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  • Open Access

    ARTICLE

    IWTW: A Framework for IoWT Cyber Threat Analysis

    GyuHyun Jeon1, Hojun Jin1, Ju Hyeon Lee1, Seungho Jeon2, Jung Taek Seo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1575-1622, 2024, DOI:10.32604/cmes.2024.053465 - 27 September 2024

    Abstract The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have… More >

  • Open Access

    ARTICLE

    HWD-YOLO: A New Vision-Based Helmet Wearing Detection Method

    Licheng Sun1, Heping Li2,3, Liang Wang1,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

    Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

  • Open Access

    ARTICLE

    MG-YOLOv5s: A Faster and Stronger Helmet Detection Algorithm

    Zerui Xiao, Wei Liu, Zhiwei Ye*, Jiatang Yuan, Shishi Liu

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475 - 17 July 2024

    Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

  • Open Access

    ARTICLE

    Influence of Anteroposterior Symmetrical Aero-Wings on the Aerodynamic Performance of High-Speed Train

    Peiheng He, Jiye Zhang*, Lan Zhang, Jiaqi Wang, Yuzhe Ma

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 937-953, 2024, DOI:10.32604/cmes.2023.043700 - 30 December 2023

    Abstract The running stability of high-speed train is largely constrained by the wheel-rail coupling relationship, and the continuous wear between the wheel and rail surfaces will profoundly affect the dynamic performance of the train. In recent years, under the background of increasing train speed, some scientific researchers have proposed a new idea of using the lift force generated by the aerodynamic wings (aero-wing) installed on the roof to reduce the sprung load of the carriage in order to alleviate the wear and tear of the wheel and rail. Based on the bidirectional running characteristics of high-speed… More >

  • Open Access

    ARTICLE

    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan1, Binqiang Chen1,*, Bin Yao1, Wangpeng He2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378 - 15 December 2023

    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >

  • Open Access

    ARTICLE

    Influence of Trailing-Edge Wear on the Vibrational Behavior of Wind Turbine Blades

    Yuanjun Dai1,2,*, Xin Wei1, Baohua Li1, Cong Wang1, Kunju Shi1

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.2, pp. 337-348, 2024, DOI:10.32604/fdmp.2023.042434 - 14 December 2023

    Abstract To study the impact of the trailing-edge wear on the vibrational behavior of wind-turbine blades, unworn blades and trailing-edge worn blades have been assessed through relevant modal tests. According to these experiments, the natural frequencies of trailing-edge worn blades −1, −2, and −3 increase the most in the second to fourth order, the fifth order increases in the middle, and the first order increases the least. The damping ratio data indicate that, in general, the first five-order damping ratios of trailing-edge worn blades −1 and trailing-edge worn blades −2 are reduced, and the first five-order More >

  • Open Access

    ARTICLE

    Application of Machine Learning For Prediction Dental Material Wear

    ABHIJEET SURYAWANSHI1, NIRANJANA BEHERA2,*

    Journal of Polymer Materials, Vol.40, No.3-4, pp. 305-316, 2023, DOI:10.32381/JPM.2023.40.3-4.11

    Abstract Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, More >

  • Open Access

    ARTICLE

    Interface and Friction Properties of Copper-embedded Polyethylene Terephthalate Filament

    FOUED KHOFFI1,*, OMAR HARZALLAH2, JEAN YVES DREAN2

    Journal of Polymer Materials, Vol.40, No.1-2, pp. 59-69, 2023, DOI:10.32381/JPM.2023.40.1-2.5

    Abstract The aim of this study is to analyze the interfacial and the frictional properties of copper (Cu) reinforced polyethylene terephthalate (PET) filament. This Cu-Embedded PET filament will be used as an information transmitter. This filament was prepared by a co-extrusion process. Mechanical properties of these filaments have been quantified by tensile and pull-out analyses. It is shown that the mechanical properties of composite filament were improved by adding the copper filament (from 0.82 to 1.2 GPa). The results of the pull-out test revealed some adhesion between the copper and the PET despite the existence of More >

  • Open Access

    ARTICLE

    Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

    Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940 - 05 February 2024

    Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires… More >

  • Open Access

    ARTICLE

    Detection of Safety Helmet-Wearing Based on the YOLO_CA Model

    Xiaoqin Wu, Songrong Qian*, Ming Yang

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3349-3366, 2023, DOI:10.32604/cmc.2023.043671 - 26 December 2023

    Abstract Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been… More >

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