检索结果-内蒙古大学图书馆

A deep learning just-in-time modeling approach for soft sensor based on variational autoencoder

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年第0期197卷 103922-000页

作者： Guo, Fan Xie, Ruimin Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

This paper presents a variational autoencoder-based just-in-time (JIT) learning framework for soft sensor modeling. Just-in-Time learning is often applied for soft sensor modeling in industrial processes. However, traditional just-in-time learning methods measure the similarity based on Euclidean distance, which has not taken into consideration the uncertainty in variables. To improve traditional just-in-time learning methods, in the proposed approach, the variational autoencoder is employed to extract features from input data set containing noise. Each feature variable is expressed by a Gaussian distribution. Then, by using the distribution of each feature variable, Kullback-Leibler divergence is employed to evaluate the similarity between the historical samples and a query sample. Furthermore, historical samples that are most similar to the query samples based on the values of the Kullback-Leibler divergence are selected for modeling. Finally, Gaussian process regression as a nonlinear regression model, is used to model the relationship between the selected input samples and the corresponding output samples, and then make a prediction. A numerical example as well as application on a practical debutanizer industrial process demonstrates the effectiveness of the proposed method.

关键词： variational autoencoder Just-in-time learning Kullback Leibler divergence Gaussian process regression Soft sensor

来源：评论

学校读者我要写书评

暂无评论

Stable local interpretable model-agnostic explanations based on a variational autoencoder

引用

APPLIED INTELLIGENCE 2023年第23期53卷 28226-28240页

作者： Xiang, Xu Yu, Hong Wang, Ye Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing Peoples R China

For humans to trust in artificial intelligence (AI) systems, it is essential for machine learning (ML) models to be interpretable to users. For example, the judicial process requires that AI conclusions must be rigorous and absolutely interpretable. In this paper, we propose a novel approach, VAE-SLIME, for providing stable local interpretable model-agnostic explanations (SLIME) based on a variational autoencoder (VAE). LIME is a technique that explains the predictions of any classifier in an interpretable and faithful manner. Despite the great success of LIME, the most popular method in this category, it has several disadvantages due to its random perturbation-based sampling method. The VAE-SLIME proposed in this paper is specifically designed to address the lack of stability and local fidelity exhibited by LIME for tabular data. VAE-SLIME first employs fixed noise to replace the random Gaussian noise used by the reparameterization trick of the VAE. Then, it uses this new VAE model instead of random perturbation method to generate stable samples. By considering the sequential relationship and flipping of features, a novel explanation stability evaluation metric, the feature sequence stability index (FSSI), is introduced to accurately evaluate the stability of explanations. In a comparison with 6 state-of-the-art approaches on 7 commonly used tabular datasets, the experimental results show beyond doubt that the explanations produced by our approach are most stable, and its local fidelity is 65.17% higher than that of other approaches on average.

关键词： LIME Stability Local fidelity variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Collaborative Additional variational autoencoder for Top-N Recommender Systems

引用

IEEE ACCESS 2019年 7卷 5707-5713页

作者： He, Ming Meng, Qian Zhang, Shaozong Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance. Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems. However, they seem to fail to take into consideration the fact that the sparsity of single side information. To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item. The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner. With the help of graphical models, it can extract the implicit relationships between users and items effectively. A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved.

关键词： Collaborative filtering deep learning variational autoencoder side information

来源：评论

学校读者我要写书评

暂无评论

Innovative variational autoencoder for an End-to-End Communication System

引用

IEEE ACCESS 2023年 11卷 86834-86847页

作者： Alawad, Mohamad A. Hamdan, Mutasem Q. Hamdi, Khairi A. Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England Univ Surrey Inst Commun Syst ICS 5GIC Guildford GU2 7XH England Univ Surrey Inst Commun Syst ICS 6GIC Guildford GU2 7XH England

Powered by deep learning (DL), autoencoders (AE) end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in traditional communication systems and have achieved great success. In this paper, a new probabilistic model, based on the variational autoencoders (VAE), is proposed for short-packet wireless communication systems. Using this new approach, the information messages are represented by the so-called packet hot vectors (PHV), which are inferred by the VAE latent random variables (LRVs). Then only LRVs' parameters can be transmitted through the physical wireless channel. This results in a significant improvement in spectral efficiency when compared with the pure AE approach, where longer hot vectors are to be transmitted. Specific VAE models have been developed for both binary (BPSK) as well as Quadrature phase shift keying (QPSK) systems. Simulation and numerical results are given to demonstrate the performance of the proposed method in different real scenarios, including Rayleigh and Rician fading channels with Shadowing and Doppler effects. Our simulation and numerical results show that the new proposed VAE with a DL classifier can provide an improved symbol error rate (SER) performance than both the baseline AE and the classical Hamming code with hard decision decoding. Furthermore, as far as the spectral efficiency of the proposed method is concerned, we show that using two channels in the proposed VAE performance exceeds the 7 channels' baseline AE.

