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System Identification of User Engagement in mHealth Behavioral Interventions ⁎

IFAC-PapersOnLine 2024年第15期58卷 508-513页

作者： Mohamed El Mistiri Steven De La Torre Karine Tung Benjamin M. Marlin Misha Pavel Predrag Klasnja Donna Spruijt-Metz Daniel E. Rivera Control Systems Engineering Lab Arizona State University Tempe AZ 85281 USA The Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego CA 92093 USA Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA 01003 USA Khoury College of Computer Sciences & Bouve College of Health Sciences Northeastern University Boston MA 02115 USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor MU 48109 USA USC Dornsife Center for Economic and Social Research Department of Psychology and Population and Public Health Sciences University of Southern California Los Angeles CA 90089 USA

Digital behavior change interventions (DBCIs) such as “just-in-time” adaptive interventions (JITAIs) have demonstrated efficacy for increasing physical activity behavior. However, the effectiveness of these interventions is heavily dependent upon user engagement. Despite the inherent dynamic nature of engagement, as it varies over time based on an individual’s changing environment, context, and psychological state, the current understanding of engagement primarily comes from static snapshots of the behavior. The availability of intensive longitudinal data from JITAIs provides a unique opportunity to build and test dynamic models of behavior change from a process systems lens, relying on prediction-error methods from system identification. However, data missingness is a significant practical consideration in this process. Therefore, in this work we address missingness using a Bayesian imputation approach, which we evaluate using data from the HeartSteps II JITAI. Ultimately, the methods presented support the discovery of key factors that impact engagement behavior over time and can play an important role in the development of large-scale personalized interventions.

关键词： eHealth Bayesian methods Computational Social Sciences Time series modelling

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Structural reduction of CRNs with linear sub-CRNs

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IFAC-PapersOnLine 2021年第14期54卷 149-154页

作者： Katalin M. Hangos György Lipták Gábor Szederkényi Systems and Control Laboratory Computer and Automation Research Institute P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Budapest Hungary

A novel systems theory-based structural reduction method is proposed in this paper that can be applied to chemical reaction networks (CRNs) including subsystems consisting of linear reactions, i.e. a linear sub-CRN. The reduced model is a delayed CRN with distributed delays having less complexes (monomials) and reactions than the original model. The reduction is based on the fact that the input/output response of a linear sub-CNR can be seen as a time-delayed input to output relationship, therefore linear sub-CNRs can be interpreted as reactions with distributed delay from their input to their output complexes. The practically important example of a kinetic model describing the spread of the COVID-19 epidemic is used as a case study to illustrate the basic concepts and the computation method.

关键词： Delay Model reduction Chemical Reaction Networks COVID

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BAYESIAN ERROR-IN-VARIABLES MODELS for the IDENTIFICATION of POWER NETWORKS: TECHNICAL REPORT

arXiv

引用

arXiv 2021年

作者： Brouillon, Jean-Sébastien Fabbiani, Emanuele Nahata, Pulkit Moffat, Keith Dörfler, Florian Ferrari-Trecate, Giancarlo LausanneCH-1015 Switzerland Identification and Control of Dynamic Systems Laboratory University of Pavia Pavia Italy Department of Electrical Engineering and Computer Science UC Berkeley Berkeley United States Zurich Switzerland

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the grid, specifically the admittance matrix capturing the topology and line parameters of an electric network. However, a reliable estimate of the admittance matrix may either be missing or quickly become obsolete for temporally varying grids. In this work, we propose a data-driven identification method utilising voltage and current measurements collected from micro-PMUs. More precisely, we first present a maximum likelihood approach and then move towards a Bayesian framework, leveraging the principles of maximum a posteriori estimation. In contrast with most existing contributions, our approach not only factors in measurement noise on both voltage and current data, but is also capable of exploiting available a priori information such as sparsity patterns and known line parameters. Simulations conducted on benchmark cases demonstrate that, compared to other algorithms, our method can achieve significantly greater accuracy. © 2021, CC BY.

关键词： Electric lines

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Privacy-Preserving distributed average consensus in finite time using random gossip

arXiv

引用

arXiv 2021年

作者： Manitara, Nicolaos E. Rikos, Apostolos I. Hadjicostis, Christoforos N. Department of Electrical and Computer Engineering KIOS Center of Excelence University of Cyprus Nicosia1678 Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which each pair of components (nodes) can be randomly selected to perform pairwise standard gossip averaging of their values, and propose an enhancement that can be followed by each node that does not want to reveal its initial value to other (curious) nodes. We assume that curious nodes try to identify the initial values of other nodes but do not interfere in the computation in any other way;however, as a worst-case assumption, curious nodes are allowed to collaborate arbitrarily and are assumed to know the privacy-preserving strategy (but not the actual parameters chosen by the nodes that want to preserve their privacy). We characterize precisely conditions on the information exchange that guarantee privacy-preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the enhanced gossip protocol), when to terminate their operation because approximate average consensus has been reached, i.e., all nodes have obtained values that are within a small distance from the exact average of their initial values. © 2021, CC BY.

