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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 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. © 2024 The Authors.

关键词： Data assimilation

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes 28th

Observations of data characteristics and irregularities thro...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 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. © 2024 The Authors.

关键词： Data assimilation

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Multi-Network Blood Cell Classification System Based on Decision Fusion

Multi-Network Blood Cell Classification System Based on Deci...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ichim, Loretta Iordan, Cosmin-Andrei Popescu, Dan Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Institute of Virology Bucharest Romania Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The classification and identification of blood cells from microscopic images is very important for the detection of serious diseases. To increase the efficiency of the identification and counting of cells from 8 classes such as Basophils, Eosinophils, Erythroblasts, Immature Granulocytes, Lymphocytes, Monocytes, Neutrophils, and Thrombocytes, a special advance was taken using artificial intelligence for image analysis. This article proposes a collective intelligence decision system based on the fusion of individual decisions of four efficient convolutional neural networks, VGG 16, Xception, ResNet-50, and NasNetLarge. The fusion is based on the efficiency of each network's performance in recognizing (correctly classifying) the membership class of the analyzed cell. In this sense, a weight is established for each network associated with a certain class. The global system has better performances than each individual network and is better or comparable to those reported in recent works. © 2022 IEEE.

关键词： Classification (of information)

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Optimizing a Convolutional Neural Network using Particle Swarm Optimization 14

Optimizing a Convolutional Neural Network using Particle Swa...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Mihai, Alexandru-Cosmin Iancu, David-Traian Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

This study presents the application of the Particle Swarm Optimization (PSO) algorithm, a swarm algorithm which is based on the particle movement, to optimize the parameters of a Deep Neural Network (DNN), namely an architecture based on Convolutional Neural Networks (CNN). The model is optimized with respect to the image classification task on the MNIST dataset, consisting of images of handwritten digits. The study presents the results of training the model using different PSO hyperparameters and also compares the obtained performances with those obtained when training the model using gradient based optimizers such as Stochastic Gradient Descend (SGD) and Adam. © 2022 IEEE.

关键词： Convolutional neural networks

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Cat Swarm Optimization for Solving the N-Queens Problem 14

Cat Swarm Optimization for Solving the N-Queens Problem

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Clara, Barbu Iancu, David-Traian Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

Many swarm intelligence algorithms study the behavior of animals and make animal-based systems for solving various tasks. Cat swarm optimization (CSO) is a swarm intelligence algorithm which was originally inspired by the resting and tracking behaviors observed naturally in cats. The N-Queens problem is a classical and complex constraint satisfaction problem which has been used as a benchmark for testing AI techniques for years. This paper aims to adapt the original CSO algorithm to the N-Queens problem by replacing the continuous addition/subtraction operations with swapping operations. The results report very fast convergence at smaller numbers of queens and point towards quicker convergence than similar work at larger numbers of queens, with improvements still needed for perfecting the algorithm. © 2022 IEEE.

关键词： Animals

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A Hybrid Temporal-Magnitude Based Sampling for Event-Based control 3

A Hybrid Temporal-Magnitude Based Sampling for Event-Based C...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Sanchez-Moreno, Jose Romero-Perez, Julio-Ariel Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been demonstrated to be suitable for control systems, reporting benefits in different categories. However, these sampling strategies also present some drawbacks, discussed in this contribution. Aiming to address some of these drawbacks, a new sampling strategy is proposed gathering characteristics of both predecessors. © 2024 IEEE.

关键词： Decentralized control

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Contribution to the Sensors Faults Diagnosis of a Variable Speed Wind Turbine: Multimodel Approach 11

Contribution to the Sensors Faults Diagnosis of a Variable S...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Sayahi, Yosra Allouche, Moez Ghamgui, Mariem Moreau, Sandrine Mehdi, Driss Postal Box 1173 Sfax3038 Tunisia University of Poitiers Laboratory of Computer Science and Automatic Control for Systems France

ISBN: (纸本)9798350304886

This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer's bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods. © 2023 IEEE.

关键词： Wind turbines

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Integrating photovoltaic systems into HVAC control using fuzzy algorithms for optimizing thermal comfort 30

Integrating photovoltaic systems into HVAC control using fuz...

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30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Boicu, Mihaela-Gabriela Dobrea, Marius-Alexandru Vasluianu, Mihaela Olteanu, Gabriela-Valentina Neculoiu, Giorgian University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Bucharest Romania

ISBN: (纸本)9798331539511

This article presents a study on the use of photovoltaic panels for storing electrical energy intended to power an HVAC unit placed in a residential room. To highlight this aspect, two specific scenarios will be considered. Thus, we aim to analyze whether the generated and stored energy is sufficient to fulfil the thermal comfort requirements of the user, while also considering the primary objective of minimizing energy consumption. To achieve this goal, the HVAC unit will be controlled by implementing a fuzzy system that determines its optimal activation based on the comfort level obtained from the user. The fuzzy system presents as inputs both environmental parameters and specific personal perception parameters of the user. The research results provide an insight into the efficiency of implementing photovoltaic energy storage systems for residential applications and propose solutions for optimizing energy consumption in the context of ensuring adequate thermal comfort. Thus, the study contributes to the development of sustainable and personalized energy solutions for smart homes. © 2024 IEEE.

关键词： Thermal comfort

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Error Dependent Sampling to Reduce Transient Events in Event Based control 3

Error Dependent Sampling to Reduce Transient Events in Event...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Romero-Perez, Julio-Ariel Sanchez-Moreno, Jose Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

One of the objectives of event-based control is the reduction of generated events to perform an appropriate process control. Among the event generation techniques used to this end, those quantifying the error signal can be found more frequently for its simplicity of implementation. However, they entail a drawback in the form of trade-off between event generation and performance. Aiming to reduce the number of events, specifically in the transient response, without degrading the performance, an error dependent sampling scheme is studied in this work. © 2024 IEEE.

关键词： Electromagnetic transients

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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