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arXiv 2021年

作者： Alanwar, Amr Stürz, Yvonne Johansson, Karl Henrik Computer Science & Electrical Engineering Department Jacobs University Bremen Germany Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Model Predictive Control Laboratory University of California Berkeley United States

We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven reachable regions is proposed. The data-driven reachable regions are based on a matrix zonotope recursion and are computed based on only noisy input-output data of a trajectory of the system. We assume that measurement and process noise are contained in bounded sets. While we assume knowledge of these bounds, no knowledge about the statistical properties of the noise is assumed. In the noise-free case, we prove that the presented purely data-driven control scheme results in an equivalent closed-loop behavior to a nominal model predictive control scheme. In the case of measurement and process noise, our proposed scheme guarantees robust constraint satisfaction, which is essential in safety-critical applications. Numerical experiments show the effectiveness of the proposed data-driven controller in comparison to model-based control schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： controllers

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integrated model of positioning and situation monitoring system using LORA, WiFi, GSM technology 3

integrated model of positioning and situation monitoring sys...

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3rd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2021

作者： Tymochko, Oleksandr Afanasiev, Yurii Fustii, Vadym Afanasiev, Volodymyr Fediuk, Serhii Telyatnik, Boris Flight Faculty ivan Kozhedub Kharkiv National Air Forces University Kharkiv Ukraine V.V. Popovskyy Department of infocommunication Engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Combat Application and Operation of Automatic Control Systems ivan Kozhedub Kharkiv National Air Forces University Kharkiv Ukraine

ISBN: (纸本)9781665438452

The article analyzes the functioning of monitoring and positioning systems at complex technical objects. it is shown that when objects are located in remote areas, the absence of cellular communication is possible. To solve the problems of monitoring such territories, the complex application of such communication technologies as LoRa, Wi-Fi is considered. Based on the requirements for the monitoring system, a multilevel integrated model is proposed that allows using a unified approach to both hardware and software components of the system. Based on the proposed model, an experimental study of the positioning and monitoring system was carried out. The properties of the system operability have been confirmed under conditions of limited functioning of individual elements. © 2021 iEEE.

关键词： Monitoring

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Performance Investigation of Instantaneous Reactive Power control for 5 Level-Neutral Point Clamped Grid-Connected Photovoltaic Inverter

Performance Investigation of Instantaneous Reactive Power Co...

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Electrical engineering and Automatic control (ICEEAC), International Conference on

作者： Oumaymah Elamri Alla Eddine Toubal Maamar Charaf Chermite Moulay Rachid Douiri Mostefa Kermadi Saad Mekhilef LAMIGEP multidisciplinary research laboratory École Marocaine des Sciences de l'Ingénieur (EMSI) of Marrakech Marrakech Morocco Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara of Boumerdes Boumerdes Algeria Physics Department Control and Computer Science for Intelligent Systems and Green Energy Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco School of Engineering University of Leicester Leicester United Kingdom Power Electronics and Renewable Energy Research Laboratory (PEARL) Kuala Lumpur Malaysia Swinburne University of Technology Hawthorn Vic Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The utilization of solar energy in grid-connected photovoltaic installations has become increasingly significant. This article introduces a photovoltaic system seamlessly integrated with a three-phase network. The system employing a five-level Neutral Point Clamped (NPC) voltage source inverter is designed for a single-step connection to enhance power quality in the distribution network. The control technique employed for the efficient functioning of the photovoltaic system is based on the notion of instantaneous reactive power. Notably, this strategy is straightforward, operates the system throughout the day, and eliminates the need for Phase-Locked Loop (PLL) usage. The Pulse-Width Modulated (PWM) Current controller sends switching signals to the NPC multilevel inverter, which does a great job of dynamic performance under steady-state and transient situations. The proportional-integral controller regulates the voltage of the Uninterruptible Power Supply DC bus capacitors. The proposed system undergoes comprehensive simulation validation under various irradiation levels.

