检索结果-内蒙古大学图书馆

International Conference on Smart Innovations in Communications and Computational Sciences (ICSICCS)

作者： Wang, Xiumei Mu, Shaomin Shi, Aiju Lin, Zhongqi Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Shandong Peoples R China Shandong Agr Univ Coll Chem & Mat Sci Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9789811089718;9789811089701

Deep learning has attracted much attention because of its ability to extract complex features automatically. Unsupervised pre-training plays an important role in the process of deep learning, but the monitoring information provided by the sample of labeling is still very important for feature extraction. When the regression forecasting problem with a small amount of data is processed, the advantage of unsupervised learning is not obvious. In this paper, the pre-training phase of the stacked denoising autoencoder was changed from unsupervised learning to supervised learning, which can improve the accuracy of the small sample prediction problem. Through experiments on UCI regression datasets, the results show that the improved stacked denoising autoencoder is better than the traditional stacked denoising autoencoder.

关键词： Deep learning stacked denoising autoencoder Supervised learning Regression forecast

来源：评论

学校读者我要写书评

暂无评论

Fault Location in VSC-HVDC Using stacked denoising autoencoder 3

Fault Location in VSC-HVDC Using Stacked Denoising Autoencod...

引用

3rd IEEE International Electrical and Energy Conference (CIEEC)

作者： Luo, Guomin Hei, Jiaxin Liu, Yanying Li, Meng He, Jinghan Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728116754

This paper proposed an intelligent algorithm based approach for fault location in a high voltage direct current (HVDC) transmission system. To obtain post-fault signals, the point-to-point HVDC transmission lines, including overhead lines and cables, are modeled on PSCAD/EMTDC. The data set is split into two parts used for training and testing, separately. The proposed method uses stacked denoising autoencoder (SDAE), which takes the raw training data as the input of network and can directly obtain fault locations. SDAE with unsupervised learning is utilized to extract representative features automatically from raw data in pre-training. Then labeled data is applied to network for fine-tuning in a supervised manner. The testing data is used for the evaluation of the proposed method. The simulation results indicate that the SDAE based method performs well in fault location and has robustness against noises, ground resistances, and system parameters.

关键词： fault location HVDC stacked denoising autoencoder robustness

来源：评论

学校读者我要写书评

暂无评论

Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115359-115359页

作者： Dai, Tao Yan, Wenjun Zhang, Kaiqi Qiu, Chen Zhao, Xiangmo Pan, Shirui Changan Univ Sch Econ & Management Xian 710064 Shaanxi Peoples R China Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China Walmart Inc Sams Club Innovat Ctr Dallas TX 75202 USA Monash Univ Fac Informat Technol Melbourne Vic Australia

Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.

关键词： Global citation recommendation stacked denoising autoencoder Topic model Machine learning Deep learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine

引用

MEASUREMENT 2021年 167卷 108153-108153页

作者： Ou, Jiayu Li, Hongkun Huang, Gangjin Yang, Guowei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

Impeller is a critical component of much large equipment, such as hydropower, nuclear pump, and so on. Its processing quality seriously affects the operation states and service life span of the equipment, while the milling cutter wear determines the processing accuracy of the impeller. Therefore, it is of great important to investigate an intelligent recognition method of tool wear state during the processing. In this research, a new method named stacked denoising autoencoder (SDAE) with online sequential extreme learning machine (OS-ELM) is put forward for intelligent recognition of tool wear states. Firstly, three current signals of the spindle of CNC machine tool are collected during the cutting process and the effective values are synthesized. Then, a new SDAE neural network is trained to acquire the low dimensional features using raw current signals. Finally, OS-ELM is used to realize recognition and classification of the milling cutter, and compared the accuracy with other methods. The method proposed in this paper was verified by both the laboratory signals and the actual engineering signals. The results for spindle current signals show that the developed model has achieved effective performance on tool wear recognition. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Spindle current signals stacked denoising autoencoder Online sequential extreme learning machine Tool wear states recognition

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Image Classification Based on stacked denoising autoencoder

引用

REMOTE SENSING 2018年第1期10卷 16-16页

作者： Liang, Peng Shi, Wenzhong Zhang, Xiaokang Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Hong Kong Peoples R China

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on stacked denoising autoencoder. First, the deep network model is built through the stacked layers of denoising autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.

关键词： deep learning stacked denoising autoencoder Back Propagation neural network land cover classification

来源：评论

学校读者我要写书评

暂无评论

Auxiliary stacked denoising autoencoder based Collaborative Filtering Recommendation

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2020年第6期14卷 2310-2332页

作者： Mu, Ruihui Zeng, Xiaoqin Hohai Univ Coll Comp & Informat Nanjing 210098 Peoples R China Xinxiang Univ Coll Comp & Informat Engn Xinxiang 453000 Henan Peoples R China

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

关键词： Auxiliary information collaborative filtering data sparsity recommender system stacked denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2019年第9期30卷 095003-095003页

作者： Yu, Jun Xu, Yonggang Liu, Ke Harbin Univ Sci & Technol Sch Automat Harbin 150080 Heilongjiang Peoples R China Beijing Univ Technol Key Lab Adv Mfg Technol Beijing 100124 Peoples R China

