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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Craciunescu, Mihai Baicu, Diana Mocanu, Stefan Dobre, Cristian Faculty of Automatic Control and Computer Science Department of Automatic Control and Applied Informatics Bucharest Romania Rinftech Bucharest Romania

ISBN: (纸本)9781665439398

In this paper, a method of calculating the occupancy of a shelf will be presented. A vision pillar composed of two RGB cameras and two ToF depth cameras will be used to scan a shelf and determine the percentage of emptiness for the products. Since the cameras will scan a small section of the shelf at the time, a stitching method will be applied on both depth and RGB stream. In order to identify categories of products, along with their spatial delimitation within a shelf, a segmentation neural network is used. By combining the segmentation output with the depth information, the distance from the pillar to the product can be determined. Since this type of information does not help a human operator or a supervisor in understanding the state of the shelf or in making an appropriate decision, such as restocking, a method for computing the shelf occupancy is proposed. Optimization must be done to the processing algorithms to enable them to run on an embedded platform, which will allow for the implementation of the proposed architecture on a platform deployed in a production environment. All the computation is done on board of the mobile robot without transmitting the data to an external server. © 2021 IEEE.

关键词： Cameras

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance

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Procedia computer science 2024年 246卷 4421-4430页

作者： Urszula Stańczyk Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies.

关键词： Attribute domain Data irregularities Sub-concept Disjunct Pattern recognition Stylometry Authorship attribution

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Towards Empirical Transfer Function Estimation for Frequency Domain Closed-loop System Identification

Towards Empirical Transfer Function Estimation for Frequency...

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

作者： Amirreza Zaman Dina Shona Laila Wolfgang Birk Department of Computer Science Electrical and Space Engineering Automatic Control Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work introduces a method for closed-loop system identification using frequency analysis, employing Empirical Transfer Function Estimation (ETFE). By integrating optimization within a Monte Carlo framework, it enhances the precision of ETFE, resulting in minimized frequency response errors compared to actual system data. Leveraging controller information in an offline model fitting scheme, it achieves optimal realization of process dynamics. The method is evaluated on a data center rack-level cooling system, showing Bode magnitude plots of actual and estimated closed-loop and open-loop dynamics, with confidence intervals demonstrating algorithm consistency. Numerical evaluations confirm the feasibility and potential of the approach to improve offline closed-loop system identification performance in the frequency domain, beneficial for analysis and design. There will not be a comparative study for the introduced approach.

关键词： Monte Carlo methods Heuristic algorithms Fitting Transfer functions Estimation Process control Numerical models Closed loop systems Iterative methods Optimization

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Virtual and Remote Robotic Laboratory Using EJS, MATLAB and LabVIEW

arXiv

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arXiv 2024年

作者： Chaos, Dictino Chacón, Jesús Lopez-Orozco, Jose Antonio Dormido, Sebastián Department of Computer Science and Automatic Control UNED Juan del Rosal 16 Madrid28040 Spain Department of Computers Architecture and Automatic Control Complutense University Ciudad Universitaria Madrid28040 Spain

This paper describes the design and implementation of a virtual and remote laboratory based on Easy Java Simulations (EJS) and LabVIEW. The main application of this laboratory is to improve the study of sensors in Mobile Robotics, dealing with the problems that arise on the real world experiments. This laboratory allows the user to work from their homes, tele-operating a real robot that takes measurements from its sensors in order to obtain a map of its environment. In addition, the application allows interacting with a robot simulation (virtual laboratory) or with a real robot (remote laboratory), with the same simple and intuitive graphical user interface in EJS. Thus, students can develop signal processing and control algorithms for the robot in simulation and then deploy them on the real robot for testing purposes. Practical examples of application of the laboratory on the inter-University Master of Systems Engineering and automatic control are presented. © 2024, CC BY-NC-ND.

关键词： Laboratories

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera 3

Design and Realization of Unmanned Vehicle Autonomous Naviga...

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3rd International Conference on computer science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Guo, Tianxing Lin, Lianlei Liu, Huanyu Li, Junbao School of Electronics and Information Engineering Harbin Institute of Technology Department of Automatic Test and Control Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350372052

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions. © 2023 IEEE.

关键词： Cameras

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Efficient and Parallel Separable Dictionary Learning 27

Efficient and Parallel Separable Dictionary Learning

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Rusu, Cristian Irofti, Paul University Politehnica Bucharest Faculty of Automatic Control and Computers Department of Automatic Control and Computers Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Department of Computer Science Bucharest Romania

ISBN: (纸本)9781665408783

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising. © 2021 IEEE.

关键词： Image denoising

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Evaluation of importance for condition attributes based on quality of decision reducts 26

Evaluation of importance for condition attributes based on q...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Rough set theory

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