检索结果-内蒙古大学图书馆

Gas path fault detection and isolation for aero-engine based on LSTM-DAE approach under multiple-model architecture

MEASUREMENT 2022年 202卷

作者： Wang, Kun Guo, Yingqing Zhao, Wanli Zhou, Qifan Guo, Pengfei Northwestern Polytech Univ Sch Energy & Power Xian 710129 Peoples R China

Gas path fault diagnosis plays a critical role in the security guarantee and maintenance of aero-engines. In this paper, an approach based on a fusion neural network under multiple-model architecture for gas path fault detection and isolation is proposed. We develop a multi-channel long short-term memory network based on a sliding window to explore temporal and spatial relationships of data and capture the residuals of sensor mea-surements between predicted and observed values. Additionally, denoising autoencoders under a multiple-model architecture are introduced so as to perform fault detection and isolation based on the comparison of recon-structed prediction errors and isolation thresholds. Several simulation results verify that the diagnostic model has excellent robustness and diagnostic ability. The proposed method is compared with other common methods, and the advantages and functions of this method are presented.

关键词： Aero-engine Gas path fault diagnosis Long short-term memory network denoising autoencoder Multiple -model

来源：评论

学校读者我要写书评

暂无评论

Development of an efficient data-driven method to estimate the hydraulic properties of aquifers from groundwater level fluctuation pattern features

引用

JOURNAL OF HYDROLOGY 2020年 590卷 125453-125453页

作者： Jeong, Jiho Jeong, Jina Park, Eungyu Lee, Byung Sun Song, Sung-Ho Han, Weon Shik Chung, Sungwook Kyungpook Natl Univ Dept Geol Daegu South Korea Korea Rural Community Corp Rural Res Inst Ansan South Korea Yonsei Univ Dept Earth Syst Sci Seoul South Korea Korea Basic Sci Inst Div Earth & Environm Sci Choengju South Korea

A method to develop a data-driven model able to estimate hydraulic properties based on groundwater level (GWL) fluctuation patterns is proposed. In particular, a preprocessing method using a denoising autoencoder (DAE) is incorporated into the proposed method to improve the performance of the developed method. DAE is applied to prepare the input variable of the estimation model by extracting informative low-dimensional features from the original high-dimensional GWL data. Before applying the proposed DAE to this study, the reliability of applying the DAE is validated. First, the ability to reduce the noise of GWL data is validated by observing that an average of 71% of the noise is reduced. Additionally, the performances of the extracted principal characteristics of the GWL data is confirmed by reasonable matches in the extracted features to the corresponding hydraulics of the aquifers. In this case, both synthetic data and actual data acquired over South Korea are applied. Based on the validated DAE results, models to estimate two types of hydraulic properties are constructed. The estimation performances of the models are quantitatively validated using the correlation coefficient between the estimated and actual hydraulic properties. Overall, the constructed models for k and alpha/n show an appropriate estimation accuracy with a high correlation coefficient between the actual result and estimate (0.8663 and 0.7207, respectively). Therefore, using the proposed method, the hydraulic properties of an un-informed aquifer can be effectively inferred given GWL data without conducting field experiments (e.g., pumping tests). The proposed method is promising for efficient evaluations of the physical hydraulics of un-informed aquifers and, therefore, can be used as an effective tool to manage groundwater resources.

关键词： Hydraulic property estimation Groundwater level fluctuation patterns Principal feature extraction Non-linear data dimensionality reduction denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Model for Nonlinear Regression with Missing Data UsingQuasilinearKernel

引用

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第12期15卷 1791-1800页

作者： Zhu, Huilin Tian, Yanling Ren, Yanni Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka 8080135 Japan

In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self-organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with topp%winner-take-all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state-of-the-art methods, even on datasets with a large percentage of missing values. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： nonlinear regression missing data affinity propagation algorithm denoising autoencoder support vector regression quasilinear kernel

来源：评论

学校读者我要写书评

暂无评论

A context-aware robust intrusion detection system: a reinforcement learning-based approach

引用

INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2020年第6期19卷 657-678页

作者： Sethi, Kamalakanta Rupesh, E. Sai Kumar, Rahul Bera, Padmalochan Madhav, Y. Venu Indian Inst Technol Bhubaneswar 752050 Odisha India

Detection and prevention of intrusions in enterprise networks and systems is an important, but challenging problem due to extensive growth and usage of networks that are constantly facing novel attacks. An intrusion detection system (IDS) monitors the network traffic and system-level applications to detect malicious activities in the network. However, most of the existing IDSs are incapable of providing higher accuracy and less false positive rate (FPR). Therefore, there is a need for adaptive techniques to detect network intrusions that maintain a balance between accuracy and FPR. In this paper, we present a context-adaptive IDS that uses multiple independent deep reinforcement learning agents distributed across the network for accurate detection and classification of new and complex attacks. We have done extensive experimentation using three benchmark datasets including NSL-KDD, UNSW-NB15 and AWID on our model that shows better accuracy and less FPR compared to the state-of-the-art systems. Further, we analysed the robustness of our model against adversarial attack and observed only a small decrease in accuracy as compared to the existing models. To further improve the robustness of the system, we implemented the concept of denoising autoencoder. Also, we have shown the usability of our system in real-life application with changes in the attack pattern.

