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Simulation Winter Conference

作者： David Tian Hazel Y. Squires Charlotte Buckley Duncan Gillespie Harry Tattan-Birch Lion Shahab Robert West Alan Brennan Jamie Brown Robin C. Purshouse Dept. of Automatic Control and Systems Engineering University of Sheffield UK Sheffield Centre for Health and Related Research School of Medicine and Population Health University of Sheffield UK Institute of Epidemiology and Health University College London UK

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (ABM) design for simulating the smoking behaviors of a population using the Capability, Opportunity, Motivation - Behavior (COM-B) model. Capability, Opportunity and Motivation are modeled as latent composite attributes which are composed of observable factors associated with smoking behaviors. Three forms of the COM-B model are proposed to explain the transitions between smoking behaviors: initiating regular smoking uptake, making a quit attempt and quitting successfully. The ABM design follows object-oriented principles and extends an existing generic software architecture for mechanism-based modeling. The potential of the model to assess the impact of smoking policies is illustrated and discussed.

关键词： Software architecture Object oriented modeling Trajectory Planning

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Choice of the Parameters of an EMI Monitoring System for an AC Traction Network

Choice of the Parameters of an EMI Monitoring System for an ...

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IEEE Global Electromagnetic Compatibility Conference (GEMCCON)

作者： Volodymyr Havryliuk Regis Nibaruta Dept. of automatic and telecommunication Ukrainian State University of Science and Technologies Dnipro Ukraine Faculty of Electrical Engineering Mathematics & Computer Science (EEMCS) University of Twente Enschede Netherlands

The problem considered in the paper is related to the formulation of criteria for choice of the parameters of the electromagnetic interference monitoring system for AC traction network in accordance with the requireme... 详细信息

关键词： Electromagnetic interference Harmonic analysis Electromagnetic compatibility Electromagnetics Monitoring Standards

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Xception CNN-Ensemble Learning Based Facial Emotion Recognition

Xception CNN-Ensemble Learning Based Facial Emotion Recognit...

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International Conference on Computing Communication control and Automation (ICCUBEA)

作者： Supriya Shirsath Vaishali Vikhe Pratap Vikhe Dept. of Computer Engineering Pravara Rural Engineering College Ahmednagar India Dept. of Instrumentation & Control Engineering Pravara Rural Engineering College Ahmednagar India

ISBN: (纸本)9781665484527

Emotions in human is an effective medium to study the mindset of a person. Since, expression on the face of human is significant approach to understand the condition and communicate with him to release the pressure or stress of person. The emotions and behavior have a strong relationship and is noteworthy in non-verbal form of communication. The advancement in medical science and use of image processing tools like Artificial Intelligence (AI) and Machine Learning (ML), can be helpful to recognize emotion, detect stress and depression level of a person. Thus, in this research work emotion recognition system is developed using Convolution Neural Networks (CNNs) with increased dept. and width. CNNs have been shown to enhance prediction accuracy. In terms of proper emotion categorization and accurate prediction, the suggested ensemble technique is effective. In this paper Xception CNN architecture is proposed for accurate facial emotions prediction along with an ensemble model, using Max Voting ensemble technique, mainly contributes in accurate classification. In proposed technique trained CNN models were loaded and for each trained model prediction probabilities were generated. Furthermore, it is then used for Max Voting to generate final emotion prediction. The CNN models are trained and evaluated on FER-2013 dataset.

关键词： Training Emotion recognition Computational modeling Face recognition Anxiety disorders Neural networks Machine learning

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Jawhar Ghommam Raouf Fareh Ibrahim Al Naimi Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Laboratory of Control Energy Management ENIS Tunisa Electrical Engineering Dept. University of Sharjah UAE

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach.

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Direct data-driven LPV control of nonlinear systems: An experimental result

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IFAC-PapersOnLine 2023年第2期56卷 2263-2268页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology 5600MB Eindhoven The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control 1111 Budapest Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

关键词： Data-Driven control Linear Parameter-Varying Systems

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Computationally efficient predictive control based on ANN state-space models

arXiv

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arXiv 2023年

作者： Hoekstra, Jan H. Cseppento, Bence Beintema, Gerben I. Schoukens, Maarten Kollár, Zsolt Tóth, Roland Dept. of Measurement and Information Systems The Budapest University of Technology and Economics Hungary The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system. © 2023, CC BY-SA.

