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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion

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arXiv 2021年

作者： Gao, Teng Zhou, Jian Wang, Huabin Tao, Liang Kwan, Hon Keung Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Whispered speech is a special way of pronunciation without using vocal cord vibration. A whispered speech does not contain a fundamental frequency, and its energy is about 20dB lower than that of a normal speech. Converting a whispered speech into a normal speech can improve speech quality and intelligibility. In this paper, a novel attention-guided generative adversarial network model incorporating an autoencoder, a Siamese neural network, and an identity mapping loss function for whisper to normal speech conversion (AGANW2SC) is proposed. The proposed method avoids the challenge of estimating the fundamental frequency of the normal voiced speech converted from a whispered speech. Specifically, the proposed model is more amendable to practical applications because it does not need to align speech features for training. Experimental results demonstrate that the proposed AGANW2SC can obtain improved speech quality and intelligibility compared with dynamic-time-warping-based methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

CycleGAN with dual adversarial loss for bone-conducted speech enhancement

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arXiv 2021年

作者： Pan, Qing Gao, Teng Zhou, Jian Wang, Huabin Tao, Liang Kwan, Hon Keung Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Compared with air-conducted speech, bone-conducted speech has the unique advantage of shielding background noise. Enhancement of bone-conducted speech helps to improve its quality and intelligibility. In this paper, a novel CycleGAN with dual adversarial loss (CycleGAN-DAL) is proposed for bone-conducted speech enhancement. The proposed method uses an adversarial loss and a cycle-consistent loss simultaneously to learn forward and cyclic mapping, in which the adversarial loss is replaced with the classification adversarial loss and the defect adversarial loss to consolidate the forward mapping. Compared with conventional baseline methods, it can learn feature mapping between bone-conducted speech and target speech without additional air-conducted speech assistance. Moreover, the proposed method also avoids the over-smooth problem which is occurred commonly in conventional statistical based models. Experimental results show that the proposed method outperforms baseline methods such as CycleGAN, GMM, and BLSTM. © 2021, CC BY.

关键词： Speech enhancement

Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery

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arXiv 2024年

作者： Sledge, Isaac J. Byrne, Dominic M. King, Jonathan L. Ostertag, Steven H. Woods, Denton L. Prater, James L. Kennedy, Jermaine L. Marston, Timothy M. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Naval Sea Systems Command United States The Applied Sensing and Processing Branch Naval Surface Warfare Center Panama CityFL United States The Littoral Acoustics and Target Physics Branch Naval Surface Warfare Center Panama CityFL United States The Division Head of the Sensing Sciences and Systems Division Naval Surface Warfare Center Panama CityFL United States The Applied Physics Laboratory University of Washington SeattleWA United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL United States The University of Florida United States

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant. © 2024, CC BY.

关键词： Semantic Segmentation

Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs

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Learning across multi-stimulus enhances target recognition m...

作者： Wong, Chi Man Wan, Feng Wang, Boyu Wang, Ze Nan, Wenya Lao, Ka Fai Mak, Peng Un Vai, Mang I. Rosa, Agostinho Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau China Department of Computer Science University of Western Ontario LondonON Canada Department of Psychology Shanghai Normal University Shanghai China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China LaSEEB-ISR-LARSyS Universidade de Lisboa Lisbon Portugal

Objective. Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs. © 2020 IOP Publishing Ltd.

关键词： Brain computer interface

Delay in Cooperative Communications: Achieving Higher Multiplexing Gain in Gaussian Interference Channels with Full-Duplex Transmitters

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Delay in Cooperative Communications: Achieving Higher Multip...

作者： Haghi, Ali Mohammadizadeh, Neda Khandani, Amir K. Department of Electrical and Computer Engineering Coding and Signal Transmission Laboratory University of Waterloo WaterlooN2L 3G1 Canada

Delay, guaranteeing causality, is inevitable in cooperative communication systems. Traditionally, delay granularity has been limited to one symbol;however, channel delay is in fact governed by channel memory and can be shorter. For example, the delay requirement in orthogonal frequency-division multiplexing, captured in the cyclic prefix, is typically much shorter than the symbol itself. This perspective is used to study the two-user Gaussian interference channel with full-duplex transmitters. By superimposing the signal from the other node onto its own signal, each transmitter cancels the interference at its receiver. Among other results, it is proved that under a mild condition, the maximum multiplexing gain of this channel is in fact two, rather than the limit of one, previously shown under the traditional constraint of causal delay. Further, the optimal power allocation among orthogonal sub-carriers, which maximizes the achievable sum-rate, is shown to be a generalization of the well-known water filling. Simulation results are included to demonstrate the improvement in the achievable sum-rate when full-duplex transmitters are used. © 1963-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

Comparative Study of Constant Current Modulations for Wireless Electric Vehicle Battery Charging against Misalignment Effect without Wireless Communication 4

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Comparative Study of Constant Current Modulations for Wirele...

4th IEEE International Future Energy Electronics Conference, IFEEC 2019

作者： Iam, Io-Wa Hoi, Iok-U Huang, Zhicong Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781728131535

Compared with the plug-in electric vehicle (EV) charger, wireless EV charging is convenient and safe due to elimination of physical contact, thus being paid more attention recently. However, misalignment issues are inevitable in wireless EV charging scenarios because of positioning error, leading to unexpected output and low efficiency performance. This paper highlights two receiver-side control schemes, phase shift control and on-off keying modulation, which can achieve precise constant current output without the necessary of wireless communication with the transmitter-side. And the circuit is simplified. The power efficiency for variance of load and output ripple are comprehensively compared with theoretical analysis and simulation results at series-series compensation converter. The proposed two control modulations schemes can obtain stable current output, but phase shift control has precise output. © 2019 IEEE.

关键词： Charging (batteries)

Enhanced Machine Learning Approaches for Diagnosing Building Systems

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Enhanced Machine Learning Approaches for Diagnosing Building...

2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

Annotating motion primitives for simplifying action search in reinforcement learning

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arXiv 2021年

作者： Sledge, Isaac J. Bryner, Darshan W. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Branch Head of the Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL32407 United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States The Computational NeuroEngineering Laboratory University of Florida United States

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives;motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives' motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space. © 2021, CC BY.

关键词： Reinforcement learning