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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2024年第3期86卷 67-80页

作者： Colbu, Ștefania-Cristiana Băncilă, Daniel-Marian Petrescuniță, Alina Popescu, Dumitru Olteanu, Severus-Constantin Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Mathematical Methods and Models National University of Science and Technology POLITEHNICA Bucharest Romania

The present paper introduces a comparative analysis of the management of a stochastic optimization problem using both a risk deterministic approach and a support vector machine strategy. This optimization problem is formulated in consideration of the influence produced by environmental factors, such as irradiance and temperature, on a photovoltaic panel. The problem will be solved by applying both methods to determine the Maximum Power Point (MPP), thereby to enhance the power generation efficiency. The primary objective is to conduct an analysis comparing the two approaches, with the aim of recommending the most suitable approach for Maximum Power Point Tracking (MPPT). © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： machine learning photovoltaics risk optimization support vector machine for regression

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AN INTEGRATED BREAST CANCER MICROARRAY ANALYSIS APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2022年第2期84卷 79-90页

作者： Lixandru-Petre, Irina-Oana Buiu, Catalin Dept. of Automatic Control and Systems Engineering University POLITEHNICA of Bucharest Romania

As knowledge of the biological and biophysical basis of cellular function has increased, opportunities to understand the cellular and molecular functioning of organic matter have expanded, and gene expression microarrays have become a widely used technique to study the dynamics of biological processes. In this paper, we present a gene expression analysis approach using R programming, starting from mRNA oligonucleotide matrices ending with interrelated genes, analyzed within a chosen biological process. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Microarrays

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The Transient Predictor 63

The Transient Predictor

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Moffat, Keith Dorfler, Florian Chiuso, Alessandro Automatic Control Laboratory Zürich Switzerland University of Padova Dept. of Information Engineering Italy

ISBN: (纸本)9798350316339

This paper introduces the Transient Predictor and describes how it can be used to estimate the Multistep Predictor, which can be applied to applications such as Data-Driven Predictive control (DDPC). The Transient Predictor has two desirable traits that differentiate it from other methods for estimating the Multistep Predictor, such as the standard Subspace Predictor method: 1) Causality-the Transient Predictor asserts a causal relationship between future inputs and future outputs;and 2) Bias-the Transient Predictor is a consistent predictor of future outputs. This paper provides an easy-toimplement algorithm for estimating the Transient Predictor and in turn the Multistep Predictor, and demonstrates its efficacy for DDPC. In experiments, we find that the Transient Predictorbased DDPC performs remarkably well with small lead-in data lengths, indicating that it is well-suited for tasks in which large amounts of data are not available. In addition, the Transient Predictor is not afflicted by the same bias as subspace-based methods when data is gathered in closed loop. © 2024 IEEE.

关键词： Electromagnetic transients

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BIOPHYSICAL SIGNAL PROCESSING FOR automatic ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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Assessing Uncertainty in Space Weather Forecasting Using Quantile Regression and Complex Nonlinear Systems Identification Techniques 6

Assessing Uncertainty in Space Weather Forecasting Using Qua...

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6th International Conference on computing, Electronics and Communications engineering, iCCECE 2023

作者： Wei, Hua-Liang Balikhin, Michael A. Boynton, Richard J. Walker, Simon N. The University of Sheffield Dept. of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350340921

Space weather forecasting is of global interest, and its importance is well established in research community and recognized by government, industries and stockholders. Over the past years, many types of predictive models have been developed in the literature. There is a general agreement that forecasting models should not only provide point prediction but also inform the uncertainty associated with the prediction. This study presents a novel method bases on quantile regression and complex dynamic modelling for measuring uncertainties in space weather forecasting. The approach is implemented using Quantile regression and Nonlinear AutoRegressive Moving Average with Exogenous inputs (NARMAX) methods (for short the approach is called Q-NARMAX). The method is applied to Disturbance storm index (Dst) observations to examine its interpretability and capability for uncertainty analysis. Results show that the proposed Q-NARX model can produce excellent predictions of the Dst index, and meanwhile provides a measure for assessing the uncertainty in the forecast. The innovative integration of quantile regression, complex dynamic modelling and nonlinear system identification techniques enables the proposed work to have following attractive advantages and properties: 1) it can produce excellent prediction accuracy for space weather forecasting, 2) it uses transparent models to approximate (represent) black-box systems, enabling to interpret the dependent relationship between space weather indices (system outputs) and their drivers (system inputs), and 3) more importantly, it allows for uncertainty assessment and analysis of models and forecasts. © 2023 IEEE.

关键词： Uncertainty analysis

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks 50

Detection of Electric Motor Damage Through Analysis of Sound...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bauer, Waldemar Zagórowska, Marta Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9781665464543

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided. © 2024 IEEE.