关键词： Photovoltaic systems Reactive power Voltage source inverters Switches Solar energy Steady-state Transient analysis

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Normal Dynamics: Solving Newton's Equations of Motion in the Reciprocal Space

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SSRN 2023年

作者： Cammarata, Antonio Dašić, Miljan Nicolini, Paolo Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague16627 Czech Republic Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia Institute of Physics Czech Academy of Sciences Na Slovance 2 Prague18200 Czech Republic

We here present the Normal Dynamics technique, which provides a way to integrate the Newton's equations of motion by sampling the reciprocal space. We discuss the sampling strategy and show how it is capable to produce dynamical trajectories at the ab initio level on an ordinary desktop computer. The reciprocal space sampling allows to i) obtain a systematic improvement of the results accuracy and a fine control of the computational load, and ii) account for distortions realized across large atomic distances without the use of large unit cells. We present three case studies: the first two are aimed to show the capabilities of the method while in the latter we investigate on the characterization of Raman spectra at different temperatures in MoS2/MX2 transition metal dichalcogenide heterostructures. Finally, we discuss how the sampling procedure is general and can be used to simulate periodic, semiperiodic and finite systems such as crystals, slabs, nanoclusters or molecules. © 2023, The Authors. All rights reserved.

关键词： Equations of motion

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Algorithmic Optimization of Vehicle Detection System based on Hybrid Metaheuristic with Machine Learning

Algorithmic Optimization of Vehicle Detection System based o...

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International Conference on Sciences and Techniques of Automatic control and Computer engineering (STA)

作者： S. Bougharriou F. Hamdaoui A. Mtibaa Electrical Department Laboratory of Control Electrical Systems and Environment National Engineering School of Monastir (ENIM) University of Monastir Monastir Tunisiam Electronic and Micro-Electronic Laboratory University of Monastir Monastir Tunisia National Engineering School of Sfax (ENIS) University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

In this paper a robust and accurate car detection system has been developed. To successfully detect vehicles in the road and taking into account the different weather conditions of the captured scenes, we use the Histogram of Oriented Gradient (HOG) method to extract features from an image and the support vector machine linear (SVM) for classification. To optimize this car detection system, a Particle Swarm Optimization (PSO) technique is used for the selection of the extracted features.

关键词： Support vector machines Machine learning algorithms Roads Urban areas Metaheuristics Feature extraction Automobiles

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Intellectual capital management and the innovation process: Does one size fit all?

Intellectual capital management and the innovation process: ...

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International Conference of Modelling and Simulation in engineering, Economics, and management, MS 2018

作者： Salazar-Elena, Juan Carlos Cancino, Christian A. López López, Asunción Guimón de Ros, José Department of Economic Structure and Development Economics Autonomous University of Madrid Campus de Cantoblanco Madrid Spain Department of Management Control and Information Systems University of Chile Diagonal Paraguay 257 Santiago Chile

ISBN: (纸本)9783030154127

Intellectual Capital management (ICM) research has focused predominantly on demonstrating the link between ICM and innovative performance from a general, context-free perspective. This paper analyses deeper implications of ICM showing the inadequacy of this one-size-fits-all approach, and introduces a more realistic view of the role of ICM on innovation process. We propose an analytical framework where differences in ICM strategies are explained by differences in the environment faced by firms and characterized by disparate levels of threats posed by potential imitators. Although legal protection of intellectual property rights has been traditionally the center of attention on this topic, the firm’s strategy might be far more complex involving alternative ICM activities guided by the incentives of the environment. Using panel data for Spanish SMEs over the 7-year period between 2008 and 2014, we provide evidence consistent with this approach. Empirical analysis shows that certain ICM strategies are profitable in specific environments, but might be very unattractive in other contexts where other strategies prove to be more efficient. These findings contribute to the development of a more nuanced interpretation of the role played by ICM on the innovation process. © 2020, Springer Nature Switzerland AG.

