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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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Zero-Shot Learning for Intelligent Fault Detection 27

Zero-Shot Learning for Intelligent Fault Detection

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27th International Conference on Automation and Computing, ICAC 2022

作者： Zhang, Siyu Wei, Hua-Liang University of Sheffield Dept. Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Signal-based fault detection, as an essential technology in many engineering and industrial applications, has received extensive attention. Nevertheless, in a real-world application setting, collecting samples for some specific types of faults may be time-consuming and damaging, implying that samples of unseen faults may be unavailable in the training step. Keeping this in mind, we present a new semantic space-based zero-shot learning (SSB-ZSL) method for fault detection. The implementation of the method comprises three phases: feature extraction, human-defined semantic space, and a feature embedding model to address this issue. To evaluate the proposed method, we carry out experiments on two real datasets: a vibration-based rolling element bearing dataset from Case Western Reserve University, and an acoustic-based air compressor fault dataset. The results are promising and significantly better than that given by the compared state-of-the-art methods, showing that the proposed method can effectively detect unseen types of faults in the absence of their samples. © 2022 IEEE.

关键词： Fault detection

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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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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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Current-limiting control of interlinking converter to maintain power balance in hybrid microgrids

Current-limiting control of interlinking converter to mainta...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skentzos, Panagiotis Konstantopoulos, George C. The University of Sheffield Dept. of Automatic Control and Systems Engineering Sheffield United Kingdom University of Patras Dept. of Electrical and Computer Engineering Patra Greece

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, a novel nonlinear current-limiting controller that maintains the desired power balance in a hybrid microgrid, is proposed for interlinking converters (ICs). The RMS value of the IC current is analytically proven to remain below a desired maximum value at all times using nonlinear ultimate boundedness theory, without limiting separately the d- and q-axis current components and without using adaptive saturation units, as done in conventional approaches. Furthermore, the stability of the closed loop system is investigated through root locus analysis for different values of the controller gain. Finally, the performance of the proposed control scheme is validated through detailed simulation results of an interlinking converter connected to both an AC and a DC subgrid. © 2022 IEEE.

关键词： controllers

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Wei, Hua-Liang Dept. Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Energy Institute The University of Sheffield Sheffield United Kingdom Centre of Machine Intelligence The University of Sheffield Sheffield United Kingdom Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9798350322972

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting. © 2023 IEEE.

关键词： Electric power utilization

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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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Tuning the Feedback controller Gains is a Simple Way to Improve Autonomous Driving Performance

Tuning the Feedback Controller Gains is a Simple Way to Impr...

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UKACC International Conference on control (control)

作者： Wenyu Liang Pablo R. Baldivieso Ross Drummond Donghwan Shin Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Dept. of Computer Science University of Sheffield Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness.

关键词： Machine learning algorithms Neural networks control systems Robustness Feedback control control theory Adaptive control

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An Adaptive CUSUM Approach for Automating Sleep Apnoea Analysis Based on Pulse and Oximetry Data 20

An Adaptive CUSUM Approach for Automating Sleep Apnoea Analy...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Dongjin Bhargava, Eishaan Elphick, Heather Mihaylova, Lyudmila S. University of Sheffield Dept. of Automatic Control and Systems Engineering United Kingdom Sheffield Children's Hospital Department of Paediatric Otolaryngology Sheffield United Kingdom Sheffield Children's Hospital Department of Paediatric Respiratory and Sleep Medicine Sheffield United Kingdom

ISBN: (纸本)9798350320831

Sleep apnoea is a common sleep disorder during human sleep. It is usually diagnosed by a doctor after recording one nights' sleep signals. Patients have to go to the hospital to record sleep signals, which is time-consuming and resource-intensive. The study focused on two signals, pulse data and oximetry data, with the aim of detecting apnoea using a single signal. This paper introduced an anomaly detection approach using the adaptive cumulative sum (ACUSUM) change point detection algorithm to monitor outliers in the signal. In addition, the test results of ACUSUM will be compared with the test results of classical CUSUM. Besides, the threshold selection has been changed from an unchanging constant to a value related to the standard deviation of the selected signal based on a rational subgroup process. The results of the comparison confirm that ACUSUM is better than classical CUSUM in the accuracy of automatic detection. © 2023 IEEE.

关键词： Anomaly detection

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