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arXiv 2017年

作者： Madeyski, Lech Kawalerowicz, Marcin Wroclaw University of Science and Technology Faculty of Computer Science and Management Wyb.Wyspianskiego 27 Wroclaw50-370 Poland Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics ul. Sosnkowskiego 31 Opole45-272 Poland CODEFUSION Sp. z o.o. Armii Krajowej 16/2 Opole45-071 Poland

We would like to present the idea of our Continuous Defect Prediction (CDP) research and a related dataset that we created and share. Our dataset is currently a set of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data we mine from software repositories. Our dataset embraces 1265 software projects, 30,022 distinct commit authors and several software process metrics that in earlier research appeared to be useful in software defect prediction. In this particular dataset we use TravisTorrent as the source of CI data. TravisTorrent synthesizes commit level information from the Travis CI server and GitHub open-source projects repositories. We extend this data to a file change level and calculate the software process metrics that may be used, for example, as features to predict risky software changes that could break the build if committed to a repository with CI enabled. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Pupil detection algorithm based on RANSAC procedure

Pupil detection algorithm based on RANSAC procedure

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Radu Gabriel Bozomitu Alexandru Păsărică Robert Gabriel Lupu Cristian Rotariu Eugen Coca Universitatea Tehnica Gheorghe Asachi din Iasi Iasi RO Faculty of Electronics Telecommunication and Information Technology “Gheorghe Asachi” Technical University Iaşi Romania Faculty of Computer Engineering and Automatic Control “Gheorghe Asachi” Technical University Iaşi Romania “Grigore T. Popa” University of Medicine and Pharmacy Iaşi Romania “Stefan cel Mare” University Suceava Romania

In this paper a new pupil detection algorithm based on RANSAC procedure is presented. Unlike other similar algorithms reported in the literature, the proposed algorithm provides higher accuracy, low running time and operates properly in noise conditions and for variable illumination. This algorithm is used in the field of assistive technology in order to communicate with neuromotor disabled people by detecting their gaze direction, and can easily be adapted for other applications, such as PC control using eye-gaze detection.

关键词： Fitting Handheld computers Data models Detection algorithms Real-time systems Cameras Feature extraction

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment

Integration of ANN for Accurate Estimation and Control in Wa...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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DC Link Voltage control of an Induction Generator/PWM Converter System

DC Link Voltage Control of an Induction Generator/PWM Conver...

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2007 International Aegean Conference on Electrical Machines and Power Electronics, vol.2

作者： M. Albu V. Horga M. Ratoi C. Botan Electrical Engineering Faculty Gheorghe AsachiE2809C Technical University Iasi Romania Automatic Control and Computer Science Faculty Gheorghe AsachiE2809C Technical University Iasi Romania

The paper presents the results referring to the dc link voltage control of an induction generator/PWM converter system. This system can be used for the renewable energy sources in isolated areas. The voltage control i... 详细信息

关键词： Voltage control Induction generators Pulse width modulation converters control systems Power systems Wind turbines Analog-digital conversion Static power converters Capacitors Rectifiers

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GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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Retraction Note: EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm

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Neural Computing and Applications 2024年第24期36卷 15215-15215页

作者： Hassan, Mohammed K. Desouky, Ali I. El Badawy, Mahmoud M. Sarhan, Amany M. Elhoseny, Mohamed Gunasekaran, M. Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura City Egypt Department of Computer Engineering and Automatic Control Faculty of Engineering Tanta University Tanta Egypt Faculty of Computers and Information Mansoura University Mansoura Egypt University of California Davis USA

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Type of Material in the Pipes Overhead Power Lines Impact on the Distribution on the Size of the Overhang and the Tension

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IOP Conference Series: Materials Science and engineering 2017年第2期245卷

作者： Urszula Pawlak Marcin Pawlak Department of Mechanics Metal Structures and Computer Methods Faculty of Civil and Environmental Engineering Kielce University of Technology Kielce Poland Department of Power Engineering Electronics Faculty of Electrical Engineering Automatic Control and Computer Kielce University of Technology Kielce Poland

The article presents the material type from which the conductors of the overhead power lines are produced influences on the size of the overhang and the tension. The aim of the calculations was to present the benefits of the mechanics of the cable resulting from the type of cable used. The analysis was performed for two types of cables: aluminium with steel core and aluminium with composite core, twice span power line section. 10 different conductor-to-strand coil, wind, icing, and temperature variations were included in the calculations. The string description was made by means of a chain curve, while the horizontal component H of the tension force was determined using the bisection method. The loads were collected in accordance with applicable Eurocode.

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Multi-method analysis of emotional and affective states

Multi-method analysis of emotional and affective states

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Progress in Applied Electrical engineering (PAEE)

作者： Adrian Luckiewicz Bogdan Ruszczak Mariusz Pelc Dariusz Mikołajewski Adam Sudoł Barbara Grochowicz Edward J. Gorzelanczyk Patryk Mendon Aleksandra Kawala-Sterniu Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

关键词： Electrical engineering Brain Data processing Hemodynamics Functional near-infrared spectroscopy

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

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Progress in Applied Electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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GPI-based secrecy rate maximization beamforming scheme for wireless transmission with an-aided directional modulation

arXiv

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arXiv 2017年

作者： Yu, Hai Wan, Simin Cai, Wenlong Xu, Ling Zhou, Xiaobo Wang, Jin Wu, Yongpeng Shu, Feng Wang, Jiangzhou Wang, Jianxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China faculty of National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Shanghai Key Laboratory of Navigation and Location-Based Services Shanghai Jiao Tong University Minhang200240 China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases. Copyright © 2017, The Authors. All rights reserved.

关键词： Modulation

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