关键词： variational autoencoder machine learning auto-encoder Hamming code latent random variable probabilistic models wireless communications binary phase shift keying quadrature phase shift keying

来源：评论

学校读者我要写书评

暂无评论

A variational autoencoder (VAE)-based Deep Learning Anomaly Detection Model for Industrial Products with Dynamic Weights Assigned to Loss Function

引用

SENSORS AND MATERIALS 2023年第7期35卷 2241-2264页

作者： Nakata, Shunta Kasahara, Takehiro Nambo, Hidetaka Kanazawa Univ Grad Sch Nat Sci & Technol Div Elect & Comp Sci Kanazawa Ishikawa 9201192 Japan Ind Res Inst Ishikawa Kanazawa Ishikawa 928203 Japan

In the industrial field, deep-learning-based image anomaly detections are attracting attention because of some of their advantages. The deep-learning-based models can overcome the shortcomings of traditional methods, such as human eye detection and rule-based machine detection. When using deep learning, which has many advantages, one of the limitations is that anomalous products are difficult to obtain. Since most industrial products do not have defects, unsupervised learning detection models are strongly required. We propose a new model based on the variational autoencoder ( VAE), which is a generative model applicable to detection by unsupervised learning. VAE is a model for optimizing parameters or latency based on a loss function that is the sum of several terms, and in our proposed method, original weights are given to these terms. In addition, our model dynamically and adaptively explores a ratio of weights. We have developed a dynamic weighted VAE adapted to area under the receiver operating characteristic curve (AUROC, AUC) using validation data. We have already reported the efficiency of the AUC-adapted VAE;however, this method is not unsupervised learning, and a method that does not use validation data was desired. In this paper, we discuss the previous method in more detail and describe the new method, which is fully unsupervised learning, by conducting additional experiments. The results of several experiments show that the proposed method is potentially effective for some actual industrial product image datasets while maintaining unsupervised learning.

关键词： anomaly detection industrial product variational autoencoder deep learning generative model loss function unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Text feature extraction based on stacked variational autoencoder

引用

MICROPROCESSORS AND MICROSYSTEMS 2020年 76卷 103063-103063页

作者： Che, Lei Yang, Xiaoping Wang, Liang Renmin Univ China Sch Informat Beijing 100872 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat Management Beijing 100192 Peoples R China

This paper presents a text feature extraction model based on stacked variational autoencoder (SVAE). A noise reduction mechanism is designed for variational autoencoder in input layer of text feature extraction to reduce noise interference and improve robustness and feature discrimination of the model. Three kinds of deep SVAE network architectures are constructed to improve ability of representing learning to mine feature intension in depth. Experiments are carried out in several aspects, including comparative analysis of text feature extraction model, sparse performance, parameter selection and stacking. Results show that text feature extraction model of SVAE has good performance and effect. The highest accuracy of SVAE models of Fudan and Reuters datasets is 13.50% and 8.96% higher than that of PCA, respectively. (C) 2020 Elsevier B.V. All rights reserved.

关键词： variational autoencoder Text feature extraction Deep stack Noise reduction

来源：评论

学校读者我要写书评

暂无评论

Feasibility of a Specklegram-Based Quasi-Distributed Temperature Sensor With Principal Component Analysis and variational autoencoder

引用

IEEE SENSORS JOURNAL 2024年第14期24卷 22410-22418页

作者： Yue, Shichao Lu, Huizhen Li, Boyi Che, Zifan Southeast Univ Dept Mech Engn Nanjing 211100 Jiangsu Peoples R China KTH Royal Inst Technol Sch Elect Engn & Comp Sci Dept Intelligent Syst SE-10044 Stockholm Sweden

Specklegram-based optical fiber sensors have gained attention for their advantages of sensitivity, low cost, and intelligent sensing ability in fields such as force, small deflection, single-point temperature sensing, and so on. To enhance specklegram-based distributive temperature sensing through a multimode optical fiber (MMF), this work proposes a quasi-distributed approach involving a triple-color illumination with light emitting diodes (LEDs) and a hybrid model that combines principal component analysis and variational autoencoder (PCAVAE). The proposed MMF optical sensor demonstrates strong performance in sensing the distributive temperature configurations of four heaters, showcasing its capability in detecting quasi-distributed temperature information in the range of 30 degrees C-60 degrees C, achieving a spatial resolution of 3 cm and a temperature resolution of 1 degrees C with a mean absolute error (MAE) of 1.85 degrees C and a root mean square error (RMSE) of 2.63,degrees C respectively. The proposed sensor opens a gateway for exploiting multiple sites sensing capability with a single optical fiber specklegram and exhibits application potentials for low-cost quasi-distributed optical temperature detection.