关键词： Damage detection

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Design of a Digital Phase Lead-and-Lag Voltage control based on Frequency Response Approach for Single-Phase Half-Bridge DC-AC Converters 15

Design of a Digital Phase Lead-and-Lag Voltage Control based...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： Buzzi, Henrique Eissmann Busarello, Tiago Davi Curi Péres, Adriano Dept. of Control Automation and Computing Engineering Federal University of Santa Catarina Blumenau Brazil

ISBN: (纸本)9798350314182

Grid-forming inverters is getting special attention of industries and academia due to their important role in the realization of microgrids. A crucial feature of them is to have an accurate voltage control. This paper proposes a digital phase lead-and-lag voltage control based on frequency response approach for single-phase half-bridge DC-AC converters. Initially, the system is modeled through the small-signal technique. Then, the controller is designed based on the frequency response approach. The discretization through Tustin and Backward methods are presented and compared. Moreover, designing the controller in discrete-time domain by conventional methods using the fictitious w-domain is also presented. The system is tested in a controller Hardware-in-Loop setup, where the power topology runs in the HIL402 device and the digital voltage controller runs in the TMS320FF28335 digital signal controllers. Scenarios like initialization, nonlinear load and oscillation at the DC voltage are performed. Results show the efficacy of the methodology proposed in this paper. ©2023 IEEE.

关键词： discretization Half-Bridge converter hardware-in-loop voltage control

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Circuit Design Method of Rate Increase in Operational Amplifiers Based on the MH2XA031 Array Chip and CBJT Technological Routes

Circuit Design Method of Rate Increase in Operational Amplif...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Titov, Alexey E. Kleimenkin, Dmitriy V. Prokopenko, Nikolay N. Southern Federal University Dept. of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Dept. of Scientific Research Dept. of Information Systems and Radio Engineering Rostov-on-Don Russia

ISBN: (纸本)9798350360646

It has been revealed that in the conditions of small-scale production of discrete-analog filters on switched capacitors and ADC-drivers, it is most advisable to design high-speed operational amplifiers (OAs) based on the CBJT technological route and the MH2XA031 array chip, which allows operation in conditions of low temperatures and exposure to radiation. It has been established that most commercially produced O A s provide an average value of the slew rate (SR, up to 200 div 300 ~V / μ s). This is largely due to the limitations of the technologies used, and most importantly, to the irrational design of circuits. We study the maximum performance parameters of a CBJT OA with one integrating capacitor, which ensures the stability of the O A, and a differentiating transient correction circuit, which is implemented by connecting additional small capacitors to the original circuit. In this case, the S R of the O A increases by more than 500 times (up to 4000 ~V / μ s). © 2024 IEEE.

关键词： array chip CBJT operational amplifier slew rate

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Constrained Predictive Current control in dq Frame for a Permanent Magnet Synchronous Machine 28

Constrained Predictive Current Control in dq Frame for a Per...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Costin, Madalin Lazar, Corneliu Gheorghe Asachi Technical University of Iasi Dept.of Automatic Control and Applied Informatics Iasi Romania Dunarea de Jos University of Galati Dept. of Automatic Control and Electrical Engineering Galati Romania

ISBN: (纸本)9798350339918

In the paper, the design and Simulink implementation of a predictive current control structure in dq frame for a permanent magnet synchronous machine (PMSM) are presented. Starting from the multivariable model of the currents in the dq reference frame, first it is decoupled and through the rejection of the disturbance introduced by the back-EMF, two single-input single-output (SISO) systems result whose dynamics are generated by R-L circuits. For these SISO systems, the model predictive control (MPC) algorithms are designed that allow the consideration of physical limitations through constraints The performances of the current control structure with MPC algorithms are compared with those obtained with the conventional PI controllers. © 2023 IEEE.

关键词： Model predictive control

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WIND FARM SENSOR FAULT DIAGNOSIS USING ARTIFICIAL INTELLIGENCE AND WAVELET TRANSFORM

WIND FARM SENSOR FAULT DIAGNOSIS USING ARTIFICIAL INTELLIGEN...

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International Conference on Green Energy, computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Aly, Mahmoud Shaaba, Mahmoud Kasem, Tamer Shalaby, Raafat Mechatronics Program Nile University Giza Egypt Nile University Giza Egypt Dept. of Engineering Mathematics and Physics Cairo University Giza Egypt Automatic Control Dept. Faculty of Electronic Engineering Menofia University Egypt

ISBN: (纸本)9781837242689

A methodology for the diagnosis of sensor faults in wind farms is introduced. Simulations are performed using a multi-domain model that integrates mechanical, electromagnetic, electronic control, and power grid components. The prevalent issue of intermittent sensor failures is addressed. The discrete wavelet transform is employed for feature extraction. The transient signals are processed effectively. Various wavelet families are evaluated to identify optimal candidates for fault diagnosis. Additionally, the methodology integrates three machine learning classifiers, achieving accuracy rates consistently exceeding 99%. The outcomes demonstrate the advantages of this approach in terms of enhanced accuracy. This research represents a significant advancement in sensor fault diagnosis, contributing valuable insights for the operation and maintenance of renewable energy systems. © The Institution of engineering & Technology 2024.

关键词： Windmill

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