Prediction of tire pattern noise in early design stage based on convolutional neural network

Lee, Sang-Kwon; Lee, Hwajin; Back, Jiseon; An, Kanghyun; Yoon, Young-Sam; Yum, Kiho; kim, Sungdae; Hwang, Sung-Uk

doi:10.1016/j.apacoust.2020.107617

Cited by 25 publications

(9 citation statements)

References 16 publications

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“…According to (3) and (4), when performing convolution operation, DSC needs to calculate more parameters than standard convolution when the number of convolution kernels is 1. But when the number of convolution kernels is greater than 1, standard convolution needs to calculate more parameters than DSC ( Lee et al., 2021 ). If a model needs to obtain more image features through convolution operations, the number of convolution kernels will increase accordingly.…”

Section: Methodsmentioning

confidence: 99%

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Zhai

Sun

2021

International Journal of Cognitive Computing in Engineering

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Purpose As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low cost and high accuracy of single plane lesions recognition. The current COVID-19 detection models have shortcomings such as weak robustness, unreliable generalization ability, and long training time. Methods To solve the above problems, our team proposed two novel frameworks and five methods to diagnose COVID-19 based on chest X-ray images. (i) A novel framework – depthwise separable convolutional neural network (DCNN), and we tested Three methods, viz., using LeNet-5, VGG-16, and ResNet-18 as backbones. (ii) A novel framework – dilated and depthwise separable convolutional neural network (DDCNN), and we tested Two methods, viz., using VGG-16 and ResNet-18 as backbones. Results Experiment results show that our models not only improve the detection accuracy, but also reduce the training time. Conclusions Our methods are superior to state-of-the-art methods in both above aspects.

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Section: Methodsmentioning

confidence: 99%

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Zhai

Sun

2021

International Journal of Cognitive Computing in Engineering

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“…The gradient is not accurate enough when processing a large amount of data due to the complexity of the LSTM structure, which affects the prediction results. Optimization algorithms that are commonly used include stochastic gradient descent (SGD), adaptive gradient algorithm (AdaGrad), and root mean square prop (RMSprop) (Cheridito et al, 2020;Han et al, 2017;Lee et al, 2021). The Adam algorithm is currently the most popular deep learning optimization method because it not only improves operation efficiency but also consumes less memory (Kingma and Ba, 2014).…”

Section: Adam Algorithmmentioning

confidence: 99%

Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China

Zhao

et al. 2023

Environ Sci Pollut Res

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Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.

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“…The CNN gradient vectors loaded with the pixel information derived from the dataset D where all the object shapes and features were considered as gradient of (f ) and gx + yx [36,37]. The features coordinates such as (x, y) represents the shape on the bases of z variable where each pixel intensity is considerable amount of information which can be used to train and test the CNN architecture [38].…”

Section: Tensor Flow Data Conversionmentioning

confidence: 99%

Deep Learning Framework for Precipitation Prediction Using Cloud Images

Baig¹,

Mallah²,

Shaikh³

2022

Computers, Materials &Amp; Continua

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Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers to know about the weather conditions and to care their cash crops. This research proposes a framework to classify the dark cloud patterns (DCP) for prediction of precipitation. The framework consists upon three steps to classify the cloud images, first step tackles noise reduction operations, feature selection and preparation of datasets. Second step construct the decision model by using convolutional neural network (CNN) and third step presents the performance visualization by using confusion matrix, precision, recall and accuracy measures. This research contributes (1) real-world clouds datasets (2) method to prepare datasets (3) highest classification accuracy to predict estimated as 96.90%.

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Prediction of tire pattern noise in early design stage based on convolutional neural network

Cited by 25 publications

References 16 publications

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images

Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China

Deep Learning Framework for Precipitation Prediction Using Cloud Images

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