检索结果-内蒙古大学图书馆

3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Liu, Zeyu Zhang, Bingchen Li, Qiang Zhao, Feng Liu, Di Hou, Kai Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China State Grid Information and Telecommunication Group Co. Ltd Beijing102211 China

ISBN: (纸本)9798350318180

A data driven approach for reliability assessment of composite power systems have been proposed in our paper. A Multi-Layer Extreme Learning Machine (MELM) is trained to graph the relations among system states versus minimum load curtailment. This approach is a computationally efficient alternative to the traditional optimal power flow (OPF) optimizations. Moreover, the pretraining process using denoising autoencoder (DAE) is utilized to improve the feature extraction and predictive performance. By conducting case studies on the RTS-79 system, the proposed methodology was evaluated. Results show that the proposed method is efficient and effective in enhancing the assessment of reliability of composite power systems. © 2023 IEEE.

关键词： composite systems data-driven denoising autoencoder multilayer exterme learning machine reliability assessment

来源：评论

学校读者我要写书评

暂无评论

Prediction model of bearing fault remaining useful life based on weighted variable loss degradation characteristics

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096122-096122页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

In the prediction of bearing fault remaining useful life (RUL), the identification and feature extraction of early bearing faults are very important. In order to improve the accuracy of early fault RUL prediction, a bearing fault RUL prediction model based on weighted variable loss degradation characteristics is proposed. The model is composed of a stack denoising autoencoder (SDAE) module guided by variable loss, a signal-to-noise feature adaptive weighting module and a long-short term memory (LSTM) degradation characteristics extraction and regression output module. Firstly, this model improves the ability of SDAE model to extract weak fault features by ascending dimension learning and variable loss function. Then, an adaptive weighting matrix is generated according to the test signal to modulate the weight vector of SDAE. Finally, the hidden layer features of SDAE were input into LSTM model to extract the bearing state degradation features and realize the RUL prediction of bearing faults. The experimental results show that the proposed model can accurately predict the RUL of the test data in the early fault stage and the fault development stage. The proposed model can give early fault warning to the bearing state.

关键词： remaining useful life prediction early failure denoising autoencoder long-short term memory bearing failure

来源：评论

学校读者我要写书评

暂无评论

Damage detection for high-speed railway standard box girders based on time-frequency characteristics of train-induced strain

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 218卷

作者： Wang, Xin Li, Shunlong Yang, Yaowen Zhuo, Yi Yang, Lei North Univ China Sch Environm & Safety Engn Taiyuan 030051 Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150090 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore China Railway Design Corp Tianjin 300142 Peoples R China

This paper presents a novel approach for detecting damage in high-speed railway standard box girders by leveraging the time -frequency characteristics of train -induced strain. Based on the mechanical and deformation characteristics of high-speed railway box girders, the method involves segmenting the box girder into distinct components based on plate element analysis to identify their damage separately. It utilizes coefficients derived from wavelet transforms as indicators sensitive to damage, and employs the particle swarm optimization algorithm (PSO) to determine the ideal frequency intervals in terms of number and position. Within these optimal frequency intervals, the sum of wavelet coefficients is only sensitive to damage features while remaining unaffected by environmental and operational variations. A convolutional denoising autoencoder is employed to remove noise from the strain time -frequency image, improving the robustness of the proposed method. By utilizing the Gaussian inverse cumulative distribution function to estimate confidence boundary (CB) for damage features (DF), outliers are detected, allowing for precise damage localization and quantification. A case study for the high-speed railway box girder show that the proposed method can effectively identify, locate and quantify damage across all components in the critical key section by using data acquired from just 4 strain sensors. This method holds promise for facilitating the development of effective maintenance strategies for high-speed railway box girders.

关键词： Damage detection Train -induced response Time -frequency characteristics denoising autoencoder High-speed railway box girder

来源：评论

学校读者我要写书评

暂无评论

A novel health indicator by dominant invariant subspace on Grassmann manifold for state of health assessment of lithium-ion battery

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 130卷

作者： Zhang, Ying Li, Yan-Fu Zhang, Ming Wang, Huan Univ Sci & Technol Beijing Sch Mech Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Inst Qual & Reliabil Beijing 100084 Peoples R China Aston Univ Coll Engn & Phys Sci Birmingham B4 7ET England

The precise estimation of the state of health (SoH) in Lithium-ion batteries (LiBs) relies heavily on a reliable health indicator (HI). Conventional indicators are often constructed by directly concatenating features from multiple sources. It overlooks significant non-linear and correlative information inherent in raw signals. To address this limitation, this paper introduces an innovative approach for SoH estimation in LiBs. Deep features extracted from signals of various sensors are obtained using denoising auto-encoders (DAEs). Then the dominant invariant subspaces (DIS) are calculated through the non-linear transformation of multi-source features on the Grassmann manifold. It can preserve essential and robust characteristics. The health indicator quantifies the geodesic distance of DIS using a projection metric. It provides a more comprehensive inclusion of nonlinear and correlation information. Consequently, this indicator offers heightened precision in discerning differences in health states. Validation of the proposed method is conducted using the NASA dataset. The result demonstrates its effectiveness on the SoH assessment and superiority to the state-of-the-art method.

