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An Intelligent Speech Multifeature Recognition Method Based on deep Machine Learning: A Smart City Application

JOURNAL OF TESTING AND EVALUATION 2024年第3期52卷 1389-1403页

作者： Song, Ye Yan, Kai Guangdong Vocat & Tech Coll Posts & Telecom Dept Econ Management 191 Zhongshan Rd Guangzhou 510000 Peoples R China Guangdong Vocat & Tech Coll Posts & Telecom Sch Mobile Commun 191 Zhongshan Rd Guangzhou 510000 Peoples R China

Speech recognition has the problem of low recognition accuracy because of poor denoising effect and low endpoint detection accuracy. Therefore, a new intelligent speech multifeature recognition method based on deep machine learning is proposed. In this method, speech signals are digitally processed, a first-order finite impulse response (FIR) high pass digital filter is used to preemphasize digital speech signals, and short-term energy and zero crossing rate are combined to detect speech signals to expand endpoints. The detected speech signal is input into the depth autoencoder, and the features of the speech signal are extracted through deep learning. The Gaussian mixture model of deep machine learning is constructed using a continuous distribution hidden Markov model, and the extracted features are input into the model to complete feature recognition. The experimental results show that the proposed method has high endpoint detection accuracy, good denoising effect, and high recognition accuracy, and this method has higher application value.

关键词： deep machine learning first-order FIR high pass digital filter deep autoencoder endpoint detec-tion Gaussian mixture model speech feature recognition

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Reservoir Inflow Forecast Using a Clustered Random deep Fusion Approach in the Three Gorges Reservoir, China

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JOURNAL OF HYDROLOGIC ENGINEERING 2018年第10期23卷

作者： Bai, Yun Sun, Zhenzhong Zeng, Bo Long, Jianyu Li, Chuan Zhang, Jin Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Tech Univ Dresden Inst Urban Water Management D-01062 Dresden Germany

Reservoir inflow forecast plays a crucial part in programming, development, operation, and management of water resource systems. To better reveal the complex properties of daily reservoir inflow, a clustered deep fusion (CDF) approach is proposed in this paper. First, variational mode decomposition (VMD) is used to decompose the daily reservoir inflow series into multiple modes, which are clustered into different sets by fuzzy c-means according to the Xie-Beni index in view of attribute domain. In each cluster, a deep autoencoder model (DAE) is developed for deep representations of the attributes in the deep domain. DAE outputs are finally fused at the synthesis domain into the forecasting results using random forest (RF). In this way, the inflow time series may be successively observed in the attribute domain, deep domain, and synthesis domain, which results in a clearer understanding of reservoir inflow trend. The present approach is modeled and evaluated using historical data collected from the Three Gorges Reservoir, China. For comparison, two kinds of learning patternsdeep learning (VMD-DAE-RF and DAE) and shallow learning (feed-forward neural network, least-squares support regression, and RF)are applied to the same case. The results indicate that the proposed CDF model outperforms all comparison models in terms of mean absolute percentage error (6.174%), root mean-square error (1,077.428m3/s), and correlation coefficient criteria (0.987). Thus, it is concluded that deep learning in the cluster fusion architecture is more promising.

关键词： Variational mode decomposition Fuzzy c-means deep autoencoder Random forest fusion Reservoir inflow forecast

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Prediction of multiple fatigue crack growth based on modified Paris model with particle filtering framework

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期190卷

作者： Wang, Li Zhang, Chao Tao, Chongcong Ji, Hongli Yang, Yu Qiu, Jinhao Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China Aircraft Strength Res Inst China Struct Damage Monitoring Lab Xian 710065 Peoples R China

This paper presents a novel method based on modified Paris model with particle filtering (PF) framework to prognosis fatigue multi-cracks in metal structures. Since conventional Paris model is only applicable for the single crack, modified Paris model is proposed by introducing the mutual interaction between multiple cracks into stress intensity factor (SIF) expression and utilized to portray multiple fatigue crack growth process. To accurately monitor multi-cracks lengths, Lamb waves are periodically acquired during the fatigue growth of multiple cracks and fed into a deep autoencoder (DAE) network to automatically track response signals variations. Online monitoring model of multi-cracks lengths is then constructed by fitting the mapping relationship between the deep damage feature extracted by the bottleneck layer of DAE network and crack length. PF framework is further adopted to strategically fusing the prediction results of modified Paris model and real-time measurements of online monitoring model to reduce the uncertainties of material parameters and obtain more reliable prognosis results of multi-cracks lengths. The proposed method is demonstrated on center-hole metal specimens with two fatigue cracks. Experimental results show that the proposed method can accurately prognosis two fatigue cracks lengths, in which modified Paris model could more accurately describe multiple fatigue crack growth than conventional Paris model, and online monitoring model based on deep damage feature could more accurately track the fatigue crack length than artificial damage feature.

