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Nondestructive evaluation of bonding quality of dual-layer coatings based on the multi-feature ultrasonic method

APPLIED ACOUSTICS 2024年 224卷

作者： Fan, Shaorui Yuan, Maodan Xu, Jianlin Song, Yongfeng Chen, Yan Ji, Xuanrong Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou 510004 Peoples R China

Ultrasound has been extensively utilized to evaluate the bonding quality of coatings. However, current ultrasonic methods based on single temporal or spectral feature cannot accurately characterize the bonding quality within industrial coatings, especially for potential debonding at different layers. This paper proposed an ultrasonic method with multiple temporal and spectral features to evaluate the bonding condition of dual-layer coatings. Ultrasonic features were automatically extracted based on stacked sparse autoencoder (SSAE) to compare with the conventional selected features. Firstly, numerical simulations were carried out to analyze the variation of ultrasonic signals with different stiffnesses at topcoat and primer interfaces. Two temporal and three spectral features were manually extracted and show high sensitivity to different bonding conditions of the two interfaces. Meanwhile, a SSAE network was designed to automatically extract ultrasonic features. The high correlation between the manually and automatically extracted features demonstrates its effectiveness of automatic feature extraction via SSAE. Then, a 50 MHz ultrasonic pulse-echo system was applied to collect signals from 115 coating samples of laptop shell. Four sets of ultrasonic features were extracted via manual method and SSAE networks. Principal components analysis was then employed to reduce the feature space and support vector machine was applied to distinguish topcoat debonding and primer debonding from the intact coatings. The results show that the feature set including 64 temporal and spectral features extracted by SSAE exhibit the best classification with an accuracy of 95.652 %. This proposed multi-feature ultrasonic method can accurately assess the bonding quality of different interfaces in the multi-layer coatings.

关键词： Dual -layer coating Topcoat debonding Primer debonding Ultrasonic features stacked sparse autoencoder Support vector machine

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Assessment of PD severity in gas-insulated switchgear with an SSAE

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第4期11卷 423-430页

作者： Tang, Ju Jin, Miao Zeng, Fuping Zhang, Xiaoxing Huang, Rui Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Province Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China State Grid Shandong Elect Power Co Shandong Elect Power Res Inst Jinan 250002 Peoples R China

Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft-max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer-wise pre-training and supervised fine-tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.

关键词： partial discharge measurement gas insulated switchgear statistical analysis neural nets learning (artificial intelligence) encoding feature extraction computerised instrumentation PD severity assessment gas-insulated switchgear SSAE partial discharge severity assessment discharge time discharge amplitude deep-learning neural network model stacked sparse autoencoder feature extraction soft-max classifier unsupervised greedy layer-wise pre-training method supervised fine-tuning method support vector machine algorithm

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Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第4期31卷 799-814页

作者： Zhang, Yanxi You, Deyong Gao, Xiangdong Wang, Congyi Li, Yangjin Gao, Perry P. Guangdong Univ Technol Guangdong Prov Welding Engn Technol Res Ctr Guangzhou 510006 Peoples R China US China Youth Educ Solut Fdn New York NY 10019 USA

The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible band visual sensor system, a spectrometer and two photodiodes. The signals captured by different sensors are analyzed via signal or digital image processing algorithms, and distinct features are extracted from these signals to depict the online welding statuses. A deep learning framework based on stacked sparse autoencoder (SSAE) is established to model the relationship between the multi-sensor features and their corresponding welding statuses, and Genetic algorithm (GA) is applied to optimize the parameters of the SSAE framework (SSAE-GA). The proposed framework achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest. Three new experiments with different welding parameters are implemented to validate the effectiveness and generalization of our proposed method. This study provides a novel and accurate method for high-power disk laser welding status monitoring.

关键词： Features fusion Deep learning Genetic algorithm stacked sparse autoencoder Multiple-sensor signals

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A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

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IEEE ACCESS 2020年 8卷 202100-202110页

作者： Yang, Jun Ma, Zhengmin Wang, Jin Fu, Yunfa Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650093 Yunnan Peoples R China Yunnan Univ Fac Informat Kunming 650500 Yunnan Peoples R China

Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-to-noise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.

关键词： Brain– computer interface discriminative and representative deep learning feature fusion convolution neural network stacked sparse autoencoder

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Deep neural network for semi-automatic classification of term and preterm uterine recordings

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 105卷 101861-101861页

作者： Chen, Lili Xu, Huoyao Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Chongqing Key Lab Urban Rail Transit Vehicle Chongqing 400074 Peoples R China

Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse autoencoder (SSAE) network with two hidden layers and one final softmax layer. To this end, the bursts of all 26 recordings of the publicly available TPEHGT DS database corresponding to uterine contraction intervals and non-contraction intervals (dummy intervals) were manually segmented. 20 features were extracted by two feature extraction algorithms including sample entropy and wavelet entropy. Afterwards, the SSAE network is adopted to learn high-level features from raw features by unsupervised learning. The softmax layer is added at the top of the SSAE network for classification. In order to verify the effectiveness of the proposed method, this study used 10-fold cross-validation and four indicators to evaluate classification performance. Experimental research results display that the performance of deep neural network can achieve Sensitivity of 98.2%, Specificity of 97.74%, and Accuracy of 97.9% in the publicly TPEHGT DS database. The performance of deep neural network outperforms the comparison models including deep belief networks (DBN) and hierarchical extreme learning machine (H-ELM). Finally, experimental research results reveal that the proposed method could be valid applied to semi-automatic identification of term and preterm uterine recordings.