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

arXiv

引用

arXiv 2023年

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long The School of Artificial Intelligence Hubei University Hubei Wuhan430062 China The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Beijing Huairou Academy of Parallel Sensing Beijing101499 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100864 China The Waytous Inc. Beijing100083 China

Safety and cost are two important concerns for the development of autonomous driving technologies. From the academic research to commercial applications of autonomous driving vehicles, sufficient simulation and real world testing are required. In general, a large scale of testing in simulation environment is conducted and then the learned driving knowledge is transferred to the real world, so how to adapt driving knowledge learned in simulation to reality becomes a critical issue. However, the virtual simulation world differs from the real world in many aspects such as lighting, textures, vehicle dynamics, and agents' behaviors, etc., which makes it difficult to bridge the gap between the virtual and real worlds. This gap is commonly referred to as the reality gap (RG). In recent years, researchers have explored various approaches to address the reality gap issue, which can be broadly classified into three categories: transferring knowledge from simulation to reality (sim2real), learning in digital twins (DTs), and learning by parallel intelligence (PI) technologies. In this paper, we consider the solutions through the sim2real, DTs, and PI technologies, and review important applications and innovations in the field of autonomous driving. Meanwhile, we show the state-of-the-arts from the views of algorithms, models, and simulators, and elaborate the development process from sim2real to DTs and PI. The presentation also illustrates the far-reaching effects and challenges in the development of sim2real, DTs, and PI in autonomous driving. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks

arXiv

引用

arXiv 2023年

作者： Asadi, M. Mehdi Khosravi, Mohammad Mosalli, Hesam Blouin, Stéphane Aghdam, Amir G. The Department of Electrical Engineering Polytechnique Montréal 2500 chemin de Polytechnique MontréalQCH3T 1J4 Canada The Delft Center for Systems and Control Delft University of Technology Delft Netherlands The Department of Electrical and Computer Engineering Concordia University MontréalQCH3G 1M8 Canada The Defence Research and Development Canada Atlantic Research Centre DartmouthNSB3A 3C5 Canada

The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first to compute the generalized algebraic connectivity of asymmetric networks. After properly transforming the Laplacian matrix of the network, two sequences of one-dimensional and two-dimensional subspaces are generated iteratively, one of which converges to the desired subspace spanned by the eigenvector(s) associated with the eigenvalue(s) representing the network’s generalized algebraic connectivity. A distributed implementation of the proposed power iteration algorithm is then developed to compute the generalized algebraic connectivity from the viewpoint of each node, which is scalable to any asymmetric network of any size with a fixed message length per node. The convergence analysis of these algorithms is subsequently provided under some weak assumptions. The efficiency of the developed algorithms in computing the network connectivity is then demonstrated by simulations. © 2023, CC BY-NC-SA.

关键词： Eigenvalues and eigenfunctions

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories

Assessment and recognition in technical massive open on-line...

引用

IEEE Education engineering (EDUCON)

作者： Sergio Martin Manuel Castro Elio Sancristobal Gabriel Diaz Felix Garcia-Loro Clara Perez Blanca Quintana Rosario Gil Pedro Plaza Africa Lopez German Carro Antonio Robles Llanos Tobarra Miguel Rodriguez-Artacho Jose A. Ruiperez-Valiente Electrical and Computer Engineering Department Universidad Nacional de Educación a Distancia Madrid Spain Dep. Sistemas de Comunicación y Control Universidad Nacional de Educación a Distancia Madrid Spain Dep. Lenguajes y Sistemas Informáticos Universidad Nacional de Educación a Distancia Madrid Spain Teaching Systems Lab Masschusetts Institute of Technology USA

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done.

关键词： VHDL Laboratories Organizations Task analysis engineering education Field programmable gate arrays Certification

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Parallel Distance: A New Paradigm of Measurement for Parallel Driving

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1169-1178页

作者： Teng Liu Hong Wang Bin Tian Yunfeng Ai Long Chen the Vehicle Intelligence Pioneers Inc. Qingdao 266109 the Department of Automotive Engineering Chongqing UniversityChongqing 400044China the Mechanical and Mechatronics Engineering Department Waterloo UniversityN2L 3G1Canada the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then,Markov chain(MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.

关键词： Artificial and physical system parallel distance parallel driving 3.0 parallel system rotational and accelerator signal

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Towards probabilistic intrusion detection in supervisory control of discrete event systems 1

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IFAC-PapersOnLine 2020年第2期53卷 1776-1782页

作者： Rômulo Meira-Góes Christoforos Keroglou Stéphane Lafortune Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

In control systems, sensor deception is a class of attacks where an attacker manipulates sensor readings to cause damage to the system. Our work investigates quantitative measurements to detect this class of attacks in the context of stochastic supervisory control. We introduce the notion of ϵ -safe systems, which is a first step to generalize qualitative intrusion detection conditions to quantitative intrusion detection conditions. We provide sufficient and necessary conditions to verify if a system is ϵ -safe. Moreover, we provide an algorithm that verifies these conditions, which implies that the problem is decidable.

关键词： Supervisory control automata Discrete event modeling simulation Intrusion detection Security

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