关键词： Fatigue crack growth Paris model deep autoencoder Lamb waves Particle filtering Metal structures

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A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region

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COASTAL ENGINEERING 2024年 190卷

作者： Naeini, Saeed Saviz Snaiki, Reda Univ Quebec Ecole Technol Super Dept Construct Engn Montreal PQ H3C 1K3 Canada

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a lowdimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge.

关键词： Storm surge Significant wave height deep learning deep autoencoder

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Prediction of compressive strength of nano-silica modified engineering cementitious composites exposed to high temperatures using hybrid deep learning models

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Tanyildizi, Harun Firat Univ Technol Fac Dept Civil Engn Elazig Turkiye

This study estimated the compressive strength of nano-silica-modified engineering cementitious composites subjected to high temperatures using innovative hybrid deep learning models. The innovative hybrid models in this study were designed using autoencoder (AE)-decision tree (DT) and autoencoder (AE)-extreme learning machine (ELM). Additionally, ELM, DT, and deep AE models in this study were designed to compare the results of innovative hybrid deep learning models. The sensitivity analysis was used for the statistical assessment of the experimental results. The input variables of the models were selected as the cement amount, fly ash amount, sand amount, water amount, high-range water reducer amount, PVA (polyvinyl alcohol) fiber amount, nano-silica amount, and the degree of exposure to high temperatures. The compressive strength was used as the output variable of the models. The mixture ratio in the experimental study was 583 kg/m3 cement, 467 kg/m3 sand, 700 kg/m3 fly ash, 187 kg/ m3 water, PVA fiber (0.5 %, 1 %, 1.5 % and 2 %) and nano silica (0 %, 1 %, 2 %, 3 % and 4 %) were used. The ELM, DT, and deep AE models estimated the compressive strength of nano-silicamodified engineering cementitious composites subjected to high temperatures with 93.86 %, 77.35 %, and 86.5 % accuracy, respectively. Also, the same compressive strength was estimated with 94.28 % and 98 % accuracy using the hybrid deep AE-DT and AE-ELM models. This study found that the innovative hybrid deep AEELM model predicted compressive strength with higher accuracy than the deep AE-DT, DT, ELM, and deep AE models. Additionally, the deep AE-DT model predicted compressive strength with higher accuracy than nonhybrid models. Thus, it can be stated that innovative hybrid deep models are more advantageous than other models in estimating the compressive strength of ECC. The sensitivity analysis obtained that the PVA fiber was the most significant variable affecting the compressive strength results of nan

关键词： Engineering cementitious composites Nano-silica High temperatures deep autoencoder Decision tree Extreme learning machine Hybrid deep learning

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Data-driven learning to identify biomarkers in bipolar disorder

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 226卷 107112-107112页

作者： Li, Zhuangzhuang Li, Wenmei Yan, Wei Zhang, Rongrong Xie, Shiping Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210023 Peoples R China Nanjing Med Univ Dept Psychiat Affiliated Nanjing Brain Hosp Nanjing 210029 Peoples R China

Background and Objective: Bipolar disorder (BD) is one of the primary causes of disability globally and can be easily misdiagnosed as schizophrenia or major depression due to their similar symptoms. Hence, it is of great significance to explore the pathogenesis of BD. Statistical analysis is currently the most common method for exploring the neuropathological mechanisms of psychiatric disorders. However, this method only considers the relationship between groups and does not reflect the individual-level diagnosis. There-fore, we developed machine learning algorithms to measure pathological brain changes in psychiatric dis-orders. Methods: An autoencoder and a feature selection method are proposed to identify the abnormal structural patterns of BD in this study. The autoencoder was constructed using structural imaging data from 1113 healthy controls, which aims to define the normal range of anatomical deviations to distin-guish healthy individuals from BD patients. The biomarkers of BD were identified by the reconstruction errors in each brain region. The proposed feature selection (FS)-select framework aimed to determine the optimal FS method and identify the most reproducible feature associated with BD. Results: We found that the left orbital region of the middle frontal gyrus had the greatest difference between healthy controls and BD patients using a trained autoencoder. The most reproducible feature was the left orbital region of the middle frontal gyrus by FS-select framework when using the different cross-validation strategies. Conclusions: A consistent result was obtained from the above two proposed methods wherein a signifi-cant difference between healthy controls and BD patients was identified in the left orbital region of the middle frontal gyrus.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Bipolar disorder Structural magnetic functional imaging deep autoencoder Feature selection

