A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model

Jiang, Fan; Guan, Zhidong; Li, Zengshan; Wang, Xiaodong

doi:10.1016/j.cja.2020.10.006

Cited by 28 publications

(10 citation statements)

References 18 publications

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“…where β denotes the risk probability of the supply chain. The present work mainly adopts the logistic regression model with two classifications shown in Equation (7). PCA tries to regroup many original indicators with specific correlations (such as P indicators) into a new set of unrelated comprehensive indicators to replace the original indicators [28].…”

Section: Logistic Regression Analysismentioning

confidence: 99%

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Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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The present work is aimed at solving the investment risks in the supply chain management (SCM) process of enterprises. Therefore, the Back Propagation Neural Network (BPNN) algorithm, logistic regression analysis, and other related theories are used for the risk prediction analysis of supply chain samples. Firstly, 40 pieces of supply chain training data are collected as research samples. Secondly, the examples are trained using the BPNN algorithm. Meanwhile, a logistic regression model is constructed based on Principal Component Analysis (PCA). Finally, the two models conduct risk prediction on the test samples. The results indicate that the BPNN model can effectively predict various risks in the SCM process. It achieves an excellent evaluation effect of single risks, consistent with the actual results. Still, there are some deviations between the prediction results and the actual results of mixed risks. When the significance P value is more than 0.5, the sample is predicted to be of high risk. When it is less than 0.5, the sample is predicted to be of low risk. The prediction accuracy of the logistic regression model is as high as 92.8%, demonstrating brilliant applicability and popularization in the investment risk prediction of the supply chain. The BPNN algorithm and logistic regression model can precisely predict the investment risk in SCM and provide a reference for the improved SCM and the sustainable and stable development of enterprises in the supply chain.

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Section: Logistic Regression Analysismentioning

confidence: 99%

“…Jiang et al established a risk index evaluation system by sampling the supply chain data of enterprises. They also built a logistic regression model based on PCA to predict the risk of supply chain variables faced by enterprises [7]. Zheng et al used the primary effect model to evaluate the influencing factors of logistics enterprises.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

2022

Wireless Communications and Mobile Computing

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“…LR is a supervised learning algorithm mainly used for binary classification. In landslide modeling using this method, the presence of landslides is considered as "1" and the absence of landslides is considered as "0" [23]. When LR is fitted for a regression or classification problem, it first creates a valuable practice.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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“…In this model, whether the eggshell has cracks was regarded as the dependent variable. The dependent variable is binary [33], where "0" implies the presence of cracks and "1" indicates no cracks. Therefore, logistic regression technology is used to analyze whether the duck eggshell has cracks.…”

Section: Logistic Regressionmentioning

confidence: 99%

Duck Eggshell Crack Detection by Nondestructive Sonic Measurement and Analysis

Lai

Huang

et al. 2021

Sensors

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Duck eggs are a good source of essential nutrients for the human body. However, transportation, processing, and handling can easily cause cracks in the eggshells. These cracks can lead to microbial contamination, reducing the shelf life and compromising food safety. In this study, a method for the nondestructive testing of cracks in duck eggshells was developed. First, the acoustic emission signals of intact and cracked eggshells were measured, and the most significant frequency features were selected to establish a calibration curve for cracked eggshells. Logistic regression using the frequency features was then adopted to predict intact and cracked eggshells. Then, we establish a set of optimal regression models and used independent samples for verification. The overall accuracy rates of the calibration and prediction models using five frequencies of bandwidth (1500, 5000, 6000, 8500, and 10,000 Hz) were 89.7% and 87.6%, respectively. Sound measurement enables a simple and quantitative method for duck egg crack detection and classification. This nondestructive and cost-effective method can be used for duck egg quality screening and can be integrated into duck egg processing machinery.

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A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model

Cited by 28 publications

References 18 publications

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Duck Eggshell Crack Detection by Nondestructive Sonic Measurement and Analysis

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