Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, 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 >

Qianyi Dai^1,2,*, Bin Zhang^1,2, Kaiyong Xu^1,2, Shuqin Dong^1,2

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 >

Yalong Yang^1,2,3, Wenjing Xu^1,2,3, Yinfeng Zhu⁴, Liangliang Su^1,2,3,*, Gongquan Zhang^1,2,3

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 >

Xiaoyu Cheng¹, Mingxian Long¹, Wei He^1,2,*, 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 >

Zeyong Sun¹, Guo Ran², Zilong Jin^1,3,*

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 >

Qunbiao Wu¹, Zhen Wang^1,*, 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 >

Chen Chen^1,2, Xingqiu Li^2,3,*, 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 >

Qiang Zhi¹, Wanxu Pu¹, Jianguo Ren¹, Zhengshu Zhou^2,*

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 >

Idris Rabiu^1,3,*, Naomie Salim², Maged Nasser^1,4, 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 >

Ji Wang¹, Liming Li^1,2,3, Shubin Zheng^1,3, Shuguang Zhao², Xiaodong Chai^1,3, Lele Peng^1,3, Weiwei Qi^1,3, 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