关键词： Neural networks

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Fuzzy Logic controller for the Chemotherapy of Brain Tumor

Fuzzy Logic Controller for the Chemotherapy of Brain Tumor

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Muhammad Zubair Daniela Iacoviello Iqra Shafeeq Mughal Dept. Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. di Ingegneria Electrica Electronica e Informatica DIEEI University of Catania Catania Italy

An advanced Fuzzy Logic controller (FLC) that considers all the states of the brain tumor system is designed for the chemotherapy treatment. A Mamdani-type FLC is proposed for dynamically controlling the chemotherapy drug for the tumor system; the chemotherapy treatment of brain tumors requires advanced strategies which mainly depend upon the severity of the tumor. In this work, the advanced FLC designed aims both at determining the amount of chemotherapy to eliminate tumor cells, and at preserving the minimum amount of healthy and immune cells. The controller's performance is verified using MATLAB software based on different control parameters, showing its effectiveness in reducing the tumor cells. It has shown favorable results in terms of steady-state error, rate of convergence, and amount of drug consumed.

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An Efficient GPU-based Implementation for Noise Robust Sound Source Localization

arXiv

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arXiv 2025年

作者： Lin, Zirui Takigahira, Masayuki Terakado, Naoya Gulzar, Haris Busto, Monikka Roslianna Eda, Takeharu Itoyama, Katsutoshi Nakadai, Kazuhiro Amano, Hideharu Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan NTT Software Innovation Center Tokyo Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

Robot audition, encompassing Sound Source Localization (SSL), Sound Source Separation (SSS), and automatic Speech Recognition (ASR), enables robots and smart devices to acquire auditory capabilities similar to human hearing. Despite their wide applicability, processing multi-channel audio signals from microphone arrays in SSL involves computationally intensive matrix operations, which can hinder efficient deployment on Central Processing Units (CPUs), particularly in embedded systems with limited CPU resources. This paper introduces a GPU-based implementation of SSL for robot audition, utilizing the Generalized Singular Value Decomposition-based Multiple Signal Classification (GSVD-MUSIC), a noise-robust algorithm, within the HARK platform, an open-source software suite. For a 60-channel microphone array, the proposed implementation achieves significant performance improvements. On the Jetson AGX Orin, an embedded device powered by an NVIDIA GPU and ARM®Cortex™-A78AE v8.2 64-bit CPUs, we observe speedups of 4645.1× for GSVD calculations and 8.8× for the SSL module, while speedups of 2223.4× for GSVD calculation and 8.95× for the entire SSL module on a server configured with an NVIDIA A100 GPU and AMD EPYC™ 7352 CPUs, making real-time processing feasible for large-scale microphone arrays and providing ample capacity for real-time processing of potential subsequent machine learning or deep learning tasks. © 2025, CC BY-NC-ND.

关键词： Acoustic generators

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Constructing strings avoiding forbidden substrings 32

Constructing strings avoiding forbidden substrings

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32nd Annual Symposium on Combinatorial Pattern Matching, CPM 2021

作者： Bernardini, Giulia Marchetti-Spaccamela, Alberto Pissis, Solon P. Stougie, Leen Sweering, Michelle CWI Amsterdam Netherlands Dept. of Computer Automatic and Management Engineering Sapienza University of Rome Italy Vrije Universiteit Amsterdam Netherlands ERABLE Team Lyon France

ISBN: (纸本)9783959771863

We consider the problem of constructing strings over an alphabet Σ that start with a given prefix u, end with a given suffix v, and avoid occurrences of a given set of forbidden substrings. In the decision version of the problem, given a set Sk of forbidden substrings, each of length k, over Σ, we are asked to decide whether there exists a string x over Σ such that u is a prefix of x, v is a suffix of x, and no s ϵ Sk occurs in x. Our first result is an O(|u| + |v| + k|Sk|)-time algorithm to decide this problem. In the more general optimization version of the problem, given a set S of forbidden arbitrary-length substrings over Σ, we are asked to construct a shortest string x over S such that u is a prefix of x, v is a suffix of x, and no s ϵ S occurs in x. Our second result is an O(|u| + |v| + ||S|| · |Σ|)-time algorithm to solve this problem, where ||S|| denotes the total length of the elements of S. Interestingly, our results can be directly applied to solve the reachability and shortest path problems in complete de Bruijn graphs in the presence of forbidden edges or of forbidden paths. Our algorithms are motivated by data privacy, and in particular, by the data sanitization process. In the context of strings, sanitization consists in hiding forbidden substrings from a given string by introducing the least amount of spurious information. We consider the following problem. Given a string w of length n over Σ, an integer k, and a set Sk of forbidden substrings, each of length k, over Σ, construct a shortest string y over Σ such that no s ϵ Sk occurs in y and the sequence of all other length-k fragments occurring in w is a subsequence of the sequence of the length-k fragments occurring in y. Our third result is an O(nk|Sk| · |Σ|)-time algorithm to solve this problem. © Giulia Bernardini, Alberto Marchetti-Spaccamela, Solon P. Pissis, Leen Stougie, and Michelle Sweering.

关键词： Data privacy

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops

A Framework Leveraging Robotics and Machine Learning Technol...

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Nikolaos Kounalakis Emmanouil Kalykakis Dimitrios Kosmopoulos John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework.

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