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Crack detection in carbide anvil using acoustic signal and deep learning with particle swarm optimisation

MEASUREMENT 2021年 173卷 108668-108668页

作者： Yang, Jun Chen, Bin Wang, Yanan Wang, Chunzhu Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

The carbide anvil plays a significant role in producing synthetic diamond. However, it suffers from complex alternating stresses and consequently results in fatigue damage such as cracks. Accurate crack detection of the carbide anvil still faces a significant challenge. This paper develops an acoustical crack detection method of the carbide anvil using the deep learning. In the method, an online sound impulse extraction strategy is designed to construct an anvil dataset. Subsequently, the stacked autoencoder model is designed to learn a robust feature representation of the anvil states from the measured sound impulse signals. Besides, an improved particle swarm optimisation method based on classification probability is proposed for the hyper-parameter optimisation. Finally, the performance of the proposed method is evaluated using experimental data. This research can provide a potential tool for the engineers to automatically detect the crack of the carbide anvils in the diamond industry.

关键词： Crack detection Acoustic signal stacked autoencoder Particle swarm optimisation Carbide anvil

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Forecasting influenza epidemics in Hong Kong using Google search queries data: A new integrated approach

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115604-115604页

作者： Liu, Yunhao Feng, Gengzhong Tsui, Kwok-Leung Sun, Shaolong Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Tat Chee Ave Hong Kong Peoples R China Virginia Polytech Inst & State Univ Grad Dept Ind & Syst Engn Blacksburg VA 24061 USA

Forecasting influenza epidemics has important practical implications. However, the performance of traditional methods adopting in Hong Kong influenza forecasting is limited due to its particularity. This paper proposes an integrated approach for Hong Kong influenza epidemics forecasting. The novelties of our approach mainly include: firstly, we adopt a model for Google search queries data collection and selection in Hong Kong to substitute Google Correlate. Secondly, we adopt the stacked autoencoder (SAE) to reduce the dimensionality of Google search queries data. Thirdly, we adopt a signal decomposition method named variational mode decomposition (VMD) to decompose the influenza data into modes with different frequencies, which can extract the characteristic. Fourthly, we use artificial neural networks (ANN) to forecast these modes of influenza epidemics extracted by VMD respectively, then these forecasts of each mode are added to generate the final forecasting results. From the perspective of forecasting accuracy and hypothesis tests, the empirical results show that our proposed integrated approach SAE-VMD-ANN significantly outperforms some other benchmark models both in the whole period and influenza season. The performance of our proposed model during the COVID-19 pandemic is checked too.

关键词： Influenza epidemics forecasting Variational mode decomposition stacked autoencoder Artificial neural networks Google search queries data

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A 3D convolutional neural network based near-field acoustical holography method with sparse sampling rate on measuring surface

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MEASUREMENT 2021年 177卷 109297-109297页

作者： Wang, Jiaxuan Zhang, Zhifu Huang, Yizhe Li, Zhuang Huang, Qibai Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Near-field acoustical holography (NAH) is an efficient noise diagnosis method with the deficiencies including wraparound error, which will increase when the spatial sampling rate is reduced below the minimum specified by Shannon-Nyquist theorem. Based on 3D convolutional neural network (3D-CNN) and stacked autoencoder (SAE), a method called CSA-NAH is proposed to reduce the wraparound error under sparse measuring. Subsequently, numerical calculations are carried out to illustrate the feasibility and performance of CSA-NAH. The results show that when holographic measurement point number is 64, average reconstruction error of CSA-NAH on an aluminum plate for sound pressure within 2000 Hz is 4.32%, while the latest existing methods is greater than 10%. For error in 1200 Hz similar to 2000 Hz, the error is reduced from more than 15% of the existing methods to 5.5%. Therefore, the application of the proposed CSA-NAH can cut down the measuring cost by reducing the number of microphones without wraparound error.

关键词： Near-field acoustical holography Low sampling rate Wraparound error Convolutional neural network stacked autoencoder

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The Forecasting of PM2.5 Using a Hybrid Model Based on Wavelet Transform and an Improved Deep Learning Algorithm

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IEEE ACCESS 2019年 7卷 142814-142825页

作者： Qiao, Weibiao Tian, Wencai Tian, Yu Yang, Quan Wang, Yining Zhang, Jianzhuang North China Univ Water Resources & Elect Power Sch Environm & Municipal Engn Zhengzhou 450046 Henan Peoples R China Sinopec Zhongyuan Petr Engn Co Ltd Zhengzhou 450046 Henan Peoples R China

