Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025

Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >

Lulu Zhang, Xuehui Du^*, Wenjuan Wang, Yu Cao, Xiangyu Wu, Shihao Wang

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1901-1919, 2025, DOI:10.32604/cmc.2025.064270 - 09 June 2025

Abstract To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features More >

Wenqiu Zhu^1,2, Yongsheng Li^1,2, Qiang Liu^1,2,*, Zhigao Zeng^1,2

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216 - 31 October 2023

Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs… More >

Yinghua Song^1,2, Hairong Lyu^1,2, Wei Zhang^1,2,*

Journal on Big Data, Vol.5, pp. 19-40, 2023, DOI:10.32604/jbd.2023.038249 - 05 October 2023

Abstract A precise and timely forecast of short-term rail transit passenger flow provides data support for traffic management and operation, assisting rail operators in efficiently allocating resources and timely relieving pressure on passenger safety and operation. First, the passenger flow sequence models in the study are broken down using VMD for noise reduction. The objective environment features are then added to the characteristic factors that affect the passenger flow. The target station serves as an additional spatial feature and is mined concurrently using the KNN algorithm. It is shown that the hybrid model VMD-CLSMT has a More >

Guowei Zhu^*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1521-1528, 2022, DOI:10.32604/fdmp.2022.020942 - 27 May 2022

Abstract In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. More >