关键词： State of health Lithium-ion battery Grassmann manifold Dominant invariant subspace Health indicator denoising autoencoder Multi-source information fusion

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Integrated Framework for stock price movement prediction

引用

APPLIED SOFT COMPUTING 2023年 133卷

作者： Zhao, Yanli Yang, Guang Wuhan Business Univ Sch Business Adm Wuhan Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China

Stock market prediction is a very important problem in the economics field. With the development of machine learning, more and more algorithms are applied in the stock market to predict the stock price movement. However, stock market prediction is regarded as a challenging task for the noise and volatility of stock market data. Therefore, in this paper, a novel hybrid model SA-DLSTM is proposed to predict stock market and simulation trading by combine a emotion enhanced convolutional neural network (ECNN), the denoising autoencoder (DAE) models, and long short-term memory model (LSTM). Firstly, user-generated comments on Internet were used as a complement to stock market data, and ECNN was applied to extract the sentiment representation. Secondly, we extract the key features of stock market data by DAE, which can improve the prediction accuracy. Thirdly, we take the timeliness of emotion on stock market into consideration and construct more reliable and realistic sentiment indexes. Finally, the key features of stock data and sentiment indexes are fed into LSTM to make stock market prediction. Experiment results show that the prediction accuracy of SA-DLSTM are superior to other compared models. Meanwhile, SA-DLSTM has a good performance both in return and risk. It can help investors make wise decisions. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Stock market prediction Sentiment analysis Long short-term memory denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

AutoMF: A hybrid matrix factorization model with deep learning to select anti-viral drugs for Covid-19

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2023年 74卷

作者： Sajadi, Seyedeh Zahra Sadjadi, Seyed Mojtaba Chahooki, Mohammad Ali Zare Yazd Univ Dept Comp Engn Yazd Iran Shahrood Univ Technol Fac Comp Engn Shahrood Iran

In the wake of the novel Covid-19 disease pandemic, the global economy has been affected and health crises are widespread. The disease is still incurable, and no effective treatment exists for it. During the Covid-19 crisis, drug repurposing has proven to be an effective treatment strategy. Drug repositioning is an approach to finding effective drugs for treating new diseases by discovering new efficacy of existing drugs. Studies on drug-virus association can reveal new efficacy. The antiviral drug repositioning problem is defined here as a matrix completion problem in which antiviral drugs go down the rows, while viruses go down the columns. We propose a hybrid model called AutoMF that identifies new drug-virus association. This new hybrid model aims to develop a matrix factorization model with deep learning and use it to predict drug-virus associations for repositioning drugs. In our model, a heterogeneous drug-virus network is used, which combines drug-virus associations, drugdrug similarity matrix, and virus-virus similarity matrix. Matrix factorization can extract latent factors from the drug-virus associations. The sparse nature of the associations may prevent the latent factors from being very effective. To solve this problem, deep learning is used to learn effective latent representations from similarity matrices to incorporate with MF to enhance their latent factor priors. Our model outperforms several recent approaches in comparison to benchmarking tests performed on the DVA dataset. In our approach, we identify antiviral drugs currently being tested in clinical trials or those used currently to treat patients.

关键词： Drug-virus association Deep learning Hybrid model Latent feature denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

Noise reduction method for wind turbine gearbox vibration signals based on CVMD-DRDSAE

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第11期35卷 116146-116146页

作者： Yao, Jinbao Yue, Bohao Wang, Yizhu Li, Xiang Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

Wind turbine gearbox fault feature extraction is difficult due to strong background noise. To address this issue, a noise reduction method combining comprehensive learning particle swarm optimization-variational mode decomposition (CLPSO-VMD) and deep residual denoising self-attention autoencoder (DRDSAE) is proposed. Firstly, the proposed CLPSO-VMD algorithm is used to decompose the noisy wind turbine gearbox vibration signals. Subsequently, the intrinsic mode functions highly correlated with the original signals are selected through the Spearman correlation coefficient and utilized for signal reconstruction, thereby filtering out high-frequency noise outside the fault frequency band in the frequency domain characterization. Secondly, the improved DRDSAE is utilized to learn the latent representations of data in the first-level denoised signal, further reducing the residual noise within the fault frequency band while retaining important signal features. Finally, the envelope spectrum highlights the weak feature of the wind turbine gearbox vibration signal. Experimental results demonstrate the effectiveness of the proposed method in denoising wind turbine gearbox vibration signals under strong noise.

