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ELEKTRO

作者： Marek Bujň á k Rastislav Pirní k Pavol Kuchá r Alž beta Kaná liková Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of &#x017D ilina &#x017D ilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

The spherical robots are becoming very popular in the field for many scientists and researchers working with surveillance, military, or transportation systems. This paper deals with comparing of each driving mechanism of the spherical robot. The vast majority of spherical robots only have inertial sensors, which they use for movement, positioning, travel balancing, etc. Few spherical robots can interact with the environment. Those who can do this have sensors that can measure the properties of the environment or can influence the environment in some way. The article also deals with the possibility to use sensors like cameras, gas sensors, thermocouples, and LiDARs. Data from these sensors will be processed using artificial intelligence techniques to detect anomalies in the environments. Chosen sensors can expand a robot's sensing capabilities which are usually very narrow. The aim of the article is to design s special type of spherical robot. If we join all these components together we can create a reconnaissance spherical robot for rescue services special designated for tunnels or closed *** prototype is a financially inexpensive robot, which is also suitable for the teaching process, as an element that collects data from the environment (sensors, video, etc.), which will be used for working with AI tools.

关键词： Temperature measurement Laser radar Robot vision systems Thermocouples Reconnaissance Robot sensing systems Sensors

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ELEKTRO

作者： Juraj Ž dá nsky Jozef Hrbč ek Karol Rá stoč ný Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of &#x017D ilina &#x017D ilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

Safety PLCs have become a popular device for implementing safety functions in industrial applications. Their correct use is the prerequisite for achieving the required safety integrity level (SIL). This is closely related to the parametrization of the safety PLC (setting of the parameters). This paper deals mainly with parameters influencing the safety of two-channel connection of sensors to the safety PLC.

关键词： Performance evaluation Analytical models Computer architecture Probabilistic logic Time measurement Safety Sensors

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First Order Features Extraction from Thermal Images for Human State Recognition

First Order Features Extraction from Thermal Images for Huma...

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International Symposium on electrical and Electronics engineering (ISEEE)

作者： Simona Moldovanu Sorin Pavel Dorel Aiordachioaie Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images.

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Comparison of Various Mathematical Models of 8/20 µs Current Surge and their Influence on RFID Marker

Comparison of Various Mathematical Models of 8/20 µs Curren...

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ELEKTRO

作者： Peter Vestenický Alž beta Kaná liková Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of &#x017D ilina &#x017D ilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

In technical praxis the underground facility networks such as gas and water pipes or telecommunication and energetic cables are marked by special RFID markers to enable localization and identification of the network. Due to influence of climatic effects such as lightning part of the lightning surge current may be induced into the markers. This paper gives a comparison of various mathematical models of the surge current induced into conductors and these mathematical models are used to calculate current waveform in the markers. Finally, the voltage waveform across the capacitor in the marker resonant circuit is calculated to enable proper dimensioning of the capacitor and to increase the long term marker reliability.

关键词： Analytical models Capacitors Resonant frequency RLC circuits Voltage Mathematical models Surges

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Emotion Recognition in Human Thermal Images with Artificial Intelligence technology

Emotion Recognition in Human Thermal Images with Artificial ...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Sorin Pavel Simona Moldovanu Dorel Aiordachioaie Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy.

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features

Diabetic Retinopathy Image Classification Using Machine Lear...

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International Symposium on electrical and Electronics engineering (ISEEE)

作者： Mihaela Miron Anisia Culea-Florescu Simona Moldovanu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task.

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Individual pitch controller with static inverted decoupling for periodic blade load reduction on monopile offshore wind turbines

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Ocean engineering 2025年 334卷

作者： Lara, Manuel Ruz, Mario L. Mulders, Sebastiaan Paul Vázquez, Francisco Garrido, Juan Department of Electrical Engineering and Automation University of Cordoba Campus of Rabanales Cordoba14071 Spain Department of Mechanics University of Cordoba Campus of Rabanales Cordoba14071 Spain Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Individual pitch control (IPC) is a technique used to reduce periodic blade loads in wind turbines. It generally uses the multiblade coordinate transformation to convert blade load measurements from a rotating frame into a two-axes non-rotating frame. Although these non-rotating axes are assumed to be decoupled, studies reveal persistent interactions. Reducing this coupling, such as by introducing an azimuth offset, enhances IPC performance. This study explores the impact of static inverted decoupling, which decouples the process in the steady state, on IPC performance. The proposed IPCs are adaptive, scheduling controller and decoupling gains based on operational conditions. In such IPC designs, the integral gains of the diagonal controllers and the decoupling elements can either be the same or different. These methods were validated on a simulated 15 MW wind turbine. controller parameter optimization was accomplished through genetic algorithms to minimize blade fatigue loads, measured via the damage equivalent load (DEL). Results indicate that incorporating static inverted decoupling into IPC improves blade load reduction without increasing pitch actuator effort. IPCs with similar integral gains and matching absolute values in decoupling elements achieved the best balance between DEL reduction and complexity with minimal actuator effort, while additional optimization parameters provided negligible improvements. © 2025 Elsevier Ltd

关键词： Genetic algorithms

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Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: A case study

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Materials Today: Proceedings 2022年 56卷 194-199页

作者： Jain, Sarika Rathee, Sarita Kumar, Ajay Sambasivam, Anivel Boadh, Rahul Choudhary, Tushar Kumar, Parveen Kumar Singh, Pravin Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram122505 India Department of Mechanical Engineering Saveetha Engineering College Thandalam Tamilnadu Chennai602105 India School of Basic and Applied Sciences K R Mangalam University Haryana Gurgaon122103 India PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Mechanical Engineering Rawal Institute of Engineering and Technology Haryana Faridabad121004 India Department of Mechanical and Automation Engineering Amity University Jharkhand Ranchi834001 India

In this paper, the estimation capacities of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) are examined to forecast temperature at two pressure level 400 hPa and 980 hPa approximately over Delhi Safdarjung Airport Station. Different meteorological parameters such as temperature, relative humidity, dew point temperature, mixing ratio, potential temperature has been taken as an input for ANN and MLR models. Statistical bases give us forecasting of temperature by using above mentioned parameters. ANN gives us better results in forecasting temperature as compared to MLR and if compared different techniques of ANN then Multiple layer Artificial Neural Network (MLANN) proved to be best as compared to Single layer Artificial Neural Network (SLANN). © 2022

关键词： Multiple linear regression

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Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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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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