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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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control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach

Control of Isolated AC Microgrids with Constant Power Loads:...

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Mediterranean Conference on control and Automation (MED)

作者： Grigoris Michos George C. Konstantopoulos Paul A. Trodden Visakan Kadirkamanathan Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street Sheffield UK Department of Electrical and Computer Engineering University of Patras Rion Greece

This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System

Prediction of Radiated Electromagnetic Interference Based on...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yixin Shi Wenjie Chen Fengjiao Li Jinpeng Kan Yongqing Meng Guang Yang School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems.

关键词： Motor drives Accuracy Silicon carbide Electromagnetic interference Layout Predictive models Motors Topology Gallium nitride Cables

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

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

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International Conference on systems and control (ICSC)

作者： Yosra Sayahi Moez Allouche Mariem Ghamgui Sandrine Moreau Driss Mehdi Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Computer Science and Automatic Control for Systems University of Poitiers France

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.

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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Nonlinear Identification for control by Using HARMAX Models

Nonlinear Identification for Control by Using HARMAX Models

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

作者： Janetta Culita Dan Stefanoiu Andreea-Maria Nica Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest Romania Faculty of Automatic Control and Computers University “Politehnica” of Bucharest Romania

The purpose of this article is to perform a comparison between two identification models: a nonlinear one referred to as HARMAX and a classical linear one, of Box-Jenkins (BJ) type. The first model modifies a classical ARMAX model by applying Hammerstein polynomials on input, output, and noise signals. Identification of models was performed by means of Multi-Step Least Squares Method, as described into the article. The simulations on a real-world plant, namely ASTANK2, a fluidic system with two inputs, two outputs and nonlinear static characteristic, have proven that the HARMAX model performs better than the BJ model and provides more accurate useful models to be integrated into closed loop configurations.

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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A Fast and Accessible Neural Network Based Eye-Tracking System for Real-Time Psychometric and HCI Applications 10

A Fast and Accessible Neural Network Based Eye-Tracking Syst...

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10th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2024

作者： Iacobelli, Emanuele Pelella, Davide Ponzi, Valerio Russo, Samuele Napoli, Christian Department of Computer Automatic and Management Engineering Sapienza University of Rome Rome00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Rome00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Eye-tracking technology has long been a valuable tool across various domains, and recent advancements in neural networks have significantly expanded its versatility and potential. However, real-world applications continue to face challenges such as accommodating users’ natural movements, variations in lighting, occlusions of the eyes, and the limited availability of large, open-source datasets for training models. To address these issues, we developed a comprehensive pipeline that produces a lightweight and efficient model, requiring only an RGB camera as external hardware, making it easily deployable on standard PCs. Key input features include facial images, eye regions, head pose angles, the Eye Aspect Ratio (EAR), and a face grid that determines the face’s location within the camera’s frame. The model was trained using a custom dataset, in which participants were instructed to fixate on both randomly positioned points and the standard 9-point grid commonly employed in eye-tracking calibration. The resulting system was integrated into a real-time application, offering fast and accessible gaze tracking, making it well-suited for studies requiring rapid gaze assessments across broad regions of the screen, such as psychometric research and Human-computer Interaction (HCI) tasks. Its design is particularly advantageous for gaze laterality studies, which explore hemispheric dominance and attentional bias in cognitive and emotional processing, key concepts relevant to ADHD and dyslexia. Moreover, the system’s capabilities naturally extend to emotional and decision-making tasks, where broad-area gaze tracking can support the analysis of preference formation and attentional patterns without the need for specialized hardware. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Open systems

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A Hybrid Intelligence Model Forecasts the Temperature of a Battery Used in Electric Vehicles 21st

A Hybrid Intelligence Model Forecasts the Temperature of a...

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21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024

作者： Rubiños, Manuel Arcano-Bea, Paula Díaz-Longueira, Antonio Michelena, Álvaro Vega, Rafael Vega Casteleiro-Roca, José-Luis Andújar, José Manuel CTC Department of Industrial Engineering CITIC University of A Coruña EPEF Calle Mendizábal s/n Campus de Esteiro A Coruña Ferrol15403 Spain Department of Electronic Engineering Computer Systems and Automatic University of Huelva Campus de El Carmen Huelva Huelva21071 Spain

ISBN: (纸本)9783031739095

Battery technology is advancing due to the requirement to store reliable energy in electric transportation systems and portable electronics devices. Assessing battery conditions and increasing the autonomy of electric vehicles requires to improve the behavior models. Prognosticating battery performance, nevertheless, is complex due to multiple variables, such as temperature, wear, and usage guidelines. Understanding battery performance requires a depth knowledge of, for example, time series analysis, above all forecasting. This research uses Artificial Neural Networks (ANN) modeled taking into account system dynamics, to create a Lithium Ion Battery (LIB) prediction model. Testing was performed with a 59.2 VDC, 120 Ah LIB, monitoring voltage, current and temperature while simulating charges and discharges. The model used is a hybrid type, which uses clustering to generate internal local models. This model predict the temperature development with very good results in terms of Mean Squared Error. From this research it is deduced that hybrid models are appropriate tools for prognostic battery performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Prediction models

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