Cotta-Str. 2 Freiberg09599 Germany School of Computing Informatics And Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States

ISBN: (纸本)9781467380263

Robotic manipulation tasks often require the control of forces and torques exerted on external objects. This paper presents a machine learning approach for estimating forces when no force sensors are present on the robot platform. In the training phase, the robot executes the desired manipulation tasks under controlled conditions with systematically varied parameter sets. All internal sensor data, in the presented case from more than 100 sensors, as well as the force exerted by the robot are recorded. Using Transfer Entropy, a statistical model is learned that identifies the subset of sensors relevant for torque estimation in the given task. At runtime, the model is used to accurately estimate the torques exerted during manipulations of the demonstrated kind. The feasibility of the approach is shown in a setting where a robotic manipulator operates a torque wrench to fasten a screw nut. Torque estimates with an accuracy of well below ±1Nm are achieved. A strength of the presented model is that no prior knowledge of the robot's kinematics, mass distribution or sensor instrumentation is required. © 2016 IEEE.

关键词： Torque

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Estimating perturbations from experience using neural networks and information transfer

Estimating perturbations from experience using neural networ...

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2016 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2016

作者： Berger, Erik Vogt, David Grehl, Steve Jung, Bernhard Amor, Heni Ben Institute of Computer Science Technical University Bergakademie Freiberg Bernhard-von-Cotta-Str. 2 Freiberg09599 Germany School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States

ISBN: (纸本)9781509037629

In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a forcetorque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware. © 2016 IEEE.

关键词： Firmware

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Learning human-robot interactions from human-human demonstrations (with applications in Lego rocket assembly)

Learning human-robot interactions from human-human demonstra...

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IEEE-RAS International Conference on Humanoid Robots

作者： David Vogt Simon Stepputtis Richard Weinhold Bernhard Jung Heni Ben Amor Faculty of Mathematics and Informatics Technical University Bergakademie Freiberg Freiberg Germany School of Computing Informatics Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (纸本)9781509047192

This video demonstrates a novel imitation learning approach for learning human-robot interactions from human-human demonstrations. During training, the movements of two human interaction partners are recorded via motion capture. From this, an interaction model is learned that inherently captures important spatial relationships as well as temporal synchrony of body movements between the two interacting partners. The interaction model is based on interaction meshes that were first adopted by the computer graphics community for the offline animation of interacting virtual characters. We developed a variant of interaction meshes that is suitable for real-time human-robot interaction scenarios. During humanrobot collaboration, the learned interaction model allows for adequate spatio-temporal adaptation of the robots behavior to the movements of the human cooperation partner. Thus, the presented approach is well suited for collaborative tasks requiring continuous body movement coordination of a human and a robot. The feasibility of the approach is demonstrated with the example of a cooperative Lego rocket assembly task.

关键词： Hidden Markov models Robot kinematics Trajectory Computational modeling Collaboration

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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On Social Network Firewall Selection

On Social Network Firewall Selection

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International Conference on computing, Networking and Communications

作者： Anisha Mazumder Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University

ISBN: (纸本)9781467385800

It is common knowledge that the decision of an individual regarding adoption of a product or technology is, more often than not, heavily influenced by their friends and acquaintances. In real world, there are different competing products and innovations that try to garner as many loyal followers as possible. Over the past few years, there has been a significant interest in the research community to study social network problems with a flavor of competition. Such problems often focus on identification of a set of people in a given social network by the competing players in order to achieve some goal. In this paper, we introduce the weighted Segregating Vertex Set (wSVS) problem, in which we are given a weighted undirected graph with a subset of nodes identified as the seedset of the first player and the goal for the second player is to identify a subset of nodes (firewall) of minimum cumulative weight, such that the total weight of the nodes reachable by the first player is strictly less than the total weight of the nodes not reachable by the first player. Thus, the second player tries to contain the reach of the first player within the social network community. This problem is also relevant for containment of disease in epidemiology, containment of forest fire and several other domains. We prove that this problem is NP-complete and provide an optimal solution through the use of Mixed Integer Linear Programming. We also provide a heuristic solution for the wSVS problem and show its efficacy through detailed experimentation. Our heuristic solution delivers near optimal solution in lesser time compared to that needed to find the optimal solution.

关键词： Social network services Sociology Statistics Companies Particle separators Diseases Social computing

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Visuospatial skill learning for robots

arXiv

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arXiv 2017年

作者： Ahmadzadeh, S. Reza Mastrogiovanni, Fulvio Kormushev, Petar School of Interactive Computing Georgia Institute of Technology AtlantaGA United States Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova via Opera Pia 13 Genova16145 Italy Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom

A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observing spatial relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based approach that focuses on achieving a desired goal configuration of objects relative to one another while maintaining the sequence of operations. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum prior knowledge about the objects and the environment. In contrast to many existing approaches, VSL offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed actions are learned using VSL, while the sensorimotor skills are learned using a conventional trajectory-based learning approach. such integration easily extends robot capabilities to novel situations, even by users without programming ability. In VSL-SP the internal planner of VSL is integrated with an existing action-level symbolic planner. Using the underlying constraints of the task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is updated. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be used in planning and performed independently during the learning phase. The proposed approach is validated through several real-world *** Codes 68T40 Copyright © 2017, The Authors. All rights reserved.

关键词： Demonstrations

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STATE OF THE CLIMATE IN 2021

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2022年第8期103卷 S1-S1页

作者： Blunden, Jessica NOAA/NESDIS National Centers for Environmental Information Asheville NC United States NOAA NESDIS National Centers for Environmental Information Silver Spring MD United States Met Office Hadley Centre Exeter United Kingdom Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville NC United States Communications and Outreach NOAA/NESDIS National Centers for Environmental Information Asheville NC United States Innovative Consulting and Management Services LLC NOAA/NESDIS National Centers for Environmental Information Asheville NC United States European Centre for Medium-Range Weather Forecasts Reading United Kingdom CMNS-Earth System Science Interdisciplinary Center University of Maryland College Park MD United States Department of Meteorology National Centre for Earth Observation University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton VA United States National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Life and Environmental Sciences Osaka Prefecture University Osaka Sakai Japan University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder CO United States Centro de Investigaciones sobre Desertificación Spanish National Research Council (CIDE CSIC-UV-Generalitat Valenciana) Climate Atmosphere and Ocean Laboratory (Climatoc-Lab) Moncada Valencia Spain Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich United Kingdom Joint Research Centre of the European Commission Ispra Italy GloH2O Almere Netherlands University of Reading Reading United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Cité Institut de physique du globe de Paris CNRS IGN Paris

Editors note: For easy download the posted pdf of the State of the Climate in 2021 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few mi... 详细信息

关键词： Greenhouse gases

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