A novel deformation forecasting method utilizing comprehensive observation data

Du, Sunwen; Yao, Li

doi:10.1177/1687814018796330

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The SVM algorithm offers robustness in learning biases in data [62]. It is simple and best suited to small sample cases [63]. Dubey and Mani [43] applied SVM to predict high school student employability in the US and got the an accuracy score of 93%.…”

Section: Yearmentioning

confidence: 99%

Applying Data Mining in Graduates’ Employability

2023

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Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.

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Section: Yearmentioning

confidence: 99%

Applying Data Mining in Graduates’ Employability

2023

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“…The Autoregressive Integrated Moving Average (ARIMA) method can extract the autocorrelation of time series [19][20][21][22][23], but it requires the time series to be stable and can only capture linear relationships. Multiple regression analysis is simple and easy to use with high accuracy [24][25][26][27][28], but it has issues with multicollinearity and lacks causal inference capability. Genetic algorithms are suitable for handling complex problems and situations lacking mathematical expressions [29][30][31][32][33], but they require special definitions, and parameter adjustments, and cannot guarantee the quality of solutions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

Xie,

Meng,

Wang

et al. 2024

Remote Sensing

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Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies.

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Experimental research on the acoustic transmission characteristics of refractory materials

Liu

Aqib

Wang

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The damage acoustic emission (AE) signal of refractories can be used to evaluate the classification and degree of the phase damage, which is different from the traditional damage location study in AE. In the analysis of AE signal, considering the nonuniformity of refractory inner structure, results of damage source inspection are usually affected by the signal transmission path. Therefore, the influence of transmitting path should be studied first. Piezoceramic material, capable of actuation and sensing, is commonly used to build AE probes. In this paper, two kinds of industrial refractories were selected as research objects and AE probes were chosen to generate emission acoustic signals. The relative amplitude attenuation coefficient and the relative energy attenuation coefficient were consequently designed as discriminant indicators to study the propagation characteristics of stress wave under different transmission paths and thicknesses. Results indicate that energy attenuation is higher than amplitude attenuation. Meanwhile, signal attenuation is principally proportional to transmission distance without directional selectivity. The above conclusions provide strong support to arrange AE sensors for nondestructive testing in refractory industry.

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A novel deformation forecasting method utilizing comprehensive observation data

Cited by 4 publications

References 25 publications

Applying Data Mining in Graduates’ Employability

Applying Data Mining in Graduates’ Employability

Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

Experimental research on the acoustic transmission characteristics of refractory materials

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