关键词： Temperature sensors Sensors Adaptive optics Optical fiber sensors Optical variables control Optical refraction Optical fibers Distributive temperature sensor optical fiber variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Machine Learning With variational autoencoder for Imbalanced Datasets in Intrusion Detection

引用

IEEE ACCESS 2022年 10卷 15247-15260页

作者： Lin, Ying-Dar Liu, Zi-Qiang Hwang, Ren-Hung Van-Linh Nguyen Lin, Po-Ching Lai, Yuan-Cheng Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 621 Taiwan Univ Informat & Commun Technol Dept Informat Technol Thai Nguyen 25000 Vietnam Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei 106 Taiwan

As a result of the explosion of security attacks and the complexity of modern networks, machine learning (ML) has recently become the favored approach for intrusion detection systems (IDS). However, the ML approach usually faces three challenges: massive attack variants, imbalanced data issues, and appropriate data segmentation. Improper handling of the issues will significantly degrade ML performance, e.g., resulting in high false-negative and low recall rates. Despite many efforts have done in the literature, detecting security attacks in a complicated network environment with imperfect data collection is still an open issue. This work proposes a machine learning framework with a combination of a variational autoencoder and multilayer perceptron model to deal with imbalanced datasets and detect the explosion of attack variants on the Internet. The detection engine also includes an efficient range-based sequential search algorithm to address the segmentation challenge in data pre-processing from multiple sources (network packets, system/statistic logs) effectively. Our work is the first attempt to demonstrate the effect of using an appropriate combination of ML models for boosting IDS detection capability in a heterogeneous environment, where data collection imperfection is common. Experimental results on a public system log dataset (e.g., HDFS) show that our method gains approximately as much as 97% on F1 score and 98% on recall rate, a promising result compared to the same measurement of other solutions. Even better, we found that the proposed treatment of imbalanced datasets can improve up to 35% on the F1 score and 27% on recall rate. The testing results also indicate that our model can detect new attack variants.

关键词： Training Data models Intrusion detection Machine learning Soft sensors Predictive models Explosions Imbalanced dataset machine learning variational autoencoder intrusion detection

来源：评论

学校读者我要写书评

暂无评论

RUL Prediction Using a Fusion of Attention-Based Convolutional variational autoencoder and Ensemble Learning Classifier

引用

IEEE TRANSACTIONS ON RELIABILITY 2023年第1期72卷 106-124页

作者： Remadna, Ikram Terrissa, Labib Sadek Al Masry, Zeina Zerhouni, Noureddine Univ Biskra Dept Comp Sci LINFI Lab Biskra 07000 Algeria Univ Bourgogne Franche Comte CNRS FEMTO ST Inst ENSMM F-25044 Besancon France

Predicting the remaining useful life (RUL) is a critical step before the decision-making process and developing maintenance strategies. As a result, it is frequently impacted by uncertainty in a practical context and may cause issues. This article proposes a new hybrid deep architecture that predicts when an in-service machine will fail to overcome the latter problem, allowing for an improved data analysis and dimensionality reduction capability providing better spatial distributions of features and increasing interpretability. A deep convolutional variational autoencoder with an attention mechanism (ACVAE) has been developed and tested using the aero-engine C-MAPSS dataset. We defined two adapted threshold settings (alpha 1, alpha 2) by analyzing the spatial distribution and minimizing the overlapping area between the degradation classes. To reduce the conflict zone, we used the soft voting classifier. The performance of our visual explainable deep learning model has reached a higher level of accuracy compared with previous existing models.

关键词： Feature extraction Degradation Computer architecture Estimation Decoding Prognostics and health management Visualization Attention mechanism data visualization deep learning prognostics and health management variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Emotion-Regularized Conditional variational autoencoder for Emotional Response Generation

引用

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2023年第1期14卷 842-848页

作者： Ruan, Yu-Ping Ling, Zhen-Hua Natl Univ Def Technol Hefei 230031 Peoples R China Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei 230027 Peoples R China

This article presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses. In conventional CVAE-based emotional response generation, emotion labels are simply used as additional conditions in prior, posterior and decoder networks. Considering that emotion styles are naturally entangled with semantic contents in the language space, the Emo-CVAE model utilizes emotion labels to regularize the CVAE latent space by introducing an extra emotion prediction network. In the training stage, the estimated latent variables are required to predict the emotion labels and token sequences of the input responses simultaneously. Experimental results show that our Emo-CVAE model can learn a more informative and structured latent space than a conventional CVAE model and output responses with better content and emotion performance than baseline CVAE and sequence-to-sequence (Seq2Seq) models.

关键词： Training Emotional responses Testing Decoding Earth Observing System Bit error rate Semantics Emotional response generation latent variables variational autoencoder

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：