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...
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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.
Serous effusion is frequently encountered specimen type in (cyto)pathological assessment. However, this assessment is time-consuming and leads to variability among pathologists. The cell nuclei is seen as the corner s...
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ISBN:
(纸本)9781538615010
Serous effusion is frequently encountered specimen type in (cyto)pathological assessment. However, this assessment is time-consuming and leads to variability among pathologists. The cell nuclei is seen as the corner stone for diagnostic purposes in automatic analysis of cytopathological images. In this paper, a stacked sparse autoencoder (SSAE) is proposed for nuclei detection in serous effusion cytology. SSAE is an unsupervised deep learning method which learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. High-level features, obtained via the autoencoder, are then subsequently fed to a softmax classifier which categorizes each patch as nuclei or non-nuclei. With a detection rate of 98.3% based on images of serous effusion cytology, proposed method shows good performance.
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)....
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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.
Deep learning is an important research achievement of artificial intelligence in recent years and has received special attention from scientists around the world. This study applies deep learning to spectral analysis ...
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Deep learning is an important research achievement of artificial intelligence in recent years and has received special attention from scientists around the world. This study applies deep learning to spectral analysis techniques and proposes a rapid analysis method for cereals. First, the advanced features of the near infrared spectroscopy (NIR) were extracted by the deep learning-stacked sparse autoencoder (SSAE) method, and then the prediction model is built using the affine transformation (AT) and the extreme learning machine (ELM). Experiments were conducted on corn and rice data sets to verify the effectiveness of the method. The results show that the proposed method achieves good prediction results and is superior to other typical NIR analysis methods.
Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the c...
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Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.
Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to ...
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ISBN:
(纸本)9781728138046
Effective early diagnosis of autism can have a significant impact on its intervention and treatment. In this paper, an approach is proposed for comprehensively considering genetic factors and environmental factors to predict the severity of autism. According to the Childhood Autism Rating Scale (CARS), a sample set was collected from the autism clinic and a predictive model based on a stacked sparse autoencoder combined with a softmax classifier was constructed. We compared the proposed model with decision trees and support vector machines. Experiments show that the proposed model has a highest accuracy in predicting the severity of autism. Our method can help patients predict their condition and assist doctors in accurate diagnosis.
Current prosthetic control systems explored in the literature that use pattern recognition can perform a limited number of pre-assigned functions, as they must be trained using muscle signals for every movement the us...
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Current prosthetic control systems explored in the literature that use pattern recognition can perform a limited number of pre-assigned functions, as they must be trained using muscle signals for every movement the user wants to perform. The goal of this study was to explore the development of a prosthetic control system that can classify both trained and novel gestures, for applications in commercial prosthetic arms. The first objective of this study was to evaluate the feasibility of three different algorithms in classifying raw sEMG data for both trained isometric gestures, and for novel isometric gestures that were not included in the training data set. The algorithms used were; a feedforward multi-layer perceptron (FFMLP), a stacked sparse autoencoder (SSAE), and a convolution neural network (CNN). The second objective is to evaluate the algorithms' abilities to classify novel isometric gestures that were not included in the training data set, and to determine the effect of different gesture combinations on the classification accuracy. The third objective was to predict the binary (flexed/extended) digit positions without training the network using kinematic data from the participants hand. A g-tec USB Biosignal Amplifier was used to collect data from eight differential sEMG channels from 10 able-bodied participants. These participants performed 14 gestures including rest, that involved a variety of discrete finger flexion/extension tasks. Forty seconds of data were collected for each gesture at 1200 Hz from eight bipolar sEMG channels. These 14 gestures were then organized into 20 unique gesture combinations, where each combination consisted of a different sub-set of gestures used for training, and another sub-set used as the novel gestures, which were only used to test the algorithms' predictive capabilities. Participants were asked to perform the gestures in such a way where each digit was either fully flexed or fully extended to the best of their abilities.
Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extract...
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Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk st...
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Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury;scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early s...
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ISBN:
(纸本)9789881476852
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early stage because the feature can be observed readily in clinical examinations with a high specificity of 93%. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. To address this problem, we proposed an automated method for analyzing patches of clinical basal cell carcinoma images using stacked sparse autoencoder ( SSAE). SSAE learns high-level features in unsupervised manner and all learned features are fed into a softmax classifier for translucency detection. Across the 4401 patches generated from 32 clinical images, the proposed method achieved a 93% detection accuracy from a five-fold cross-validation. The preliminary result suggested that the proposed method could detect translucency from skin images.
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