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deep learning shear capacity prediction of fibrous recycled aggregate concrete beams strengthened by side carbon fiber-reinforced polymer sheets

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COMPOSITE STRUCTURES 2022年 300卷

作者： Ghoniem, Amr Zagazig Univ Fac Engn Struct Engn Dept Zagazig 44519 Egypt

The goal of this paper is to evaluate the shear capacity of fibrous recycled aggregate concrete beams side bonded with carbon fiber-reinforced polymer (CFRP) based on numerical modeling and deep learning. Forty-five models were numerically investigated in ANSYS with different lengths and layers of vertical CFRP sheets. Concrete properties built for coarse recycled aggregate and various fibers as 3D and 5D steel, synthetic, and hybrid fibers added at a 0.75% volume fraction. Finally, feedforward, cascade-forward backpropagation, and NARX network algorithms were adopted to predict the shear capacity. Shear capacity improvement was significantly affected by CFRP rehabilitation. Double-layered rehabilitation applied on the zone between support and load was the most adequate. The performance of cascade-forward backpropagation in the estimation of shear capacity was superior to the other deep autoencoder models. The present paper offers good guidance on the practice field as a valuable supplement to the laboratory investigation and theoretical calculations.

关键词： CFRP Concrete properties deep autoencoder Hybrid fiber Numerical RCA

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Research on heart and lung sound separation method based on DAE-NMF-VMD

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 59页

作者： Sun, Wenhui Zhang, Yipeng Chen, Fuming Chinese Peoples Liberat Army Med Secur Ctr 940th Hosp Joint Logist Support Force Lanzhou 730050 Gansu Peoples R China Gansu Univ Tradit Chinese Med Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

关键词： Heart and lung sound separation deep autoencoder Variational mode decomposition Nonnegative matrix factorization deep learning

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Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection

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INFORMATION PROCESSING & MANAGEMENT 2022年第2期59卷 102844-102844页

作者： An, Peng Wang, Zhiyuan Zhang, Chunjiong Ningbo Univ Technol Coll Elect & Informat Engn Ningbo 315211 Zhejiang Peoples R China Tongji Univ Int Coll Football Shanghai 200092 Peoples R China Tongji Univ Dept Comp Sci Shanghai 201804 Peoples R China

Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.

关键词： Multidomain data Cyberattacks deep autoencoder GMM

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Modal decomposition-based hybrid model for stock index prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷 1页

作者： Lv, Pin Shu, Yating Xu, Jia Wu, Qinjuan Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China

Stock index prediction is considered one of the most challenging issues in the financial sector owing to its noise, volatility, and instability. Traditional stock index prediction methods, such as statistical and machine learning methods, cannot achieve a high denoising effect, and also cannot mine enough data features from the stock data, resulting in a poor prediction performance. deep learning has become an effective tool to predict non-stationary and nonlinear stock indices with strong learning ability. However, there is still room for prediction accuracy improvement if a single deep learning prediction model is replaced with a hybrid model. Therefore, this study proposes a novel deep learning hybrid model for stock index prediction named CEEMDAN-DAE-LSTM. In this hybrid model, the stock index is first decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) into a series of intrinsic mode functions (IMFs) arranged from high to low frequency. Next, the deep autoencoder (DAE) is applied to remove redundant data and extract deep-level features. Then, high-level abstract features are separately fed into long short-term memory (LSTM) networks to predict the stock returns of the next trading day. Finally, the final predicted value is obtained by synthesizing the value of each component. Empirical research results on six stock indices representing both developed and emerging markets showed that our model is superior to other reference models in terms of prediction accuracy and stock index trends;furthermore, it has higher prediction performance for stock indices with greater volatility. In general, this model could be applicable to various stock markets with different degrees of development.

关键词： Stock index prediction deep learning hybrid prediction model Adaptive noise complete ensemble empirical mode decomposition deep autoencoder Long short-term memory

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