关键词： EHG and TOCO signals sample entropy wavelet entropy stacked sparse autoencoder softmax

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Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning

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APPLIED ENERGY 2019年 233卷 930-942页

作者： Zhang, Zehan Li, Shuanghong Xiao, Yawen Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Key Lab Minist Educ Syst Control & Informat Proc Shanghai 200240 Peoples R China Orient Secur Co Ltd Shares South Zhong Shan Rd 318 Shanghai 200010 Peoples R China

Fault diagnosis technology is a vital tool for ensuring the stability and durability of solid oxide fuel cell systems. Simultaneous faults are common problems in modern industrial systems. Many fault diagnosis methods have been successfully designed for solid oxide fuel cell systems, but they only address independent faults, and only a few researchers have studied simultaneous fault diagnosis. The design of a simultaneous fault diagnosis method for solid oxide fuel cell systems remains a huge challenge. This study introduces a deep learning technology into the simultaneous fault diagnosis for the solid oxide fuel cell system and proposes a novel simultaneous fault diagnosis method on the basis of a deep learning network called stacked sparse autoencoder. The proposed method can automatically capture the essential features from the original system variables, thereby consuming minimal time on heavily hand-crafted features. Moreover, massive unlabeled samples are fully utilized through the proposed method. Experimental results show that the proposed method can diagnose simultaneous faults with high accuracy requiring only a few independent fault samples and a minimal number of simultaneous fault samples. Comparisons between traditional machine learning methods and experimental results on training sets of different sizes verify the superiority of the proposed method. Deep learning provides an effective and promising approach for simultaneous fault diagnosis in the field of fuel cells.

关键词： Deep learning stacked sparse autoencoder Automated feature learning Simultaneous fault diagnosis SOFC system

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Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 783-815页

作者： Bao Rong Chang Hsiu-Fen Tsai Yu-Chieh Lin Department of Computer Science and Information Engineering National University of KaohsiungKaohsiungTaiwan Department of Fragrance and Cosmetic Science Kaohsiung Medical UniversityKaohsiungTaiwan

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system *** paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput ***,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data ***,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job *** a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively.

关键词： stacked sparse autoencoder Elasticsearch distributed indexing data retrieval deep neural network job scheduling

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A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

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Computers, Materials & Continua 2024年第1期78卷 845-871页

作者： Zhenxiang He Xunxi Wang Chunwei Li School of Cyberspace Security Gansu University of Political Science and LawLanzhou730000China

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated ***,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network *** leads to compromised detection accuracy and overlooked temporal *** this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection *** approach reduces feature dimensionalitywith a stacked sparse autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection *** the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 *** the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 *** findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is *** contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

关键词： Network intrusion detection bidirectional long short-term memory network time series stacked sparse autoencoder temporal convolutional network time steps

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Brain Tumor Detection by Using stacked autoencoders in Deep Learning

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JOURNAL OF MEDICAL SYSTEMS 2019年第2期44卷 1-12页

作者： Amin, Javaria Sharif, Muhammad Gul, Nadia Raza, Mudassar Anjum, Muhammad Almas Nisar, Muhammad Wasif Bukhari, Syed Ahmad Chan COMSATS Univ Islamabad Dept Comp Sci Wah Campus Islamabad Pakistan POF Hosp Wah Med Coll Dept Radiol Rawalpindi Punjab Pakistan NUST Coll EME Rawalpindi Punjab Pakistan St Johns Univ Collins Coll Profess Studies Div Comp Sci Math & Sci New York NY USA

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.

关键词： stacked sparse autoencoder Glioma Softmax Hidden size Magnetic resonance images

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Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep autoencoder

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JOURNAL OF MEDICAL SYSTEMS 2017年第10期41卷 1-11页

作者： Jia, Wenjuan Yang, Ming Wang, Shui-Hua Nanjing Normal Univ Sch Comp Sci & Engn Nanjing 210023 Jiangsu Peoples R China Nanjing Med Univ Childrens Hosp Dept Radiol Nanjing 210008 Jiangsu Peoples R China CUNY City Coll Dept Elect Engn New York NY 10031 USA

Hearing loss, a partial or total inability to hear, is known as hearing impairment. Untreated hearing loss can have a bad effect on normal social communication, and it can cause psychological problems in patients. Therefore, we design a three-category classification system to detect the specific category of hearing loss, which is beneficial to be treated in time for patients. Before the training and test stages, we use the technology of data augmentation to produce a balanced dataset. Then we use deep autoencoder neural network to classify the magnetic resonance brain images. In the stage of deep autoencoder, we use stacked sparse autoencoder to generate visual features, and softmax layer to classify the different brain images into three categories of hearing loss. Our method can obtain good experimental results. The overall accuracy of our method is 99.5%, and the time consuming is 0.078 s per brain image. Our proposed method based on stacked sparse autoencoder works well in classification of hearing loss images. The overall accuracy of our method is 4% higher than the best of state-of-the-art approaches.

关键词： Data augmentation stacked sparse autoencoder Regularization Minibatch scaled conjugate gradient

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