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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Jiang, Lin Hu, Ruimin Wang, Xiaochen Zhang, Maosheng Wuhan Univ Comp Sch Natl Engn Res Ctr Multimedia Software Wuhan 430072 Peoples R China Wuhan Univ Res Inst Shenzhen Peoples R China E China Inst Technol Software Sch Nanchang Peoples R China

ISBN: (纸本)9783319240756;9783319240749

Modern audio coding technologies apply methods of bandwidth extension (BWE) to efficiently represent audio data at low bitrates. An established method is the well-known spectral band replication (SBR) that can provide the very high sound quality with imperceptible artifact. However, its bitrates and complexity are very high. Another great method is LPC-based BWE, which is part of 3GPP AMR-WB+ codec. Although its bitrates and complexity are reduced distinctly, the sound quality it provided is unsatisfactory for music. In this paper, a novel bandwidth extension method is proposed which provided the high sound quality close to eSBR, with only 0.8 kbps bitrates. The proposed method predicts the fine structure of high frequency band from low frequency band by a deep auto-encoder, and only extracts the envelope of high frequency as side information. The performance evaluation demonstrates the advantage of the proposed method compared to the state of the art. Compared with eSBR, the bitrates drop about 63 %, and the subjective listening quality is close to it. Compared with LPC-based BWE, the subjective listening quality is better than it with the same bitrates.

关键词： Bandwidth extension Audio compression deep auto-encoder

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Low-dimensional representation of spectral envelope using deep auto-encoder for speech synthesis 2018

Low-dimensional representation of spectral envelope using de...

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2nd International Conference on Mechatronics Systems and Control Engineering (ICMSCE)

作者： Choi, Heejin Kim, Jaeseok Park, Jinuk Kim, Juntae Hahn, Minsoo Korea Adv Inst Sci & Technol 291 Daehak Ro Daejeon South Korea

ISBN: (纸本)9781450363792

This paper proposes a deep auto-encoder structure to extract robust spectral features for statistical parametric speech synthesis. The technique allows us to compress the low-dimensional features from high dimensional spectral envelope without degradation for full-band speech in a data-driven way. We carried out a subjective evaluation and found that the optimum auto-encoder architecture. Experimental results showed that an analysis-by-synthesis using the proposed auto-encoder has lower reconstruction error of spectral envelope than conventional mel-cepstral analysis in narrow-band as well as full-band. Our results confirm that the proposed method increases the quality of synthesized speech in text-to-speech experiments.

关键词： Statistical parametric speech synthesis deep auto-encoder Spectral envelope Vocoder

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deep auto-encoder and deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

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SENSORS 2021年第24期21卷 8373-8373页

作者： Yu, Hui Chen, Chuang Lu, Ningyun Wang, Cunsong 38 Res Inst CETC Integrated Syst Integrat Dept Hefei 230088 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 210009 Peoples R China

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA's open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

关键词： failure prognosis maintenance decision-making deep auto-encoder deep forest maintenance cost

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Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096007-096007页

作者： Chang, Xiaoxin Yang, Shaopu Li, Shaohua Gu, Xiaohui Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China

Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

关键词： bearing fault diagnosis multi-objective optimization deep auto-encoder transfer diagnosis

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A hybrid of Random Forest and deep auto-encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction

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JOURNAL OF HYDROLOGY 2021年 597卷 125717-125717页

作者： Abbasi, Mahdi Farokhnia, Ashkan Bahreinimotlagh, Masoud Roozbahani, Reza Univ Tehran Dept Irrigat & Reclamat Engn Karaj Iran Water Res Inst Tehran Iran

