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  • Open Access


    MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes

    Yingkun Song1, Shunhe Hong1, Chentao Hu1, Pingan He2, Lingbing Tao1, Zhixin Tie1,3,*, Chengfu Ding4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910

    Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism, Bidirectional Feature Pyramid Network (BiFPN)… More >

  • Open Access


    An Erebus Attack Detection Method Oriented to Blockchain Network Layer

    Qianyi Dai1,2,*, Bin Zhang1,2, Kaiyong Xu1,2, Shuqin Dong1,2

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5395-5431, 2023, DOI:10.32604/cmc.2023.036033

    Abstract Recently, the Erebus attack has proved to be a security threat to the blockchain network layer, and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer. The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks. This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer. The goal is to improve the performance of Erebus attack detection methods, by combining the traffic behavior… More >

  • Open Access


    A Novel Detection Method for Pavement Crack with Encoder-Decoder Architecture

    Yalong Yang1,2,3, Wenjing Xu1,2,3, Yinfeng Zhu4, Liangliang Su1,2,3,*, Gongquan Zhang1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 761-773, 2023, DOI:10.32604/cmes.2023.027010

    Abstract As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance… More >

  • Open Access


    Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

    Xiaoyu Cheng1, Mingxian Long1, Wei He1,2,*, Hailong Zhu1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2821-2844, 2023, DOI:10.32604/csse.2023.037330

    Abstract Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA),… More >

  • Open Access


    Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

    Zeyong Sun1, Guo Ran2, Zilong Jin1,3,*

    Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416

    Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve… More >

  • Open Access


    A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

    Qunbiao Wu1, Zhen Wang1,*, Haifeng Fang1, Junji Chen1, Xinfeng Wan2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 961-979, 2023, DOI:10.32604/csse.2023.036239

    Abstract For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump… More >

  • Open Access


    A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

    Chen Chen1,2, Xingqiu Li2,3,*, Kai Huang4, Zhongwei Xu1, Meng Mei1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033

    Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data pre-processing and 2D representation. Next,… More >

  • Open Access


    A Defect Detection Method for the Primary Stage of Software Development

    Qiang Zhi1, Wanxu Pu1, Jianguo Ren1, Zhengshu Zhou2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5141-5155, 2023, DOI:10.32604/cmc.2023.035846

    Abstract In the early stage of software development, a software requirements specification (SRS) is essential, and whether the requirements are clear and explicit is the key. However, due to various reasons, there may be a large number of misunderstandings. To generate high-quality software requirements specifications, numerous researchers have developed a variety of ways to improve the quality of SRS. In this paper, we propose a questions extraction method based on SRS elements decomposition, which evaluates the quality of SRS in the form of numerical indicators. The proposed method not only evaluates the quality of SRSs but also helps in the detection… More >

  • Open Access


    Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

    Idris Rabiu1,3,*, Naomie Salim2, Maged Nasser1,4, Aminu Da’u1, Taiseer Abdalla Elfadil Eisa5, Mhassen Elnour Elneel Dalam6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6001-6017, 2023, DOI:10.32604/cmc.2023.035221

    Abstract Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique,… More >

  • Open Access


    A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

    Ji Wang1, Liming Li1,2,3, Shubin Zheng1,3, Shuguang Zhao2, Xiaodong Chai1,3, Lele Peng1,3, Weiwei Qi1,3, Qianqian Tong1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1671-1706, 2023, DOI:10.32604/cmes.2022.022143

    Abstract Loosening detection; cascade deep convolutional neural network; object localization; saliency detection problem of bolts on axlebox covers. Firstly, an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers. And then, the A2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts. Finally, a rectangular approximation method is proposed to regularize the marker line regions as a way to calculate the angle of the marker line and plot all the angle values into an angle table, according… More > Graphic Abstract

    A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

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