关键词： Laws and legislation

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GCNPMDA: Human Microbe-Disease Association Prediction by Hirarchical Graph Convolutional Network with Layer Attention

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SSRN 2024年

作者： Wu, Chuanyan Lin, Bentao Zhang, Huanghe Xu, Da Gao, Rui Song, Rui Liu, Zhi-Ping De Marinis, Yang School of Intelligent Engineering Shandong Management University No.3500 Dingxiang Road Shandong Jinan250357 China School of Control Science and Engineering Shandong University No.17923 Jingshi Road Shandong Jinan250061 China Center for Robotics School of Control Science and Engineering Shandong University No.17923 Jingshi Road Shandong Jinan250061 China School of Mathematics Science Liaocheng University No.1 Hunan Road Dongchangfu District Shandong Liaocheng252000 China Department of Clinical Sciences Lund University Solvegatan 19 Skaane Malmo22184 Sweden Oxford Centre for Diabetes Endocrinology & Metabolism Radcliffe Department of Medicine University of Oxford Old Road OxfordOX3 7LE United Kingdom

Microorganisms play a crucial role in various physiological processes. Changes in microbial communities can serve as potential biomarkers for diseases. However, the association between microorganisms and diseases is still unclear. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe-microbe similarities, and disease-disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe-microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease-disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model's predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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First Studies to Apply the Theory of Mind Theory to Green and Smart Mobility by Using Gaussian Area Clustering

First Studies to Apply the Theory of Mind Theory to Green an...

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2021 International Conference of Yearly Reports on Informatics, Mathematics and engineering, ICYRIME 2021

作者： Brandizzi, Nicolo Russo, Samuele Brociek, Rafal Wajda, Agata Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00135 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland

This paper investigates emerging pattern in users green and smart mobility. Our focus is finding an optimal clusterization of vehicles based on user demand in the metropolitan city of Barcelona and analyze the users' behavior with a theory of mind framework. The notions of clusters, algorithms definitions and optimization procedure are introduced in this study. The following assumption will be considered: the task is a dynamic multi-agent problem settled in a city on a group of scooters. Given the conditions and the data set, the algorithm consists of two phases. First we estimate the vehicles density during the day, hour and location, and we analyze the users' behavior and draw conclusion on their habits. Then we propose a unsupervised clustering technique based on Gaussian area, that takes into consideration point of interests and dis-interest, shaping the area accordingly. Our focus is on simplicity and reproducibility, for this reason our formulation and solution can be considered a general approach that can be adapted based on specific needs. © 2021 Copyright for this paper by its authors

关键词： Multi agent systems

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Intelligent model for assessing the suitability of water based on its chemical and biological parameters 24

Intelligent model for assessing the suitability of water bas...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Tynchenko, Yadviga Tynchenko, Vadim Degtyareva, Ksenia Kuznetsov, Georgy Kukartseva, Svetlana Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk Russia Department of Systems Analysis and Operations Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Applied Informatics Moscow Timiryazev Agricultural Academy Moscow Russia Center for Continuing Education Bauman Moscow State Technical University Moscow Russia Information-Control Systems Department Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia

ISBN: (纸本)9798331515799

This paper presents a study on the development and testing of a machine learning model for classifying water according to its purpose, based on chemical and microbiological indicators. The aim of the study is to create an accurate and stable model that can effectively determine the categories of water - drinking, industrial, agricultural and technical - based on the analysis of its qualitative characteristics. The gradient boosting method was chosen as the algorithm for classification, which demonstrated high accuracy and reliability when working with multidimensional data. The study included the processing of a synthetic dataset containing information on water parameters such as calcium concentration, total bacterial content, phosphate levels, alkalinity, sulfates and oxidizability. The analysis of the correlation matrix made it possible to identify the relationships between the features, which made it possible to optimize the operation of the model and improve its accuracy. The quality of the model was tested using accuracy, completeness and F1-score metrics, which demonstrated almost perfect results for all categories. In conclusion, it is concluded that the proposed model can be successfully used to automate the analysis of water quality in ecology, industry and agriculture. The use of intelligent technologies opens up new opportunities for monitoring water resources, increasing the efficiency and reliability of quality control. © 2025 IEEE.

关键词： Taxonomies

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