Xingliang Deng¹, Peng Cao^2,*, Yintao Zhang¹, Yuhui Zhou³, Xiao Luo¹, Liang Wang³

Energy Engineering, Vol.121, No.5, pp. 1195-1207, 2024, DOI:10.32604/ee.2023.045870

Abstract The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich. The connectivity of carbonate reservoirs is complex, and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs. Thus, effective prediction of fractured-vuggy reservoirs is difficult. In view of this, this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir. To identify the complex connectivity among pores, fractures, and vugs, a simplified one-dimensional connectivity model is established by using the meshless connection element method (CEM). Considering… More >

Yingli Zhu^1,*, Jiachi Xie¹, Mingwei Zhu¹, Jun Zhang², Miaomiao Li³

Energy Engineering, Vol.121, No.5, pp. 1161-1172, 2024, DOI:10.32604/ee.2024.041205

Abstract The open ratio of a current collector has a great impact on direct methanol fuel cell (DMFC) performance. Although a number of studies have investigated the influence of the open ratio of DMFC current collectors, far too little attention has been given to how geometry (including the shape and feature size of the flow field) affects a current collector with an equal open ratio. In this paper, perforated and parallel current collectors with an equal open ratio of 50% and different feature sizes are designed, and the corresponding experimental results are shown to explain the geometry effects on the output… More >

Dingping Chen¹, Zhiheng Zhu², Jinyang Fu^1,3, Jilin He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1679-1703, 2024, DOI:10.32604/cmc.2024.049048

Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >

Le Zou¹, Linsong Hu¹, Yifan Wang¹, Zhize Wu², Xiaofeng Wang^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 927-941, 2024, DOI:10.32604/cmc.2024.048889

Abstract Depth estimation is an important task in computer vision. Collecting data at scale for monocular depth estimation is challenging, as this task requires simultaneously capturing RGB images and depth information. Therefore, data augmentation is crucial for this task. Existing data augmentation methods often employ pixel-wise transformations, which may inadvertently disrupt edge features. In this paper, we propose a data augmentation method for monocular depth estimation, which we refer to as the Perpendicular-Cutdepth method. This method involves cutting real-world depth maps along perpendicular directions and pasting them onto input images, thereby diversifying the data without compromising edge features. To validate the… More >

Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >

Yan Dong^1,2, Kang Zhao¹, Liang Gao¹, Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1-18, 2024, DOI:10.32604/cmc.2024.048870

Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >

Yakui Liu^1,2,3,*, Xing Jiang¹, Ruikang Xu¹, Yihao Cui¹, Chenhui Yu¹, Jingqi Yang¹, Jishuai Zhou¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1263-1279, 2024, DOI:10.32604/cmc.2024.048864

Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to… More >

Yushan Yin¹, Witold Pedrycz^1,2, Zhiwu Li^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 623-650, 2024, DOI:10.32604/cmc.2024.048787

Abstract Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with… More >

Lin Wang¹, Juan Zhao², Hu Song³, Xiaolong Xu^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688

Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable… More >

Meiyan Zhang¹, Boqi Zhao¹, Jipu Li², Qisong Wang^1,*, Dan Liu¹, Jinwei Sun¹, Jingxiao Liao^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1039-1061, 2024, DOI:10.32604/cmc.2024.048643

Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological signals and then reflected in… More >