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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2021年第3期83卷 47-58页

作者： Gliga, Lavinius Ioan Popescu, Dumitru Chafouk, Houcine Faculty of Automatic Control and Computer Science "Politehnica" University of Bucharest Romania IRSEEM ESIGELEC UniRouen Normandy University France

Gearless wind turbines can be outfitted with Permanent Magnet Synchronous Generators. These may be affected by impairments. Machine Current Signature Analysis can be utilized to find these faults, as they insert additional harmonics. These manifestations also exist in the residuals of the generated currents, calculated via an Extended Kalman Filter. The Fast Fourier Transform is utilized to compute the residuals spectrum, to find these supplementary harmonics. In this work, the Goertzel Filter is proposed as a better substitute to the Fast Fourier Transform, for the fault detection of such a generator. The Goertzel Filter was implemented in MATLAB / Simulink and the obtained outcomes confirm its effectiveness. Moreover, a method to estimate the covariance matrix of process noise is briefly presented. This matrix is necessary for the implementation of a Kalman filter. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Extended Kalman filters

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Split Cost Function based Single-Horizon Finite-Set Model Predictive control for Boost Converter with Loading Current Estimation

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tatari, Fatemeh Rezayof Bizhani, Hamed Iwanski, Grzegorz Warsaw University of Technology Faculty of Electrical Engineering Warsaw Poland Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

This paper presents a robust single-horizon finite-set model predictive control (FS-MPC) method for controlling the output voltage of boost converters. Unlike the long-horizon FS-MPC approach, which addresses the non-minimum phase (NMP) behavior of the converter by extending the prediction horizon (thereby increasing computational effort), the proposed method ensures stable voltage regulation by incorporating inductor current regulation into the cost function. To achieve this, the desired inductor current is calculated based on input-output power balance theory. Unlike recent studies that require an additional sensor to measure the loading current, this paper develops a loading current estimation approach to eliminate the need for extra current sensors, thereby enhancing the robustness of the proposed FS-MPC method. Additionally, over-current protection, which is typically absent in conventional FS-MPC used for direct voltage regulation, is also incorporated into the proposed method. The conventional cost function is also modified to tackle the NMP behavior more effectively. The performance of the proposed method in regulating the output voltage is evaluated through simulation results. Finally, its effectiveness across a wide range of output power and various load characteristics, including resistive, constant power, and constant current loads, is validated via experimental results. The results demonstrate that the proposed method is not only able to eliminate the overshoot and undershoot, but can also improve the settling time by 41% compared to a well-tuned conventional cascaded PI controller. In addition, the proposed method can reduce the computational burden by 84% compared to the conventional long-horizon FS-MPC approach. © 2013 IEEE.

关键词： Cost functions

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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International Conference on control Systems and computer science (CSCS)

作者： Viorel Dediu Andreea Udrea Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Bucharest Romania

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia.

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How capable are state-of-the-art language models to cope with sarcasm? 24

How capable are state-of-the-art language models to cope wit...

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Baroiu, Alexandru -Costin Trausan-Matu, Stefan Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Romanian Academy Research Institute for Artificial Intelligence 'Mihai Drǎganescu' Academy of Romanian Scientists Bucharest Romania

ISBN: (纸本)9798350313390

Sarcasm detection has established itself as one of the more difficult Natural Language Processing tasks, due to the complex nature of sarcasm. This paper aims to benchmark the performance of state-of-the-art models like BERT, RoBERTa, ALBERT and GPT-3 when faced with this task. The dataset selected is MUStARD, which has increased in popularity in recent years, especially for multimodal tasks, and is one of the most qualitative and data rich dataset. An untuned GPT-3 based model was selected as the baseline and all the other models were fine-tuned using the textual data present in MUStARD, mainly the context and utterance information. The best performer was found to be the GPT-3 fine-tuned model, with an F1 score of 77. This is in line with the reported feats of GPT-3 based models that have popularized in recent months and reaffirms the superiority of GPT-3. Future avenues of research are then presented and explored, and the conclusions are drawn. © 2023 IEEE.

关键词： Benchmarking

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Research on path planning algorithm of takeaway robots for terminal delivery

Research on path planning algorithm of takeaway robots for t...

