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Intelligent Anomaly Detection Method of Gateway Electrical Energy Metering Devices using Deep Learning

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第7期14卷 859-867页

作者： Zhang, Lihua Chen, Xu Zhang, Chao Zhang, Lingxuan Zou, Binghang State Grid Ningxia Elect Power Co Ltd Mkt Serv Ctr Metrol Ctr Yinchuan Peoples R China Sichuan Univ Coll Elect Engn Chengdu Peoples R China

anomaly detection of gateway electrical energy metering device is important for maintenance and operations in the power systems. Traditionally, anomaly detection was typically performed manually through the analysis of the collected energy information. However, the manual process is time-consuming and labor-intensive. In this condition, this paper proposes a hybrid deep-learning model, which integrates stacked intelligently detecting the abnormal events of gateway electrical energy metering device. The proposed model named SAE-LSTM model, first uses SAE to extract deep latent features of threephase voltage data collected from the gateway electrical energy metering device, and then adopts LSTM for separating the abnormal events based on the extracted deep latent features. The SAE-LSTM model, can effectively highlight the temporal information of the electrical data, thereby enhancing the accuracy of anomaly detection. The simulation experiments verify the advantages of the SAE-LSTM model in anomaly detection under different signal-to-noise ratios. The experimental results of real datasets demonstrate that it is suitable for anomaly detection of gateway electrical energy metering devices in practical scenarios.

关键词： Anomaly detection gateway electric energy meter-ing device stacked autoencoder long short-term memory

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A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

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SENSORS 2022年第10期22卷 3687-3687页

作者： Nguyen, Tuan-Khai Ahmad, Zahoor Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen;as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen's useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback-Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen's work.

关键词： acoustic emission deep neural network concrete structures health indicator Kullback-Leibler divergence remaining useful life stacked autoencoder

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Ranked Dropout for Handwritten Digit Recognition 12

Ranked Dropout for Handwritten Digit Recognition

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12th International Conference on Graphics and Image Processing (ICGIP)

作者： Tang, Yue Liang, Zhuonan Shi, Huaze Fu, Peng Sun, Quansen Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781510642782;9781510642775

Overfitting is a common problem in training of neural network with small training sets, which leads to worse performance on the new samples. Dropout has been proved to be an effective method to avoid overfitting, which prevents co-adaptation of features detectors by randomly discarding nodes from hidden layers of network. Inspired by dropout, we proposed a ranked dropout method to remove randomness of standard dropout mask, which discards a part of active nodes and forces the inactive nodes to learn more features to improve generalization ability. We apply the proposed ranked dropout to a stacked autoencoder network and compare it with standard dropout, gaussian dropout, uniform dropout and DropConnect on MNIST dataset. Experimental results of handwritten digit recognition demonstrate that the ranked strategy leads to better classification performance and the proposed ranked dropout can effectively reduce interference of overfitting and improve model's generalization ability.

关键词： Overfitting dropout stacked autoencoder handwritten digit recognition

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An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

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SENSORS 2022年第23期22卷 9480-9480页

作者： Awan, Amna Waheed Usman, Syed Muhammad Khalid, Shehzad Anwar, Aamir Alroobaea, Roobaea Hussain, Saddam Almotiri, Jasem Ullah, Syed Sajid Akram, Muhammad Usman Bahria Univ Dept Comp Engn Islamabad 44000 Pakistan Air Univ Fac Comp & AI Dept Creat Technol Islamabad 44000 Pakistan Univ West London Sch Comp & Engn London W5 5RF England Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia Univ Brunei Darussalam Sch Digital Sci Jalan Tungku Link Gadong BE1410 Brunei Univ Agder UiA Dept Informat & Commun Technol N-4898 Grimstad Norway Villanova Univ Dept Elect & Comp Engn Villanova PA 19085 USA Natl Univ Sci & Technol NUST Coll Eletr & Mech Engn E & ME Islamabad 44000 Pakistan

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection;therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The propose

关键词： emotion charting EEG signals physiological signals ECG signals ICA stacked autoencoder ensemble classifier

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Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling

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ADVANCED ENGINEERING INFORMATICS 2022年 52卷

作者： Wang, Yalin Liu, Diju Liu, Chenliang Yuan, Xiaofeng Wang, Kai Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China