In recent years, the haze has caused serious troubles to peoples lives, with the continuous increase of PM2.5 emissions. The accurate prediction of PM2.5 is very crucial for policy makers to make predictive measures. Due to the nonlinearity of the PM2.5 time series, it is difficult to predict accurately. Despite some studies about PM2.5 being proposed, the problem of the LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers isnt still solved. In this study, a novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM is proposed. Firstly, six study sites from China are taken as examples and WT is used to decompose PM2.5 time series into several low-and high- frequency components based on different samples. Secondly, the decomposed components are predicted based on SAE-LSTM. Finally, the predicted results are reconstructed in view of all low-and high-frequency components and the predicted results are obtained. The results imply that: (1) the forecasting performance of SAE-LSTM is better than that of other models (e.g., BP (back propagation)) used for comparison;(2) for six different PM 2.5 samples, four orders five layers, five orders six layers, five orders seven layers, three orders six layers, five orders seven layers, and five orders six layers are the most appropriate. The conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.

关键词： Predictive models Forecasting Wavelet transforms Logic gates Neural networks Atmospheric modeling PM 2 5 time series wavelet transform stacked autoencoder long short-term memory prediction

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Discriminative feature learning for blade icing fault detection of wind turbine

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第11期31卷 115102-115102页

作者： Yi, Huaikuan Jiang, Qinchao East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

The early detection of blade icing is gaining increasing attention due to its importance in guaranteeing wind turbine safety and operation efficiency. In this study, a wind turbine icing fault detection method based on discriminative feature learning is proposed. First, a stacked autoencoder (SAE) is trained to generate representations, which utilizes a large amount of normal operating data, as well as time series correlation information. Second, discriminative features are obtained by combining the original data, SAE-extracted features, and the residual vector. Third, the sparse linear discriminant analysis is performed on the discriminative features to achieve simultaneous feature selection and dimension reduction. Finally, the wind turbine operation status is examined using the learned discriminative feature. The proposed discriminative feature learning-based fault detection scheme is tested on a benchmark wind turbine icing dataset. Results of the comparative trial verify the feasibility and superiority of the proposed method.

关键词： fault detection stacked autoencoder wind turbine discriminative feature learning

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An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 289卷 112438-112438页

作者： Qiao, Weibiao Wang, Yining Zhang, Jianzhuang Tian, Wencai Tian, Yu Yang, Quan Yan Shan Univ Sch Vehicle & Energy Qinhuangdao 066004 Hebei Peoples R China North China Univ Water Resources & Elect Power Sch Environm & Municipal Engn Zhengzhou 450046 Peoples R China Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China

Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet' layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT.

关键词： PM10 Long short-term memory stacked autoencoder Wavelet transform Prediction

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Spatial-Adaptive Siamese Residual Network for Multi-/Hyperspectral Classification

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REMOTE SENSING 2020年第10期12卷 1640-1640页

作者： He, Zhi He, Dan Sun Yat Sen Univ Sch Geog & Planning Southern Marine Sci & Engn Guangdong Lab Zhuhai Guangdong Prov Key Lab Urbanizat & GeoSimulatCtr Guangzhou 510275 Guangdong Peoples R China Dongguan Univ Technol City Coll Dongguan 511700 Peoples R China

Deep learning methods have been successfully applied for multispectral and hyperspectral images classification due to their ability to extract hierarchical abstract features. However, the performance of these methods relies heavily on large-scale training samples. In this paper, we propose a three-dimensional spatial-adaptive Siamese residual network (3D-SaSiResNet) that requires fewer samples and still enhances the performance. The proposed method consists of two main steps: construction of 3D spatial-adaptive patches and Siamese residual network for multiband images classification. In the first step, the spectral dimension of the original multiband images is reduced by a stacked autoencoder and superpixels of each band are obtained by the simple linear iterative clustering (SLIC) method. Superpixels of the original multiband image can be finally generated by majority voting. Subsequently, the 3D spatial-adaptive patch of each pixel is extracted from the original multiband image by reference to the previously generated superpixels. In the second step, a Siamese network composed of two 3D residual networks is designed to extract discriminative features for classification and we train the 3D-SaSiResNet by pairwise inputting the training samples into the networks. The testing samples are then fed into the trained 3D-SaSiResNet and the learned features of the testing samples are classified by the nearest neighbor classifier. Experimental results on three multiband image datasets show the feasibility of the proposed method in enhancing classification performance even with limited training samples.