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2nd International Conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024)

作者： Yuhang Wan Faculty of Automatic Control Electronics and Computer Science Silesian Technical University Gliwice 44-100 Poland

ISBN: (数字)9781837242672

Takeaway delivery has become an important part of urban life. However, terminal deliveries are often blocked by security measures that often prevent them from arriving on time. Designing a takeaway delivery robot can solve this problem to a large extent. In this paper, path planning algorithm level of designing a takeaway robot is researched. In this paper, the research is carried out by closely focusing on the current stage of the path planning algorithm related difficulties. Three typical algorithms are selected for simulation testing within the scope of graph search algorithms. These three algorithms are Dijkstra algorithm, A* algorithm and rapidly-exploring random tree (RRT) algorithm. The final comparison is made with the simulation test results. And to find the optimal path planning algorithm for takeaway delivery robots within the range of graph search algorithms. This paper provides a reference for the path planning algorithm level of takeaway delivery robot design. At the same time, it helps to improve the delivery service of takeaway terminal.

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Applying NoSQL Data Adapter with the Learning Paths mechanism for better knowledge transfer in the age of distance learning 26

Applying NoSQL Data Adapter with the Learning Paths mechanis...

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

作者： Werner, Aleksandra Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Str. Gliwice44-100 Poland

Database technology applies to many engineering problems. Hence it is the subject of study at many university majors. Since teaching practical usage of theory at a distance is difficult or even impossible, we present novel mechanisms that significantly enrich e-learning platform functionalities in individualized learning of databases installed on the teacher-managed server. The crucial component of our proposal, Data Adapter, allows for interactive querying and manipulating of the NoSQL databases through an e-learning platform. The Adapter is complemented by the Learning Paths component, which enables teachers the creation of individualized didactic paths for students with special educational needs. The e-learning platform load tests confirm that new activities do not substantially affect the comfort of students' work. Its impact on learning effectiveness was verified by rating the competence test for the group of 104 course participants. Half of them were the participants of the e-learning NoSQL course and used the developed mechanisms, while the remaining, control, group was taught with the conventional methods. The research shows that the proposal successfully improves teaching modern NoSQL languages. The scores improvement in the e-learning group of students compared to results in the control group is noticeable, and observed differences are statistically significant. © 2022 The Authors. Published by Elsevier B.V.

关键词： Students

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Creating a personality leisure-time model using genetic algorithms, behavioral psychology, and a happiness dataset

Creating a personality leisure-time model using genetic algo...

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International Conference on control Systems and computer science (CSCS)

作者： Ciprian-Ionut Nutescu Mariana Mocanu Faculty of Automatic Control and Computer Science University Politehnica Bucharest Bucharest Romania

In this paper, we proposed a genetic algorithm based on behavioral psychology developed by Carl Gustav Jung (16 Personalities model), in which we describe the person’s behavioral leisure and ludic behavioral features regarding his personality. The model used for inference is based on 40 years of psychology research and studies from the book “The 16 Personalities types that determinate how we live, love and work” by Otto Kroger and Janet M. Thuesen, published in 1988. To generate the leisure model, we are using genetic algorithms. To improve the leisure model, we are reinforcing it using weight-based fitness functions with inference extracted from the HappyDB dataset. In this dataset, persons around the world express the most valuable/happy moments in their life. The leisure model describes how they would prefer spending their time and how this will bring the most enjoyment and reward (human reward psychological model).

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BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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Brown Marmorated Stink Bug Detection Using Multiple Neural Networks

Brown Marmorated Stink Bug Detection Using Multiple Neural N...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Andrei Vicol Loretta Ichim Dan Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

The identification and management of pest infestations in a timely manner have been an ever-pressing issue, more so than ever now, when new pests have arrived from around the world and governments are encouraging farmers to use fewer and fewer pesticides. The use of drones and artificial intelligence in the monitoring of orchards has taken a strong rise in recent years due to the advantages offered by efficiency, coverage, and price. A particularly dangerous insect in fruit production is the brown marmorated stink bug (BMSB). The paper seeks to look at a performance comparison in BMSB detection between three states of the art models of image detection and classification: MobileNetV2, Xception, and EfficientNet. Each network model was slightly modified being thus adapted for this application and the results obtained had high accuracy, between 97% and 99%.

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