DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection

Qin, Chengjin; Jin, Yanrui; Tao, Jianfeng; Xiao, Dengyu; Yu, Honggan; Liu, Chao; Shi, Gang; Lei, Jiehao; Liu, Chengliang

doi:10.1016/j.measurement.2021.109548

Cited by 67 publications

(36 citation statements)

References 43 publications

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“…The method of efficiency quality has also developed from a single index to a multi-dimensional index (Hu et al, 2020). Data envelopment analysis (DEA) is also used to evaluate the efficiency of investment in enterprises (Huang et al, 2020;Qin et al, 2021). However, the reasons for the low efficiency of the existing investment in SMEs are more based on the evaluation results of some factors (Das, 2019;Feng et al, 2018;Lee et al, 2020a;Wang et al, 2021a), and the results may be biased.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on the investment efficiency based on grey correlation-DEA model

Zhao

Liu

et al. 2021

Ann Oper Res

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Small and medium-sized enterprises (SMEs) are an important part of stimulating market vitality. In the post-pandemic era, the ability of SMEs to absorb employment plays an important role in stabilizing society and promoting economic growth. This paper selects 226 sample data from 2014 to 2017 measures the investment efficiency of small and medium-sized enterprises and makes a further analysis its influencing factors. Because there is a lag between investment and output. In this paper, the grey correlation analysis is used. Measuring the investment efficiency of SMEs by using BBC-DEA method. The study found that the overall investment efficiency of SMEs is low. Considering from the inside of the enterprise, this paper uses the Tobit model to make an empirical analysis. It is found that the influence of board structure and agency cost on investment efficiency are significantly negative. Growth, ownership concentration, equity incentive, salary incentive, profitability of SMEs have a significant positive effect on the investment efficiency of enterprises.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on the investment efficiency based on grey correlation-DEA model

Zhao

Liu

et al. 2021

Ann Oper Res

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“…Two hyperparameters of the RF model need to be adjusted, which are numTree and minNumLea f . To eliminate the overfitting problems caused by a limited database, a 10-fold cross-validation method was utilised, which is frequently used in predicting cementitious materials performance [47,50]. Meanwhile, all the data can be used for training and testing, giving a fair and comprehensive evaluation metric, although the database is not large.…”

Section: The 10-fold Cross-validation Processmentioning

confidence: 99%

Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite

et al. 2021

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Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively.

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“…RNN and CNN have their specific advantages and disadvantages. In 2019, some researchers proposed the DLCNN (Densely-connected LSTM-CNN) model of serial integration of densely connected LSTM network and CNN (Shahid et al, 2020;Qin et al, 2021;Jian et al, 2021). DLCNN can better balance the local characteristics and timing characteristics.…”

Section: Customer Churn Prediction In the Development Of Rural Financementioning

confidence: 99%

Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

Hou

Tang

Liu

et al. 2021

Journal of Global Information Management

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The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

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DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection

Cited by 67 publications

References 43 publications

RETRACTED ARTICLE: Research on the investment efficiency based on grey correlation-DEA model

RETRACTED ARTICLE: Research on the investment efficiency based on grey correlation-DEA model

Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite

Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

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