关键词： remote sensing classification stacked autoencoder superpixel Siamese network

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An Efficient Framework for Animal Breeds Classification Using Semi-Supervised Learning and Multi-Part Convolutional Neural Network (MP-CNN)

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IEEE ACCESS 2019年 7卷 151783-151802页

作者： Meena, S. Divya Agilandeeswari, L. Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

The automatic classification of animal images is an onerous task due to the challenging image conditions, especially when it comes to animal breeds. In this paper, we built a semi-supervised learning based Multi-part Convolutional Neural Network (MP-CNN) that classifies 35,992 animal images from ImageNet into 27 different classes of animals. The proposed model classifies the animals on both generic and fine-grained level. The animal breeds are accurately classified using Multi-part Convolutional Neural Network with a hybrid feature extraction framework of Fisher Vector based stacked autoencoder. Furthermore, with Semi-supervised learning based pseudo-labels, the model classifies new classes of unlabeled images too. Modified Hellinger Kernel classifier has been used to re-train the misclassified classes of animals and thereby improve the performance obtained from MP-CNN. The model has experimented with varied tasks to analyze its performance in each of the cases. The experimental results have proved that the coalesced approach of MP-CNN with pseudo-labels can accurately classify animal breeds and we have achieved an accuracy of 99.95 from the proposed model.

关键词： Feature extraction Convolutional neural networks Dogs Semisupervised learning Image classification Fisher vector inception-V3 modified hellinger Kernel classifier multi-part based convolutional neural network pseudo-labels semi-supervised learning stacked autoencoder

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Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2023年第1期42卷

作者： Yang, Lu Liu, Hui Chen, Fugang Kunming Univ Sci & Technol Fac Informat Engn & Automation Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Yunnan Kungang Elect & Informat Sci Ltd Kunming 650302 Yunnan Peoples R China

The difficulty of endpoint determination in basic oxygen furnace (BOF) steelmaking lies in achieving accurate real-time measurements of carbon content and temperature. For the characteristics of serious nonlinearity between process data, deep learning can perform excellent nonlinear feature representation for complex structural data. However, there is a process drift phenomenon in BOF steelmaking, and the existing deep learning-based soft sensor models cannot adapt to changes in the characteristics of samples, which may lead to their performance degradation. To deal with this problem, considering the characteristics of multimode distribution of process data, an adaptive updating deep learning model based on von-Mises Fisher (vMF) mixture model and weighted stacked autoencoder is proposed. First, the stacked autoencoder (SAE) and vMF mixture model are constructed for complex structural data, which can initially establish nonlinear mapping relationships and division of different distributions. Second, for each query sample, the basic SAE network will perform online adaptive fine-tuning according to its data with the same distribution to achieve dynamic updating. Moreover, each sample is assigned a weight according to its similarity with the query sample. Through the designed weighted loss function, the updated deep network will better match the working conditions of the query sample. Experimental studies with numerical examples and actual BOF steelmaking process data are provided to demonstrate the effectiveness of the proposed method.

关键词： BOF steelmaking endpoint stacked autoencoder vMF mixture model similarity measure adaptive weighted updating

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Deep Semi-Supervised Just-in-Time Learning Based Soft Sensor for Mooney Viscosity Estimation in Industrial Rubber Mixing Process

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POLYMERS 2022年第5期14卷 1018页

作者： Zhang, Yan Jin, Huaiping Liu, Haipeng Yang, Biao Dong, Shoulong Kunming Univ Sci & Technol Fac Informat Engn & Automat Dept Automat Kunming 650500 Yunnan Peoples R China Yunnan Key Lab Comp Technol Applicat Kunming 650500 Yunnan Peoples R China Beijing Inst Technol Sch Chem & Chem Engn Dept Chem Engn Beijing 100081 Peoples R China

Soft sensor technology has become an effective tool to enable real-time estimations of key quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring and a control of rubber manufacturing. However, it remains a challenging issue to develop high-performance soft sensors due to improper feature selection/extraction and insufficiency of labeled data. Thus, a deep semi-supervised just-in-time learning-based Gaussian process regression (DSSJITGPR) is developed for Mooney viscosity estimation. It integrates just-in-time learning, semi-supervised learning, and deep learning into a unified modeling framework. In the offline stage, the latent feature information behind the historical process data is extracted through a stacked autoencoder. Then, an evolutionary pseudo-labeling estimation approach is applied to extend the labeled modeling database, where high-confidence pseudo-labeled data are obtained by solving an explicit pseudo-labeling optimization problem. In the online stage, when the query sample arrives, a semi-supervised JITGPR model is built from the enlarged modeling database to achieve Mooney viscosity estimation. Compared with traditional Mooney-viscosity soft sensor methods, DSSJITGPR shows significant advantages in extracting latent features and handling label scarcity, thus delivering superior prediction performance. The effectiveness and superiority of DSSJITGPR has been verified through the Mooney viscosity prediction results from an industrial rubber-mixing process.

关键词： soft sensor Mooney viscosity just-in-time learning semi-supervised learning stacked autoencoder gaussian process regression rubber mixing process

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