Kechen Song^1,2,*, Wenqi Cui², Han Yu¹, Xingjie Li¹, Yunhui Yan^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451

Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel surface defects, tile surface defects,… More >

Xuelong Wu¹, Junsheng Wang^1,*, Zhongyao Li¹, Yisheng Miao¹, Chengpeng Xue¹, Yuling Lang², Decai Kong², Xiaoying Ma², Haibao Qiao²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027

Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method achieved 88.45% Class Pixel Accuracy… More >

Wuyang Fan, Shisheng Zhong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2525-2555, 2024, DOI:10.32604/cmes.2023.046951

Abstract The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model, which… More >

Longle Cheng^1,2, Xiru Li¹, Shiyu Fang², Wansu Pan¹, He Zhao^1,*, Haibo Tan¹, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2491-2509, 2024, DOI:10.32604/cmc.2024.047166

Abstract Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely… More >

Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860

Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of a multi-scale residual network, is… More >

XIN MENG^*, YAN LIU, SHUJUN XU, LIANRONG YANG, RUI YIN

BIOCELL, Vol.48, No.1, pp. 65-78, 2024, DOI:10.32604/biocell.2023.045986

Abstract Over the past decade, the swift advancement of metabolomics can be credited to significant progress in technologies such as mass spectrometry, nuclear magnetic resonance, and multivariate statistics. Currently, metabolomics garners widespread application across diverse fields including drug research and development, early disease detection, toxicology, food and nutrition science, biology, prescription, and chinmedomics, among others. Metabolomics serves as an effective characterization technique, offering insights into physiological process alterations in vivo. These changes may result from various exogenous factors like environmental conditions, stress, medications, as well as endogenous elements including genetic and protein-based influences. The potential scientific outcomes gleaned from these insights… More > Graphic Abstract

Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >

Xin Fan^1,2,3, Jingen Mao^2,3,*, Liangjue Lian^2,3, Li Yu¹, Wei Zheng^2,3, Yun Ge^2,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522

Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

Fengyong Li^1,*, Weicheng Shen¹, Zhongqin Bi¹, Xiangjing Su²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431

Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to… More >