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  • Open Access

    ARTICLE

    Short-Term Photovoltaic Power Prediction Based on Multi-Stage Temporal Feature Learning

    Qiang Wang1, Hao Cheng2, Wenrui Zhang2,*, Guangxi Li3, Fan Xu2, Dianhao Chen4, Haixiang Zang4

    Energy Engineering, Vol.122, No.2, pp. 747-764, 2025, DOI:10.32604/ee.2025.059533 - 31 January 2025

    Abstract Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources. However, the fluctuations and intermittency of photovoltaic (PV) power pose challenges for its extensive incorporation into power grids. Thus, enhancing the precision of PV power prediction is particularly important. Although existing studies have made progress in short-term prediction, issues persist, particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data. These factors hinder improvements in PV power prediction performance. To overcome these challenges, this paper… More >

  • Open Access

    ARTICLE

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

    Hongxing Wang1, Xilai Ju2, Hua Zhu1,*, Huafeng Li1,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

    Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

  • Open Access

    ARTICLE

    A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

    Jianwei Zhang1,2,*, Zhishang Zhao3, Zengyu Cai3, Yuan Feng4, Liang Zhu3, Yahui Sun3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 977-996, 2025, DOI:10.32604/cmc.2024.058438 - 03 January 2025

    Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >

  • Open Access

    ARTICLE

    A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

    Xiaoyun Chen1, Lanyao Zhang1, Xiaoling Chen1, Yigang Cen2, Linna Zhang1,*, Fugui Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 521-542, 2025, DOI:10.32604/cmc.2024.058063 - 03 January 2025

    Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >

  • Open Access

    ARTICLE

    Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

    Feifei Yu1, Yongxian Huang2,*, Guoyan Chen1, Xiaoqing Yang2, Canyi Du2,*, Yongkang Gong2

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 843-862, 2025, DOI:10.32604/cmc.2024.058051 - 03 January 2025

    Abstract To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine’s normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and… More >

  • Open Access

    ARTICLE

    Malicious Document Detection Based on GGE Visualization

    Youhe Wang, Yi Sun*, Yujie Li, Chuanqi Zhou

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1233-1254, 2025, DOI:10.32604/cmc.2024.057710 - 03 January 2025

    Abstract With the development of anti-virus technology, malicious documents have gradually become the main pathway of Advanced Persistent Threat (APT) attacks, therefore, the development of effective malicious document classifiers has become particularly urgent. Currently, detection methods based on document structure and behavioral features encounter challenges in feature engineering, these methods not only have limited accuracy, but also consume large resources, and usually can only detect documents in specific formats, which lacks versatility and adaptability. To address such problems, this paper proposes a novel malicious document detection method-visualizing documents as GGE images (Grayscale, Grayscale matrix, Entropy). The… More >

  • Open Access

    ARTICLE

    DIGNN-A: Real-Time Network Intrusion Detection with Integrated Neural Networks Based on Dynamic Graph

    Jizhao Liu, Minghao Guo*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 817-842, 2025, DOI:10.32604/cmc.2024.057660 - 03 January 2025

    Abstract The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats. Intrusion detection systems are crucial to network security, playing a pivotal role in safeguarding networks from potential threats. However, in the context of an evolving landscape of sophisticated and elusive attacks, existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts. To address these issues, this paper proposes a real-time network intrusion detection method based on… More >

  • Open Access

    ARTICLE

    Lightweight Underwater Target Detection Using YOLOv8 with Multi-Scale Cross-Channel Attention

    Xueyan Ding1,2, Xiyu Chen1, Jiaxin Wang1, Jianxin Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 713-727, 2025, DOI:10.32604/cmc.2024.057655 - 03 January 2025

    Abstract Underwater target detection is extensively applied in domains such as underwater search and rescue, environmental monitoring, and marine resource surveys. It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration. Nevertheless, low imaging quality, harsh underwater environments, and obscured objects considerably increase the difficulty of detecting underwater targets, making it difficult for current detection methods to achieve optimal performance. In order to enhance underwater object perception and improve target detection precision, we propose a lightweight underwater target detection method using You Only Look Once (YOLO) v8 with multi-scale cross-channel attention (MSCCA), named… More >

  • Open Access

    ARTICLE

    Loss Aware Feature Attention Mechanism for Class and Feature Imbalance Issue

    Yuewei Wu1, Ruiling Fu1, Tongtong Xing1, Fulian Yin1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 751-775, 2025, DOI:10.32604/cmc.2024.057606 - 03 January 2025

    Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >

  • Open Access

    ARTICLE

    IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

    Zhe Li, Yun Liang, Jinyu Wang, Yang Gao*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1171-1192, 2025, DOI:10.32604/cmc.2024.057225 - 03 January 2025

    Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >

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