Dingping Chen¹, Zhiheng Zhu², Jinyang Fu^1,3, Jilin He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1679-1703, 2024, DOI:10.32604/cmc.2024.049048

Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >

Shubin Zheng^1,3, Yue Wang², Liming Li^2,3,*, Xieqi Chen^2,3, Lele Peng^2,3, Zhanhao Shang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 565-592, 2024, DOI:10.32604/cmes.2023.043832

Abstract Regular fastener detection is necessary to ensure the safety of railways. However, the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways. Existing supervised inspection methods have insufficient detection ability in cases of imbalanced samples. To solve this problem, we propose an approach based on deep convolutional neural networks (DCNNs), which consists of three stages: fastener localization, abnormal fastener sample generation based on saliency detection, and fastener state inspection. First, a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions. Then, the foreground clip region of a fastener image… More >

Lei Hu^1,*, Yuanwen Lu¹, Si Wang², Wenbin Wang³, Yongmei Zhang⁴

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2735-2749, 2023, DOI:10.32604/cmc.2023.042974

Abstract The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle (UAV) due to the complex background of distribution lines, variable morphology of equipment, and large differences in equipment sizes. Therefore, aiming at the difficult detection of power equipment in UAV inspection images, we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s. Based on the YOLOx-s network, we make the following improvements: 1) The Receptive Field Block (RFB) module is added after the shallow feature layer of the backbone network to… More >

Ziyang Jiang¹, Ziping Wang^1,*, Kan Feng¹, Yang Zhang², Rahim Gorgin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 557-569, 2023, DOI:10.32604/sdhm.2023.042393

Abstract With the advancement of computer and mathematical techniques, significant progress has been made in the 3D modeling of foundation piles. Existing methods include the 3D semi-analytical model for non-destructive low-strain integrity assessment of large-diameter thin-walled pipe piles and the 3D soil-pile dynamic interaction model. However, these methods have complex analysis procedures and substantial limitations. This paper introduces an innovative and streamlined 3D imaging technique tailored for the detection of pile damage. The approach harnesses the power of an eight-channel ring array transducer to capture internal reflection signals within foundation piles. The acquired signals are subsequently processed using the Hilbert-Huang Transform… More >

Lucio Nobile^*

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 353-368, 2023, DOI:10.32604/sdhm.2023.028247

Abstract The frequent occurrence of seismic events in Italy poses a strategic problem that involves either the culture of preservation of historical heritage or the civil protection action aimed to reduce the risk to people and goods (buildings, bridges, dams, slopes, etc.). Most of the Italian architectural heritage is vulnerable to earthquakes, identifying the vulnerability as the inherent predisposition of the masonry building to suffer damage and collapse during an earthquake. In fact, the structural concept prevailing in these ancient masonry buildings is aimed at ensuring prevalent resistance to vertical gravity loads. Rarely do these ancient masonry structures offer relevant resistance… More >

Ki-Yeol Eom¹, Byungseok Min^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336

Abstract Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors, autonomous vehicles, and artificial intelligence devices. However, there are few solutions to segment occluded objects in the X-ray inspection efficiently. In particular, in the Ball Grid Array inspection of X-ray images, it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls. In this paper, we present a novel automatic inspection algorithm that segments solder balls, and detects defects fast and efficiently when solder balls are occluded. The proposed algorithm consists of two stages. In the first stage, the… More >

Pramote Charongrattanasakul¹, Wimonmas Bamrungsetthapong^2,*, Poom Kumam³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1631-1651, 2023, DOI:10.32604/csse.2023.036179

Abstract A novel adaptive multiple dependent state sampling plan (AMDSSP) was designed to inspect products from a continuous manufacturing process under the accelerated life test (ALT) using both double sampling plan (DSP) and multiple dependent state sampling plan (MDSSP) concepts. Under accelerated conditions, the lifetime of a product follows the Weibull distribution with a known shape parameter, while the scale parameter can be determined using the acceleration factor (AF). The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive. An economic design of the proposed sampling plan was also considered for the ALT. A genetic algorithm with… More >

Hua Li^1,*, Dezhi Xiong^2,3, Zhi Wang^2,3

Energy Engineering, Vol.120, No.3, pp. 715-727, 2023, DOI:10.32604/ee.2022.022990

Abstract The on-site inspection of high-power DC chargers results in new DC high-current measurement and DC energy traceability system requirements. This paper studies the traceability technology of electric energy value for automotive high-power DC chargers, including: (1) the traceability method of the built-in DC energy meter and shunt of the charger; (2) precision DC high current and small precision DC voltage output and measurement technology. This paper designs a 0.1 mA~600 A DC high current measurement system and proposes a 0.005 level DC power measurement traceability system. The uncertainty evaluation experiment of the DC power measurement calibration system and the high-power… More >

A. Sriram^1,*, T. D. Sudhakar²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2761-2772, 2023, DOI:10.32604/csse.2023.028559

Abstract Intended for good productivity and perfect operation of the solar power grid a failure-free system is required. Therefore, thermal image processing with the thermal camera is the latest non-invasive (without manual contact) type fault identification technique which may give good precision in all aspects. The soiling issue, which is major productivity affecting factor may import from several reasons such as dust on the wind, bird mucks, etc. The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7% in the county, such as India, to more than 25% in middle-east countries country, such as Dubai, Kuwait, etc. This… More >

Mei Wang^1,*, Hao Xu², Yadang Chen¹

Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447

Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the multivariate Gaussian (MVG), and determine… More >