Streamflow forecasting is an important component of water resources planning and management. Data-Driven Models (DDMs) are common approaches for streamflow prediction. DDMs try to obtain a mathematical relation between variables without any prior knowledge or assumption about the physical relation between them. Hence, these models are effective for complex process modeling. In this study, we use the Support Vector Regression (SVR) and Multiple Linear Regression (MLR) models for long-term streamflow prediction. Moreover, three approaches, Random Forest (RF), deep auto-encoder (DAE), and Principle Component Analysis method (PCA) are employed as pre-processing methods and they are combined to SVR and MLR (six hybrid models produced) in order to improve the accuracy of prediction and reduce its uncertainty. Different evaluation criteria, including R-2, RMSE, NSE, P-factor, and R-factor are computed to evaluate the efficiency of the hybrid models. The results of our study on Bookan dam indicate that the DAE-SVR model, in comparison with the PCA-MLR, RF-MLR, DAE-MLR, PCA-SVR hybrid models, provides a significant improvement in the runoff prediction accuracy and the reduction of its uncertainty. DAE-SVR and RF-SVR have the highest values in NSE (0.93 and 0.87, respectively), and the lowest values in RMSE (33.85 and 43.68 MCM, respectively) for the first forecast horizon. The uncertainty analysis of the streamflow prediction shows that DAE-SVR has the best performance at the 95% confidence level due to its P-factor and R-factor values (0.9 and 0.64, respectively). Furthermore, the DAE-SVR performance is likewise better than other hybrid models in the two, three, and four months forecast horizons.

关键词： Long-term streamflow prediction Dimension reduction approach Variable selection approach Random Forest deep auto-encoder Support vector regression

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A novel deep auto-encoder considering energy and label constraints for categorization

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 176卷 114936-114936页

作者： Song, Wei Zhang, Yuxuan Park, Soon Cheol Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Jeonbuk Natl Univ Dept Comp Sci Jeonju 561756 Jeonbuk South Korea

deep auto-encoder (DAE) is one of the representative deep learning algorithms for feature extraction. However, it often shows relatively poor generalization performance to express data without considering the probability distribution of data. Additionally, it cannot be directly applied to classification, because label information is ignored in DAE to judge the given categories. To tackle these issues, in this paper, we propose an energy and label constrained DAE (ELDAE) by integrating energy and label constraints to improve the feature extraction ability of network for classification. Specifically, as the probability distribution for fitting data can be reflected by energy of a network, the energy constraint is designed in this study to improve the probability of ELDAE for fitting data, and make a better expression to data. Moreover, the label constraint is integrated in ELDAE using label information to describe categorization rule, contributing to enhancing the accuracy of classification. We first give the complexity analysis of ELDAE, which is crucial to the property of speed. To exhibit the performance of the proposed ELDAE, we perform comprehensive experiments on benchmark USPS and MNIST datasets, and parameter sensitivity analysis is then provided to investigate the effects of three key parameters including the balance coefficients of weight decay, energy constraint and label constraint. In addition, we compare ELDAE with six state-of-the-art algorithms including auto-encoder (AE), Sparse AE (SAE), deep AE (DAE), deep Belief Network (DBN), Noisy AE (NAE) and Semi-supervised AE (SSAE). The comparative experimental results demonstrate that ELDAE performs better than the other six competitors in terms of classification accuracy, and keeps the same order of magnitude in terms of training time and testing time.

关键词： deep learning deep auto-encoder Semi-supervised learning Energy constraint Categorization

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A Big Network Traffic Data Fusion Approach Based on Fisher and deep auto-encoder

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INFORMATION 2016年第2期7卷 20页

作者： Tao, Xiaoling Kong, Deyan Wei, Yi Wang, Yong Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab Cloud Comp & Complex Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE) is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN), and support vector machine (SVM)) by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.

