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

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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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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Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification

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OPTICAL ENGINEERING 2017年第11期56卷 113106-113106页

作者： Wan, Xiaoqing Zhao, Chunhui Gao, Bing Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

The integration of an edge-preserving filtering technique in the classification of a hyperspectral image (HSI) has been proven effective in enhancing classification performance. This paper proposes an ensemble strategy for HSI classification using an edge-preserving filter along with a deep learning model and edge detection. First, an adaptive guided filter is applied to the original HSI to reduce the noise in degraded images and to extract powerful spectral-spatial features. Second, the extracted features are fed as input to a stacked sparse autoencoder to adaptively exploit more invariant and deep feature representations;then, a random forest classifier is applied to fine-tune the entire pretrained network and determine the classification output. Third, a Prewitt compass operator is further performed on the HSI to extract the edges of the first principal component after dimension reduction. Moreover, the regional growth rule is applied to the resulting edge logical image to determine the local region for each unlabeled pixel. Finally, the categories of the corresponding neighborhood samples are determined in the original classification map;then, the major voting mechanism is implemented to generate the final output. Extensive experiments proved that the proposed method achieves competitive performance compared with several traditional approaches. (c) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： classification adaptive guided filter hyperspectral imagery stacked sparse autoencoder Prewitt compass operator

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A Deep Learning Method for Microaneurysm Detection in Fundus Images 1

A Deep Learning Method for Microaneurysm Detection in Fundus...

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1st IEEE International Conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Shan, Juan Li, Lin Pace Univ Seidenberg Sch CSIS Dept Comp Sci New York NY 10038 USA Seattle Univ Coll Sci & Engn Dept Comp Sci & Software Engn Seattle WA 98122 USA

ISBN: (纸本)9781509009435

Diabetic Retinopathy (DR) is the leading cause of blindness in the working-age population. Microaneurysms (MAs), due to leakage from retina blood vessels, are the early signs of DR. However, automated MA detection is complicated because of the small size of MA lesions and the low contrast between the lesion and its retinal background. Recently deep learning (DL) strategies have been used for automatic feature extraction and classification problems, especially for image analysis. In this paper, a stacked sparse autoencoder (SSAE), an instance of a DL strategy, is presented for MA detection in fundus images. Small image patches are generated from the original fundus images. The SSAE learns high-level features from pixel intensities alone in order to identify distinguishing features of MA. The high-level features learned by SSAE are fed into a classifier to categorize each image patch as MA or non-MA. The public benchmark DIARETDB is utilized to provide the training/testing data and ground truth. Among the 89 images, totally 2182 image patches with MA lesions, serve as positive data, and another 6230 image patches without MA lesions are generated by a randomly sliding window operation, to serve as negative data. Without any blood vessel removal or complicated preprocessing operations, SSAE learned directly from the raw image patches, and automatically extracted the distinguishing features to classify the patches using Softmax Classifier. By employing the fine-tuning operation, an improved F-measure 91.3% and an average area under the ROC curve (AUC) 96.2% were achieved using 10-fold cross-validation.

关键词： deep learning stacked sparse autoencoder feature representation automated microaneurysm detection diabetic retinopathy

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A Distributed Anomaly Detection Method of Operation Energy Consumption using Smart Meter Data 11

A Distributed Anomaly Detection Method of Operation Energy C...

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11th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Yuan, Ye Jia, Kebin Beijing Univ Technol Coll Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9780769556680

Along with the rapid development of communication network construction, the operation energy consumption grows significantly in recent years, and the expensive electricity cost is hard to he ignored. Therefore, it is necessary to develop an operation energy anomaly detection mechanism to enhance the control ability of electricity cost. According to the practical distribution and data characteristic of smart meters, this paper presents a distributed anomaly detection method of operation energy consumption based on deep learning methods. An IOT-based distributed structure is implemented to execute data interaction. stacked sparse autoencoder is used to extract the high-level representation from massive monitoring data acquired automatically from actual smart meter network. Then softmax is used for classification to detect anomaly and send alarm messages using web technologies. The experimental results show that the proposed method with good prospect for intelligent applications achieves better accuracy and meanwhile decreases computing delay caused by central arithmetic method.

关键词： operation consumption anomaly detection stacked sparse autoencoder smart meter deep learning IOT

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MULTILAYER FEATURE LEARNING FOR POLARIMETRIC SYNTHETIC RADAR DATA CLASSIFICATION

MULTILAYER FEATURE LEARNING FOR POLARIMETRIC SYNTHETIC RADAR...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Xie, Huiming Wang, Shuang Lin, Kun Lin, Shaopeng Hou, Biao Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781479957750

Features are important for polarimetric synthetic aperture radar (PolSAR) image classification. Various methods focus on extracting feature artificially. Compared with them, we have developed a method to learn feature automatically. The method is based on deep learning which can learn multilayer features. In this paper, stacked sparse autoencoder (SAE) as one of the deep learning models is applied as a useful strategy to achieve the goal. For improving the classification result, we use a small amount of labels to fine-tuning the parameters of the proposed method Finally, a real PolSAR dataset is used to verify the effectiveness. Experiment result confirms that the proposed method provides noteworthy improvements in classification accuracy and visual effect.

关键词： polarimetric synthetic radar data deep learning feature learning stacked sparse autoencoder

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Facial Expression Recognition and Generation using sparse autoencoder 1

Facial Expression Recognition and Generation using Sparse Au...

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International Conference on Smart Computing (SMARTCOMP)

作者： Liu, Yunfan Hou, Xueshi Chen, Jiansheng Yang, Chang Su, Guangda Dou, Weibei Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781479957118

Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network-stacked sparse autoencoder (SAE). This method classifies facial expressions into six categories (i.e. happiness, sadness, anger, fear, disgust and surprise). In order to extract the representation of facial expressions, we choose the optical flow method because it could analyze video image sequences effectively and reduce the influence of personal appearance difference on facial expression recognition. Then, we train the stacked SAE with the optical flow field as the input to extract high-level features. To achieve classification, we apply a softmax classifier on the top layer of the stacked SAE. This method is applied to the Extended Cohn-Kanade Dataset (CK+). The expression classification result shows that the SAE performances the classification effectively and successfully. Further experiments (transformation and purification) are carried out to illustrate the application of the feature extraction and input reconstruction ability of SAE.

关键词： facial expression recognition deep learning stacked sparse autoencoder optical flow

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A Distributed Anomaly Detection Method of Operation Energy Consumption using Smart Meter Data

A Distributed Anomaly Detection Method of Operation Energy C...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Ye Yuan Kebin Jia College of Electronic Information and Control Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781509001897

Along with the rapid development of communication network construction, the operation energy consumption grows significantly in recent years, and the expensive electricity cost is hard to be ignored. Therefore, it is necessary to develop an operation energy anomaly detection mechanism to enhance the control ability of electricity cost. According to the practical distribution and data characteristic of smart meters, this paper presents a distributed anomaly detection method of operation energy consumption based on deep learning methods. An IOT-based distributed structure is implemented to execute data interaction. stacked sparse autoencoder is used to extract the high-level representation from massive monitoring data acquired automatically from actual smart meter network. Then softmax is used for classification to detect anomaly and send alarm messages using web technologies. The experimental results show that the proposed method with good prospect for intelligent applications achieves better accuracy and meanwhile decreases computing delay caused by central arithmetic method.

关键词： operation consumption anomaly detection stacked sparse autoencoder smart meter deep learning IOT

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