Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data

Konstantopoulos, Georgios; Koumoulos, Elias P.; Charitidis, Costas A.

doi:10.1016/j.matdes.2020.108705

Cited by 49 publications

(21 citation statements)

References 35 publications

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“…1), [42][43][44] and recently nanoindentation data has been used to train machine learning algorithms. 45 In the pharmaceutical industry, it is of utmost importance to understand the elastic and mechanical properties of active pharmaceutical ingredients (APIs). For the API, mechanical properties govern physicochemical properties such as solubility, tabletability, stability and the bioavailability of a drug substance.…”

Section: Why Are Mechanical Properties Important?mentioning

confidence: 99%

Density functional theory predictions of the mechanical properties of crystalline materials

et al. 2021

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Section: Why Are Mechanical Properties Important?mentioning

confidence: 99%

Density functional theory predictions of the mechanical properties of crystalline materials

et al. 2021

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“…In this paper, confusion matrix was introduced to evaluate the performance of the model trained by various methods, and the difference of identification performance of different methods in sawn timber data is compared. The confusion matrix can clearly show the identification results of the model on the test set [ 36 ]. Through the confusion matrix, four indicators representing the identification performance of the model, namely, accuracy rate, recall rate, accuracy rate and F1-Score, can be obtained.…”

Section: Resultsmentioning

confidence: 99%

A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet

Ding

Liu

Zhuang

et al. 2021

Sensors

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Sawn timber is an important component material in furniture manufacturing, decoration, construction and other industries. The mechanical properties, surface colors, textures, use and other properties of sawn timber possesed by different tree species are different. In order to meet the needs of reasonable timber use and product quality of sawn timber products, sawn timber must be identified according to tree species to ensure the best use of materials. In this study, an optimized convolution neural network was proposed to process sawn timber image data to identify the tree species of the sawn timber. The spatial pyramid pooling and attention mechanism were used to improve the convolution layer of ResNet101 to extract the feature vector of sawn timber images. The optimized ResNet (simply called “AM-SPPResNet”) was used to identify the sawn timber image, and the basic recognition model was obtained. Then, the weight parameters of the feature extraction layer of the basic model were frozen, the full connection layer was removed, and using support vector machine (SVM) and XGBoost classifier which were commonly used in machine learning to train and learn the 21 × 1024 dimension feature vectors extracted by feature extraction layer. Through a number of comparative experiments, it is found that the prediction model using linear function as the kernel function of support vector machine learning the feature vectors extracted from the improved convolution layer performed best, and the F1 score and overall accuracy of all kinds of samples were above 99%. Compared with the traditional methods, the accuracy was improved by up to 12%.

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“…True positives (TP) denote the success in the identification of the correct reinforcement class (positive samples), true negatives (TN) denote the successful classification of negative samples, false positives (FP) stand for the incorrect classifications of negative samples into positive samples, and false negatives (FN) denote the positive samples that are incorrectly predicted as negative samples [ 92 , 95 ]. Acc is the most primitive evaluation metric in classification problems.…”

Section: Methodsmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data

Cited by 49 publications

References 35 publications

Density functional theory predictions of the mechanical properties of crystalline materials

Density functional theory predictions of the mechanical properties of crystalline materials

A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet

A Labeling Method for Financial Time Series Prediction Based on Trends

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