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An Information Quantity-Based Uncertainty Measure to Incomplete Numerical Systems 1

9th International Conference on Fuzzy Information and Engineering, ICFIE 2017

作者： Guo, Xin Xiang, Yingzhuo Shu, Lan School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

ISBN: (数字)9783030027773

ISBN: (纸本)9783030027766

The uncertainty measure plays an important role in the analysis of data. At present, many uncertain measures for incomplete information systems or incomplete decision systems have been developed. However, these measures are mainly aimed at discrete valued information systems, but they are not suitable for real valued data sets. In this paper, we mainly study the uncertainty measurement method of incomplete numerical information systems. By introducing neighborhood tolerant rough sets model, each concept has a neighborhood tolerant subset called neighborhood tolerance granule. Neighborhood tolerance information quantity uncertainty measure is proposed. We then prove that it satisfies non-negativity and monotonicity, giving maximum and minimum values. On this basis, the concept of neighborhood-tolerance joint quantity and neighborhood-tolerance condition quantity is proposed, and the relation between them and neighborhood tolerance information is discussed. Theoretical analysis and experimental results show that, in incomplete numerical information systems, the uncertainty measures we propose are performed better than the neighborhood-tolerance approximation accuracy measure in some case. © 2019, Springer Nature Switzerland AG.

关键词： Information systems

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Fault tolerant control with delays systems: Application to a PEMFC system

Fault tolerant control with delays systems: Application to a...

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International Conference on Systems and Control (ICSC)

作者： Z. Bougatef H.P. Wang A. Aitouche M.N. Abdelkrim Sino-French International Joint Laboratory of Automatic Control and Signal Processing Nanjing University of Technology and Science Nanjing China National Engineering School of Gabs ENIG University of Gabs Tunisia Signal and Automatic Control (CRISTAL) French School of High Studies in Engineering 13 rue de Toul BP 41290 Lille Cedex France

ISBN: (纸本)9781665407830

The problem of active Fault Tolerant Control (FTC) for a Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems with time delays subject to an actuator fault, is investigated in this paper. This new approach consists on constructing an adaptive Unknown Input Observer (UIO) using linear parameter time varying model firstly and to design a control law secondly. Based on linear matrix inequality (LMI) techniques, the FTC linear parameter-varying (LPV) control scheme is developed for the LPV system. The stability of the proposed FTC via closed-loop system is verified by Lyapunov approach. Finally, the effectiveness of the proposed method is illustrated via simulation results.

关键词： Actuators Adaptation models Delay effects Simulation Fuel cells Estimation Fault tolerant control

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Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

Iterated Problem Reformulation for Evolutionary Large-Scale ...

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Congress on Evolutionary Computation

作者： Cheng He Ran Cheng Ye Tian Xingyi Zhang Department of Computer Science and Engineering University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.

关键词： Sociology Convergence Pareto optimization Indexes Computer science

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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

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Polyphonic Sound Event Detection Based on CapsNet-RNN and Post processing Optimization

Polyphonic Sound Event Detection Based on CapsNet-RNN and Po...

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International Conference on Information science and Control Engineering (ICISCE)

作者： Liujun zhang Liyan Luo Mei Wang Xiyu Song Shuting Guo Wenshan Chu Jingwen Tan Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi Provincial Ministry of Education Key Laboratory of Cognitive Radio and Signal Processing Guilin University of Electronic Technology Guilin Guangxi College of Information Science and Engineering Guilin University of Technology Guilin Guangxi

Sound event detection (SED) aims to detect the onset and offset times of sound events and gives a label to each event. In real-world, polyphonic SED is a challenging task because multiple sound events may occur at the same time. Recently, deep learning has offered valuable techniques for it, such as convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper, we design a new model that a combination of the capsule neural networks (CapsNet) and RNN, for the detection layers, we use various temporal segmentation methods and optimization algorithms to obtain the best performing threshold. CapsNet overcomes some limitations of CNN, such as the loss of position information after max-pooling. The RNN mainly models the temporal dependency of context information. The combination of CapsNet and RNN greatly enhances the relationships of part and whole, and improves the experiment performance in polyphonic SED. Experiments on the TUT-Sound Events 2017 show that the proposed approach improves ER by 21% and F1-score by 39.6% absolute compared with the state-of-the-art algorithms, respectively.

关键词： Information science Recurrent neural networks Event detection Convolution Feature extraction Routing Convolutional neural networks

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Corrigendum to “Dolphin vocal sound generation via deep WaveGAN” [J. Electron. Sci. Technol. 20 (3) (2022), 100171]

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Journal of Electronic science and technology 2025年第1期23卷

作者： Lue Zhang Hai-Ning Huang Li Yin Bao-Qi Li Di Wu Hao-Ran Liu Xi-Feng Li Yong-Le Xie Institute of Acoustics Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing 100190 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu 611731 Sichuan China

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Research on Anti-jamming technology of Missile-borne SAR in Complex Electromagnetic Environment 6

Research on Anti-jamming Technology of Missile-borne SAR in ...

