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  • Open Access


    Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

    Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

    Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854

    Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

  • Open Access


    A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

    Sen-Hui Wang1,2,*, Xi Kang1, Cheng Wang1, Tian-Bing Ma1, Xiang He2, Ke Yang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1405-1427, 2024, DOI:10.32604/cmes.2024.049281

    Abstract Accurately predicting the remaining useful life (RUL) of bearings in mining rotating equipment is vital for mining enterprises. This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features. This study proposes a hybrid predictive model to assess the RUL of rolling element bearings. The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features. The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm. Subsequently,… More >

  • Open Access


    Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

    Deng Yang1, Chong Zhou1,*, Xuemeng Wei2, Zhikun Chen3, Zheng Zhang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049

    Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The… More >

  • Open Access


    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon1, Ye-Sol Oh1, Yeon-Ji Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239

    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

  • Open Access


    A Novel Approach to Energy Optimization: Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

    Muhammad Salman Qamar1,*, Ihsan ul Haq1, Amil Daraz2, Atif M. Alamri3, Salman A. AlQahtani4, Muhammad Fahad Munir1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168

    Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

  • Open Access


    Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection

    Hang Sun, Changsheng Li*, He Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2505-2522, 2024, DOI:10.32604/cmc.2024.049898

    Abstract Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions without necessitating extensive training. However, the complexity of operational instructions and the slow pace of character spelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects for classification and tracking. By applying predefined stimulation rules and object-specific masks, the proposed system triggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in the patient’s… More >

  • Open Access


    Hyperspectral Image Based Interpretable Feature Clustering Algorithm

    Yaming Kang1,*, Peishun Ye1, Yuxiu Bai1, Shi Qiu2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2151-2168, 2024, DOI:10.32604/cmc.2024.049360

    Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral… More >

  • Open Access


    A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

    Zhigao Zeng1,2, Aoting Tang1,2, Shengqiu Yi1,2, Xinpan Yuan1,2, Yanhui Zhu1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2277-2293, 2024, DOI:10.32604/cmc.2024.047989

    Abstract Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features More >

  • Open Access


    MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI

    Moshe Dayan Sirapangi1, S. Gopikrishnan1,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438

    Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >

  • Open Access


    Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

    Fangjun Luan1,2,3, Xuewen Mu1,2,3, Shuai Yuan1,2,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2024.048502

    Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries. However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To address these issues, we propose a novel approach for online signature verification, using a one-dimensional Ghost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature information and extracting local information, effectively complementing whole and… More >

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