关键词： big network traffic data data fusion Fisher deep auto-encoder

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Spatial Fuzzy Clustering and deep auto-encoder for Unsupervised Change Detection in Synthetic Aperture Radar Images

Spatial Fuzzy Clustering and Deep Auto-encoder for Unsupervi...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yangyang Li Linhao Zhou Cheng Peng Licheng Jiao Ministry of Education Xidian University Xi'an Shaanxi Province China

Change detection in synthetic aperture radar (SAR) images is to detect the changes happening during a period of time in the same area, which has important application research value. In this paper, we propose a novel method based on spatial fuzzy clustering (SFCM) and deep auto-encoder for the change-detection of SAR. In this method, the difference image (DI) is generated by the log-ratio operator. Then, the spatial fuzzy clustering (SFCM) algorithm is used to analyze the DI. The spatial fuzzy clustering (SFCM) algorithm adds spatial information to the fuzzy cluster, which effectively reduces the influence of speckle noise. Finally, we choose appropriate samples to train the deep auto-encoder. Real data and theoretical analysis show the effectiveness and robustness of the proposed method.

关键词： Change detection Spatial fuzzy clustering deep auto-encoder Synthetic aperture radar (SAR)

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Dimensionality reduction of rolling bearing fault data based on graph-embedded semi-supervised deep auto-encoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Wei, Kongyuan Zhao, Rongzhen Kou, Haixia Chen, Pengfei Cao, Yongyong Zheng, Yuqiao Deng, Linfeng Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Peoples R China Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 730070 Peoples R China

Conventional shallow models struggle to extract effective features from high-dimensional and sparse bearing fault data, which negatively impacts fault classification accuracy. To address this issue, we propose a dimensionality reduction method for rolling bearing fault datasets based on a Graph Embedding Semi-supervised deep auto-encoder (GESDAE). This method constructs traditional fault datasets into a data network graph embedding structure and inputs them into a sparse dropout regularized semi-supervised deep auto-encoder for dimensionality reduction. The supervised component utilizes first-order proximity to maintain the local network graph embedding structure, while the unsupervised component employs second-order proximity to capture the global network graph embedding structure. By reasonably introducing a sparse dropout regularization term in the original objective function, the method helps prevent model overfitting and enhances the robustness and generalization ability of GESDAE. The effectiveness of the proposed method is validated using vibration signals from rolling bearing systems on two different experimental platforms. Results show that the proposed method achieves a correct recognition rate of 97.3 % for rolling bearing faults, with a 4.8 % improvement compared to state-of-the-art dimensionality reduction methods. Furthermore, visualization and convergence analysis demonstrate that the proposed method not only retains highly nonlinear local and global network graph embedding structures but also significantly enhances the separability of fault features, showcasing its superior robustness and generalization performance in fault diagnosis systems.

关键词： Graph embedding Semi-supervised deep auto-encoder Sparse dropout regularization Fault diagnosis

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A transferred spatio-temporal deep model based on multi-LSTM auto-encoder for air pollution time series missing value imputation

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 156卷 325-338页

作者： Zhang, Xiaoxia Zhou, Pengcheng Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China

Air pollution is one of the most severe problems facing the world. Research on air quality prediction and analysis of influencing factors also continues to grow. When conducting this research, valid, authentic, and high -quality air pollution data are necessary to obtain reasonable results. However, Missing values are unavoidable in multivariate time series due to multiple causes, such as sensor and communication failure. Most previous algorithms on missing data cannot effectively pay attention to air pollution's temporal and spatial mechanism, handle multiple missing patterns, or deal with high missing rates sequences. This paper proposes a new deep spatiotemporal imputation methodology to address this problem effectively, namely transferred Multiple LSTM based deep auto -encoder (TMLSTM-AE). Our idea is intuitive: train an auto -encoder to estimate the missing values. It uses spatial and time series information to fill in single missing, multiple missing, block missing, and long -interval consecutive missing in air quality data. To verify the effectiveness and priority of the proposed model, we conducted a case study in a city in Shaanxi, China. Long -interval consecutive missing and different missing patterns PM 25 data are filled. The results indicate that the model proposed in this paper performs well and outperforms existing models for different missing patterns and long -interval consecutive missing.

关键词： Air quality deep learning Missing value imputation Long short-term memory (LSTM) deep auto-encoder Neural network Transfer learning

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