Owing to high transmission ratio and compact structure, planetary gearboxes are widely used in industrial applications. However, due to running under noisy environment and time-varying rotational speed conditions, the acquired vibration signals are nonstationary, which seriously degrade the performance of fault diagnosis methods of planetary gears. To address this problem, a fault diagnosis method of planetary gears using a stacked denoising autoencoder (SDAE) and a gated recurrent unit neural network (GRUNN) is proposed in this paper. First, a hybrid model based on SDAE and GRUNN is developed to remove noise components from input data and process pre- and post-correlation time-series data. The training samples for planetary gear fault diagnosis are regarded as the input data of the developed hybrid model to automatically extract robust fault features. Then, the training process of the developed hybrid model is presented. The Adam optimization algorithm is utilized to optimize the parameters, and the dropout technique is employed to prevent from overfitting. Finally, a softmax classifier is employed to identify planetary gear states in test samples. The effectiveness of the proposed method is validated through a fault diagnosis experiment of planetary gears. The experimental results show that the proposed method possesses strong anti-noise ability and adaptability to time-varying rotational speed.

关键词： planetary gear fault diagnosis stacked denoising autoencoder gated recurrent unit neural network noisy environment time-varying rotational speed

来源：评论

学校读者我要写书评

暂无评论

Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder

引用

CHEMICAL ENGINEERING SCIENCE 2022年 253卷 117556-117556页

作者： Gao, Huihui Wei, Chen Huang, Wenjie Gao, Xuejin Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

In modern plants, industrial processes generally operate under different modes, and reliable monitoring for such processes is highly important. One of the key challenges is how to accurately identify the various modes including steady modes and transitions modes. In this paper, a novel monitoring scheme based on hierarchical mode identification strategy and stacked denoising autoencoder (HMI-SDAE) is proposed for multimode processes. First, a novel mode division strategy called HMI is presented. In HMI, the Gaussian mixture model (GMM) is adopted to realize the preliminary identification of various modes by extracting the global distribution features of the variables. In this way, the whole multimode process is divided into multiple steady modes. An improved density peaks clustering algorithm based on local density relation search (LDRSDPC) is proposed to achieve the transition mode identification by fully utilizing the local distribution features of the process variables involved in any two adjacent steady modes and transition mode between them. A decision criterion combined with local density relation is constructed to automatically determine the clustering center. In this hierarchical way, multiple steady modes and transition modes are divided automatically and accurately. Secondly, the deep nonlinear features embedded in process variables are mined by SDAE, and the robust monitoring model is established for each steady mode. A monitoring statistic is constructed using the reconstruction error for detecting faults. The effectiveness and feasibility of the proposed HMI-SDAE monitoring scheme are illustrated with a numerical example and Tennessee Eastman (TE) process.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Mode identification Gaussian mixture model Density peaks clustering stacked denoising autoencoder Multimode process monitoring Industrial process

来源：评论

学校读者我要写书评

暂无评论

Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with temporal information

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年

作者： Zhang, Xujie Wu, Ping He, Jiajun Liu, Yichao Wang, Lin Gao, Jinfeng Zhejiang Sci Tech Univ Sch Mech Engn & Automat Hangzhou 310018 Zhejiang Peoples R China Delft Univ Technol Fac Mech Maritime & Mat Engn Delft Netherlands Zhejiang Windey Co Ltd Key Lab Wind Power Technol Zhejiang Prov Hangzhou Zhejiang Peoples R China

Currently, the offshore wind turbine has become a hot research area in the wind energy industry. Among different offshore wind turbines, floating offshore wind turbine (FOWT) can harvest abundant wind energy in deepwater areas. However, the harsh working environment will dramatically increase the maintenance cost and downtime of FOWTs. Wind turbine fault diagnosis is being regarded as an indispensable system for maintenance issues. Owing to the complexity of FOWT, it imposes an enormous challenge for effective fault diagnosis. This paper develops a novel FOWT fault diagnosis method based on a stacked denoising autoencoder (SDAE). First, a sliding window technique is adopted for time-series data to preserve temporal information. Then, SDAE is employed to extract the features from high-dimensional data. Based on the extracted features from SDAE, a classifier using multilayer perceptrons (MLP) is developed to determine the health status of the FOWT. To verify the performance of the proposed method, a FOWT simulation benchmark based on the National Renewable Energy Laboratory (NREL) FAST simulator is employed. Results show the superior performance of the proposed method by comparison with other relevant methods.

关键词： Floating offshore wind turbines fault detection and diagnosis stacked denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

A Robust Wi-Fi Fingerprint Positioning Algorithm Using stacked denoising autoencoder and Multi-Layer Perceptron

引用

REMOTE SENSING 2019年第11期11卷 1293-1293页

作者： Wang, Rongrong Li, Zhaohui Luo, Haiyong Zhao, Fang Shao, Wenhua Wang, Qu Beijing Univ Posts & Telecommun Schoole Software Engn Beijing 100876 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (stacked denoising autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.

关键词： Indoor positioning feature extraction regression positioning stacked denoising autoencoder multi-layer perceptron

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：