Due to the limitations of sampling conditions and sampling techniques in many real industrial processes, the process data under different sampling conditions subject to different sampling frequencies, which leads to irregular interval sampling characteristics of the entire process data. The dynamic historical data information reflecting the production status under irregular sampling frequency has an important influence on the performance of data feature extraction. However, the existing soft sensor modeling methods based on deep learning do not consider introducing dynamic historical information into the feature extraction process. To combat this issue, a novel attention-based dynamic stacked autoencoder networks (AD-SAE) for soft sensor modeling is proposed in this paper. First, the sliding window technology and attention mechanism based on position coding are introduced to select dynamic historical samples and calculate the contribution of different historical samples to the current sample, respectively. Then, AD-SAE combines obtained historical sample information and current sample information as the input of the network for deep feature extraction and industrial soft sensor modeling. The experimental results on the actual hydrocracking process data set show that the proposed method has better performance than traditional methods.

关键词： Soft sensor Deep learning stacked autoencoder Attention mechanism AD-SAE

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Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2358-2367页

作者： Cen Guo Wenkai Hu Fan Yang Dexian Huang Department of Automation Tsinghua UniversityBeijing 10084China Cornell University NY 14850United States of America University of Alberta EdmontonAB T6G 1H9Canada

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

关键词： Alarm configuration Deep learning Fault detection and diagnosis Incomplete data stacked autoencoder

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An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

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GEOENVIRONMENTAL DISASTERS 2020年第1期7卷 1-16页

作者： Nam, Kounghoon Wang, Fawu Shimane Univ Dept Earth Sci 1060 Nishikawatsu Cho Matsue Shimane 6908504 Japan

BackgroundLandslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme *** prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%).ConclusionsThis study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

关键词： stacked autoencoder Sparse autoencoder Support vector machine Random forest Landslide susceptibility

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Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

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INFORMATION SYSTEMS FRONTIERS 2020年第5期22卷 1067-1083页

作者： Smiti, Salima Soui, Makram Univ Manouba Natl Sch Comp Sci Manouba Tunisia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and stacked autoencoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k- nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm.

关键词： Bankruptcy prediction Deep learning stacked autoencoder Borderline SMOTE Imbalanced dataset

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Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第1期24卷 57-68页

作者： Liu, Ning Chen, Cheng-Bang Kumara, Soundar Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.

关键词： Drugs Machine learning algorithms Machine learning Semisupervised learning Prediction algorithms Feature extraction Support vector machines Semi-supervised learning stacked autoencoder classification drug-drug interactions clinical decision support adverse event reports

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Multifaceted radiomics for distant metastasis prediction in head & neck cancer

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PHYSICS IN MEDICINE AND BIOLOGY 2020年第15期65卷 155009-155009页

作者： Zhou, Zhiguo Wang, Kai Folkert, Michael Liu, Hui Jiang, Steve Sher, David Wang, Jing Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75390 USA Univ Cent Missouri Sch Comp Sci & Math Warrensburg MO USA

Accurately predicting distant metastasis in head & neck cancer has the potential to improve patient survival by allowing early treatment intensification with systemic therapy for high-risk patients. By extracting large amounts of quantitative features and mining them, radiomics has achieved success in predicting treatment outcomes for various diseases. However, there are several challenges associated with conventional radiomic approaches, including: (1) how to optimally combine information extracted from multiple modalities;(2) how to construct models emphasizing different objectives for different clinical applications;and (3) how to utilize and fuse output obtained by multiple classifiers. To overcome these challenges, we propose a unified model termed as multifaceted radiomics (M-radiomics). In M-radiomics, a deep learning with stacked sparse autoencoder is first utilized to fuse features extracted from different modalities into one representation feature set. A multi-objective optimization model is then introduced into M-radiomics where probability-based objective functions are designed to maximize the similarity between the probability output and the true label vector. Finally, M-radiomics employs multiple base classifiers to get a diverse Pareto-optimal model set and then fuses the output probabilities of all the Pareto-optimal models through an evidential reasoning rule fusion (ERRF) strategy in the testing stage to obtain the final output probability. Experimental results show that M-radiomics with the stacked autoencoder outperforms the model without the autoencoder. M-radiomics obtained more accurate results with a better balance between sensitivity and specificity than other single-objective or single-classifier-based models.

关键词： distant metastasis prediction head & neck cancer radiomics stacked autoencoder multi-objective optimization evidential reasoning rule

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