关键词： wind turbine gearbox signal denoising variational mode decomposition self-attention mechanism denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

denoising transthoracic echocardiographic images in regional wall motion abnormality using deep learning techniques

引用

SOFT COMPUTING 2023年

作者： Beevi, A. Shamla Ratheesha, S. Kalady, Saidalavi Chackola, Jenu James Natl Inst Technol Calicut Kozhikode Kerala India SOTI India Pvt Ltd Kochi Kerala India Aster MIMS Hosp Kottakkal Malappuram Kerala India

Image analysis and classification perform well in pre-processed noise-free images than in corrupted images. Synthetic aperture radar (SAR) images, Ultrasound (US) medical images, etc. exhibit speckle noise, which has a multiplicative and granular behavior. In the existing techniques, the autoencoders are used to implement a deep learning-based denoising method specifically for US images. The traditional image denoising techniques as well as deep learning techniques for image denoising. In this paper, we have proposed a deep learning-based model called, Convolutional-based improved despeckling autoencoder (CIDAE) for denoising transthoracic echocardiographic images. The dataset for the network has been collected from patients having Regional Wall Motion Abnormality (RWMA). There were 294 subjects with routine transthoracic examinations, consisting of 151 RWMA and 143 normal hearts (55.7 percent female, ages 20-75 years). The potential of the proposed DL algorithms was evaluated visually and quantitatively using the Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR), and Mean Squared Error (MSE). Our results demonstrate the significance of the proposed CIDAE for denoising echo images of patients with RWMA and structurally normal hearts with a promising p-value < 0.0001.

关键词： Deep learning denoising autoencoder Speckle noise Ultrasound images

来源：评论

学校读者我要写书评

暂无评论

Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 96卷

作者： Berghout, Tarek Mouss, Leila-Hayet Kadri, Ouahab Saidi, Lotfi Benbouzid, Mohamed Univ Batna 2 Lab Automat & Mfg Engn Batna 05000 Algeria Univ Batna 2 Dept Comp Sci Batna 05078 Algeria Univ Tunis SIME Lab LR 13E503 Tunis Tunisia Univ Brest UMR CNRS 6027 Inst Rech Dupuy Lome F-29238 Brest France Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven prognostics. The key issue is how to design a suitable feature extractor that is able to give a raw of time-varying sensors measurements more meaningful representation to enhance prediction accuracy with low computational costs. In this paper, a new denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors (DDFF) and Updated Selection Strategy (USS) is proposed. First, depending on the characteristics of the training data that comes from aircraft sensors, robust feature extraction using a modified denoising autoencoder (DAE) is introduced to learn important patterns from data. Then, USS is integrated to ensure that only the useful data sequences pass through the training process. Finally, OS-ELM is used to fit the non-accumulative linear degradation function of the engine and to address dynamic programming by trucking the new coming data and forgetting gradually the old ones based on the proposed DDFF. The proposed DOS-ELM is tested on the public dataset of commercial modular aeropropulsion system simulation (C-MAPSS) of a turbofan engine and compared with OS-ELM trained with ordinary autoencoder (AE), basic OS-ELM and previous works from the literature. Comparison results prove the effectiveness of the new integrated robust feature extraction scheme by showing more stability of the network responses even under random solutions.

关键词： ELM OS-ELM Forgetting mechanism denoising autoencoder Updated selection strategy C-MAPPS Remaining Useful Life

来源：评论

学校读者我要写书评

暂无评论

Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features

引用

ENERGY 2022年 238卷

作者： Son, Seho Jeong, Siheon Kwak, Eunji Kim, Jun-hyeong Oh, Ki-Yong Chung Ang Univ Dept Intelligent Energy & Ind 84 Heukseok Ro Seoul 06974 South Korea Hanyang Univ Dept Mech Convergence Engn 222 Wangsimni Ro Seoul 04763 South Korea

This study proposes a highly reliable, robust, and accurate integrated framework to estimate the state-of health (SOH) of lithium-ion batteries (LIBs), focusing on feature extraction and manipulation. This framework comprises three phases: feature extraction, feature manipulation, and SOH estimation. First, multiphysics features are extracted from mechanical and electrochemical evolutionary responses as distinct health indicators (HIs) to account for the multiphysics degradation mechanisms. Second, these features are manipulated to eliminate outliers and noises. This phase is especially effective for impedance HIs, considering the high sensitivity of these HIs to minor environmental perturbations. Third, a multivariate Gaussian distribution theory estimates the SOH combined with a nonlinear quadratic kernel to account for nonlinear characteristics in degradation modes of LIBs. The estimated results under various environments verify that the multiphysics feature primarily increases accuracy, whereas the feature manipulation ensures reliability and robustness. However, both phases are complementary in securing the accuracy, reliability, and robustness of the framework. Although the lifespan of LIBs is estimated using the training set in the 5 % SOH range, the estimation errors of the proposed framework are less than 2.5 % in all test sets. Thus, the proposed method ensures its potential applicability in practical implementations of onboard battery management systems. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Lithium-ion battery denoising autoencoder Gaussian process regression State-of-health Machine learning

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

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

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：