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Zhen, Shang Baixiao, Chen 38th Research Institute of China Electronics Technology Group Corporation Hefei230088 China National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

ISBN: (纸本)9781728129129

Ballistic missile and anti-ship missile gradually equip Synthetic Aperture radar (SAR) precise guidance seeker to improve the accurate attack effect, when facing with an increasingly complex electromagnetic environment. radar as military application, the missile-borne SAR electromagnetic against should not be neglected. Missile-borne SAR seeker mainly faces active and passive jamming from the enemy. This paper summarizes various common types of electromagnetic jamming and briefly analyses its jamming mechanism. Finally, from the point of view of radar seeker system design and combat concept, various feasible anti-jamming means are discussed, which can provide reference for anti-jamming design and application of radar seeker. © 2019 IEEE.

关键词： Jamming

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Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework

arXiv

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arXiv 2020年

作者： Pham, Lam Phan, Huy Nguyen, Truc Palaniappan, Ramaswamy Mertins, Alfred McLoughlin, Ian University of Kent School of Computing Medway Kent United Kingdom Signal Processing and Speech Communication Lab Graz University of Technology Austria Institute for Signal Processing University of Lubeck Germany National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China

This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high level features and their combination (via a trained feature combiner) are then fed into different decoder models comprising random forest regression, DNNs and a mixture of experts, for back-end classification. We report extensive experiments to evaluate the accuracy of this framework for various ASC datasets, including LITIS Rouen and IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Tasks 1A & 1B and 2019 Tasks 1A & 1B. The experimental results highlight two main contributions;the first is an effective method for high-level feature extraction from multi-spectrogram input via the novel C-DNN architecture encoder network, and the second is the proposed decoder which enables the framework to achieve competitive results on various datasets. The fact that a single framework is highly competitive for several different challenges is an indicator of its robustness for performing general ASC tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Environmental Sound Recognition Based on Double-input Convolutional Neural Network Model

Environmental Sound Recognition Based on Double-input Convol...

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Mei Wang Man Yao Liyan Luo Xin Liu Xiyu Song Wenshan Chu Shuting Guo Lu Bai Provincial Ministry of Education Key Laboratory of Cognitive Radio and Signal Processing Guilin University of Electronic Technology Guilin Guangxi College of Information Science and Engineering Guilin University of Technology Guilin Guangxi Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi

ISBN: (数字)9781728199481

ISBN: (纸本)9781728199498

Most environmental sound recognition models use acoustic features like log-mel spectrogram (Logmel) or mel frequency cepstral coefficient (MFCC) as an input training network model in recent years, but the result of recognition is unsatisfactory. These acoustic features were originally designed for speech recognition and music recognition, which may not represent environmental sound comprehensively. In this paper, we designed a double-input convolutional neural network model, adopting Logmel features and raw waveforms as inputs and extracting the respective features separately using a convolutional neural network, after that performing feature combination and recognition. The network model performs feature extraction directly on raw waveforms and can extract some specific information that may not be contained in these acoustic features, which is complementary to Logmel features and improves the feature's ability to represent environmental sound. The experiments over the GoogleAudioSet dataset showed that the proposed network model achieves a recognition result of 95.1%, which is better than the network model using a single feature and combining multiple acoustic features as input.

关键词： Training Speech recognition Feature extraction Safety Convolutional neural networks Mel frequency cepstral coefficient Spectrogram

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Design of a New Sensor Integrating Scatterometer and SAR to Observe Sea Surface Wind Field

Design of a New Sensor Integrating Scatterometer and SAR to ...

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IEEE International Conference on radar

作者： Hang Li WenKang Liu GuangCai Sun MengDao Xing ZhenHua Zhang Jie Zhang The National Laboratory of Radar Signal Processing Xidian University Xi'an China Academy of Advanced Interdisciplinary Research Xidian University Xi'an China Ministry of Natural Resources Beijing Research Institute of Telemetry Ocean Telemetry Technology Innovation Center Beijing China Ministry of Natural Resources First Institute of Oceanography Ministry of Natural Resources Ocean Telemetry Technology Innovation Center Qingdao China

ISBN: (纸本)9781665468893

The sea surface wind vectors in different sea areas such as the general waters and offshore waters, change in different spatial scales and requires different resolutions. Due to different retrieving mechanisms, the scatterometer is suitable for observing wind fields with low resolution in general waters, while the SAR can be applied to obtain wind fields with higher resolution in offshore waters. In order to retrieve wind fields in all kinds of sea areas, this paper proposes a new sensor integrating scatterometer and SAR, which adopts a single digital array antenna, but functions as a SAR and a scatterometer simultaneously through multiple beamforming Besides, this paper designs the operating mode, together with the joint time sequence of the transceiver for two modes to work simultaneously. This proposed concept can benefit the system development in simpler architecture, lighter weight, and lower cost. Moreover, the multiple observation modes working synchronously can improve the retrieving accuracy, and may give birth to some new applications.

关键词： Sea surface Costs radar measurements Spaceborne radar radar scattering Transceivers Microwave antenna arrays

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