关键词： Adversarial attack Context denoising autoencoder FPR IDS Deep reinforcement learning (DRL) agent NSL-KDD AWID UNSW-NB15

来源：评论

学校读者我要写书评

暂无评论

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

引用

Computers, Materials & Continua 2020年第11期65卷 1467-1486页

作者： Kun Zhu Nana Zhang Qing Zhang Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China School of Information Science and Engineering Qufu Normal UniversityRizhao276826China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as ***,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant *** addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning *** address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic *** combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a *** compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software *** experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

关键词： Software defect prediction non-linear manifold learning denoising autoencoder deep neural network loss function deep learning

来源：评论

学校读者我要写书评

暂无评论

Learning and visualizing chronic latent representations using electronic health records

引用

BIODATA MINING 2022年第1期15卷 18-18页

作者： Chushig-Muzo, David Soguero-Ruiz, Cristina Bohoyo, Pablo de Miguel Mora-Jimenez, Inmaculada Rey Juan Carlos Univ Dept Signal Theory & Commun Telemat & Comp Syst Madrid Spain Univ Hosp Fuenlabrada Madrid Spain

Background Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods We propose the use of the denoising autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting. Results To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients. Conclusio

关键词： denoising autoencoder Chronic diseases Diabetes Hypertension Clustering Patient representation Synthetic patient Health status trajectory

来源：评论

学校读者我要写书评

暂无评论

Learning social representations with deep autoencoder for recommender system

引用

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2020年第4期23卷 2259-2279页

作者： Pan, Yiteng He, Fazhi Yu, Haiping Wuhan Univ Sch Comp Sci Wuhan Peoples R China

With the development of online social media, it attracts increasingly attentions to utilize social information for recommender systems. Based on the intuition that users are influenced by their social friends, these methods are capable of addressing the data sparse problem and improving the performance of recommender systems. However, these methods model the influences between each pair of users independently and ignore the interactions among these social influences, i.e., high-level signal of social information. In this paper, we propose a deep autoencoder model to learn social representations for recommender system. This approach aims to learn low- and high- level features from social information based on muti-layers neural networks and matrix factorization technique. Especially, we develop an improved deep autoencoder model, namedSparse Stacked denoising autoencoder(SSDAE), to address the data sparse and imbalance problems for social networks. Moreover, we incorporate these deep representations and matrix factorization model into a uniform framework for recommender system. Our experiments in Epinions and Ciao datasets demonstrate that our method can significantly improve the performance of recommender system, especially for sparse users.

关键词： Social recommendation Deep learning denoising autoencoder Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Removal of multisource noise in airborne electromagnetic data based on deep learning

引用

GEOPHYSICS 2020年第6期85卷 B207-B222页

作者： Wu, Xin Xue, Guoqiang He, Yiming Xue, Junjie Chinese Acad Sci Inst Geol & Geophys Key Lab Mineral Resources Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

关键词： airborne electromagnetic multisource noise deep learning denoising autoencoder neural network

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors

引用

SENSORS 2020年第7期20卷 1895-1895页

作者： Sigcha, Luis Costa, Nelson Pavon, Ignacio Costa, Susana Arezes, Pedro Manuel Lopez, Juan De Arcas, Guillermo Univ Politecn Madrid Grp Invest Instrumentac & Acust Aplicada I2A2 ETSI Ind Campus Sur UPMCtra ValenciaKm 7 Madrid 28031 Spain Univ Minho Sch Engn ALGORITMI Res Ctr P-4800058 Guimaraes Portugal

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

关键词： IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation

来源：评论

学校读者我要写书评

暂无评论

A synchronized heterogeneous autoencoder with feature-level and label-level knowledge distillation for the recommendation

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104494-104494页

作者： Pan, Yiteng He, Fazhi Yan, Xiaohu Li, Haoran DongGuan Univ Technol Sch Cyberspace Secur Dongguan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Shenzhen Polytech Sch Artificial Intelligence Shenzhen Peoples R China Xidian Univ ISN State Key Lab Xian Peoples R China

Motivated by the success of deep learning techniques, there are numerous deep learning models developed for recommender systems. User-oriented autoencoder (UAE) and item-oriented autoencoder (IAE) are two typical approaches developed for the recommendation by learning features from items or users respectively. Existing works demonstrate that IAE model outperforms UAE model for rating prediction tasks, while UAE model outperforms IAE model for top-N recommendation task. This fact motivates us to develop a new synchronized heterogeneous autoencoder (SHAE) for top-N recommendation by considering both features learned by IAE and UAE models. Especially, we develop two novel heterogeneous knowledge distillation methods in feature level and label-level to build relations between IAE and UAE models. Compared with matrix factorization approaches, our methods are more efficient with acceptable computational complexity for the recommendation. We conduct comprehensive experiments on public datasets to compare with several state-of-the-art approaches. Experimental results demonstrate that the proposed method significantly outperforms other comparisons for top-N recommendation tasks.

关键词： Deep learning Recommender systems denoising autoencoder Knowledge distillation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：