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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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A COMPARATIVE STUDY OF SUPPORT VECTOR MACHINE AND RISK DETERMINISTIC APPROACH ON A PHOTOVOLTAIC 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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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

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4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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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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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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NEW TOOL FOR SYSTEM IDENTIFICATION AND PREDICTIVE control BASED ON NEURAL NETWORKS USING LABVIEW SOFTWARE

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 91-110页

作者： Alshareefi, Hamid Lupu, Ciprian Alboudkhal, Hayder Ismail, Laith Dept. of Automatic Control and Computers University POLITEHNICA of Bucharest Romania Ministry of Electricity Iraq Dept. of Computer Technology Engineering AL-TURATH University College Baghdad Iraq

In this paper, a new tool for system identification and model predictive control (MPC) has been developed. The mathematical approximation of the model identification was derived using the neural network theory. The emphasis of this paper is to employ a multi-layer recurrent neural network with three layers to match an autoregressive-moving average (ARMA) model in the identification stage to provide the mathematical model for the model predictive controller in the control stage. The tool was designed using LabVIEW software and the MathScript facility. The tool can be used to identify and control simulated or real systems using the stored input/output data of the system. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Recurrent neural networks

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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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Radiomics-based classification in imbalanced datasets: Complexity or interpretability

Radiomics-based classification in imbalanced datasets: Compl...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Boesso, Simone Farina, Lorenzo Petti, Manuela Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Radiomics represents a specialized branch of medical imaging where quantitative features are extracted from images. Performing a classification using radiomics means solving two common problems: the imbalanced setting, and the large number of features that would increase the risk of overfitting. Moreover, since its main application and impact are in clinical field, there is the need of interpretable models for explaining their results. The aim of this study is to compare two modelling approaches: one based on a logistic regression model, known for its simplicity and interpretability, and RUSBoost, an ensemble method designed to handle class imbalance with potentially higher complexity, in order to answer the question whether higher complexity and lower interpretability are justified when dealing with radiomics data. Additionally, due to the large literature suggesting it, we analyze the impact of a feature selection step applied to these two classifiers. Test performances measured across 20 repeated splits on two datasets show how the RUSBoost approach is able to capture more detailed patterns of the data but this is highly dependent on the dataset at hand. © 2024 IEEE.

关键词： Logistic regression

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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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