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  • Open Access

    PROCEEDINGS

    Damage Detection in CFRP Composite Joints using Acoustic Emission Analysis

    Wenhao Li1,*, Zongyang Liu1,2, Dingcheng Ji1,2, Yiding Liu3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011927

    Abstract This research advances the field by focusing on the damage assessment of adhesively bonded joints using AE, with limited prior studies in this specific area. Through the preparation of CFRP specimens and subsequent tensile loading tests, AE signals were captured and analyzed. The study employed wavelet decomposition for noise reduction and Short-Time Fourier Transform (STFT) for signal analysis, facilitating the identification of damage-related frequencies and amplitudes. Hierarchical clustering was applied to categorize AE signals into distinct damage behaviors, utilizing a divisive approach that avoids local minima and offers unique results at each iteration. The method's… More >

  • Open Access

    ARTICLE

    Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

    Yuxuan Zhao1,2,*, Bo Wang1, Shu Wang1, Wenjun Xu2, Gang Ma2

    Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

    Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

  • Open Access

    ARTICLE

    DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

    Yuanyuan Ma1, Ang Chen1, Cunzhi Hou1, Ruixia Jin2, Jinghui Zhang1, Ruixiang Li3,4,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3301-3323, 2024, DOI:10.32604/cmc.2024.056854 - 18 November 2024

    Abstract Currently, telecom fraud is expanding from the traditional telephone network to the Internet, and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights. However, existing telecom fraud identification methods based on blacklists, reputation, content and behavioral characteristics have good identification performance in the telephone network, but it is difficult to apply to the Internet where IP (Internet Protocol) addresses change dynamically. To address this issue, we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering (DC-FIPD). First, we… More >

  • Open Access

    ARTICLE

    TLERAD: Transfer Learning for Enhanced Ransomware Attack Detection

    Isha Sood*, Varsha Sharma

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2791-2818, 2024, DOI:10.32604/cmc.2024.055463 - 18 November 2024

    Abstract Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases, rendering them less effective against evolving ransomware families. This paper introduces TLERAD (Transfer Learning for Enhanced Ransomware Attack Detection), a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains, enabling robust detection of both known and unknown ransomware variants. The proposed method More >

  • Open Access

    ARTICLE

    A Novel Filtering-Based Detection Method for Small Targets in Infrared Images

    Sanxia Shi, Yinglei Song*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2911-2934, 2024, DOI:10.32604/cmc.2024.055363 - 18 November 2024

    Abstract Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.… More >

  • Open Access

    ARTICLE

    An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

    Sonia Goel1,#, Meena Tushir1, Jyoti Arora2, Tripti Sharma2, Deepali Gupta3, Ali Nauman4,#, Ghulam Muhammad5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3125-3145, 2024, DOI:10.32604/cmc.2024.054476 - 18 November 2024

    Abstract In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear More >

  • Open Access

    ARTICLE

    Three-Level Optimal Scheduling and Power Allocation Strategy for Power System Containing Wind-Storage Combined Unit

    Jingjing Bai1, Yunpeng Cheng1, Shenyun Yao2,*, Fan Wu1, Cheng Chen1

    Energy Engineering, Vol.121, No.11, pp. 3381-3400, 2024, DOI:10.32604/ee.2024.053683 - 21 October 2024

    Abstract To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and… More >

  • Open Access

    ARTICLE

    Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

    Hongjun Zhang1,2, Zeyu Zhang3, Yilong Ruan4, Hao Ye5,6, Peng Li1,*, Desheng Shi1

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1409-1432, 2024, DOI:10.32604/cmc.2024.055406 - 15 October 2024

    Abstract The scale and complexity of big data are growing continuously, posing severe challenges to traditional data processing methods, especially in the field of clustering analysis. To address this issue, this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update (BDTMCDIncreUpdate), which combines distributed computing, storage technology, and incremental update techniques to provide an efficient and effective means for clustering analysis. Firstly, the original dataset is divided into multiple sub-blocks, and distributed computing resources are utilized to process the sub-blocks in parallel, enhancing efficiency. Then, initial clustering is performed on each sub-block… More >

  • Open Access

    ARTICLE

    Fuzzy Machine Learning-Based Algorithms for Mapping Cumin and Fennel Spices Crop Fields Using Sentinel-2 Satellite Data

    Shilpa Suman1, Abhishek Rawat2,*, Anil Kumar3, S. K. Tiwari4

    Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981 - 18 September 2024

    Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

  • Open Access

    ARTICLE

    Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

    Xianghui Hu1, Yiming Tang2,3, Witold Pedrycz3,4, Jiuchuan Jiang5,*, Yichuan Jiang1,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4917-4945, 2024, DOI:10.32604/cmc.2024.054775 - 12 September 2024

    Abstract Traditional Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms are data-driven, and their objective function minimization process is based on the available numeric data. Recently, knowledge hints have been introduced to form knowledge-driven clustering algorithms, which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints. However, these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself; they require the assistance of evaluation indices. Moreover, knowledge hints are usually used as part of the data structure (directly replacing some clustering centers),… More >

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