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  • Open Access


    Ice-Induced Vibrational Response of Single-Pile Offshore Wind-Turbine Foundations

    Zhoujie Zhu1, Gang Wang1, Qingquan Liu1, Guojun Wang2, Rui Dong2, Dayong Zhang2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 625-639, 2024, DOI:10.32604/fdmp.2023.042128

    Abstract Important challenges must be addressed to make wind turbines sustainable renewable energy sources. A typical problem concerns the design of the foundation. If the pile diameter is larger than that of the jacket platform, traditional mechanical models cannot be used. In this study, relying on the seabed soil data of an offshore wind farm, the m-method and the equivalent embedded method are used to address the single-pile wind turbine foundation problem for different pile diameters. An approach to determine the equivalent pile length is also proposed accordingly. The results provide evidence for the effectiveness and reliability More >

  • Open Access


    Field Observation and Numerical Simulation of Extreme Met-Ocean Conditions: A Case Study of Typhoon Events in South China Sea

    Chen Gu1,*, Caiyu Wang1, Mengjiao Du2, Kan Yi2, Bihong Zhu1, Hao Wang2, Shu Dai1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09776

    Abstract Site measurement is essential to the meteorological and oceanographic parameters of offshore wind farms. A floating lidar measurement buoy was deployed at a Qingzhou VI wind farm where is 45-80 km away from Guangdong coast. The field observation including wind and wave data start from March, 2021.The lidar wind data is compared and calibrated with the fixed wind tower data for three months, the accuracy meets the standard of stadge3 carbon trust. In this study, all these data are used to recalibrate for the met-ocean model to relies extreme conditions, such as Typhoon Kompasu(2118) and More >

  • Open Access


    Etude exploratoire de l’intolérance à l’incertitude et de ses processus cognitifs par la tâche du Stroop chez les parents d’un enfant en rémission de cancer

    Marie Vander Haegen*, Anne-Marie Etienne

    Psycho-Oncologie, Vol.17, No.4, pp. 257-266, 2023, DOI:10.32604/po.2023.043340

    Abstract Introduction. Aucune étude n’a examiné le lien entre le facteur d’intolérance à l’incertitude et ses effets sur les processus cognitifs de parents d’un enfant en rémission d’un cancer. L’intolérance à l’incertitude est un facteur central dans le trouble anxieux généralisé; trouble anxieux où l’on observe également un traitement altéré de l’information cognitive sous forme de biais cognitifs. L’objectif de l’étude est de mesurer via une tâche de Stroop test classique et de Stroop test émotionnel, l’orientation de l’attention sélective en lien avec le facteur d’intolérance à l’incertitude. Méthodes. Soixante et un parents d’un enfant en rémission… More >

  • Open Access


    RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

    Hangtian Song1,2, Xudong Chen1,2, Chun Zhu3, Qian Yin4, Wei Wang1,2, Qingxiang Meng1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 337-359, 2024, DOI:10.32604/cmes.2023.031496

    Abstract When the geological environment of rock masses is disturbed, numerous non-persisting open joints can appear within it. It is crucial to investigate the effect of open joints on the mechanical properties of rock mass. However, it has been challenging to generate realistic open joints in traditional experimental tests and numerical simulations. This paper presents a novel solution to solve the problem. By utilizing the stochastic distribution of joints and an enhanced-fractal interpolation system (IFS) method, rough curves with any orientation can be generated. The Douglas-Peucker algorithm is then applied to simplify these curves by removing More > Graphic Abstract

    RB-DEM Modeling and Simulation of Non-Persisting Rough Open Joints Based on the IFS-Enhanced Method

  • Open Access


    CFSA-Net: Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

    Jun Shu1,2, Shuai Wang1,2, Shiqi Yu1,2, Jie Zhang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2677-2697, 2023, DOI:10.32604/cmc.2023.045818

    Abstract Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging random sampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for… More >

  • Open Access


    Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

    Ming Wan1, Minglei Hao1, Jiawei Li1, Jiangyuan Yao2,*, Yan Song3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857

    Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged… More >

  • Open Access


    Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

    Se Li1, Tiantang Yu1,*, Tinh Quoc Bui2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2793-2808, 2024, DOI:10.32604/cmes.2023.030278

    Abstract Isogeometric analysis (IGA) is known to show advanced features compared to traditional finite element approaches. Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functional grading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward a deep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complex IGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trained using the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationship between More >

  • Open Access


    Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

    Arsal Javaid1, Areeb Abbas1, Jehangir Arshad1, Mohammad Khalid Imam Rahmani2,*, Sohaib Tahir Chauhdary3, Mujtaba Hussain Jaffery1, Abdulbasid S. Banga2,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1795-1814, 2023, DOI:10.32604/cmc.2023.044140

    Abstract To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting… More >

  • Open Access


    A Feasibility Study of Using Geothermal Energy to Enhance Natural Gas Production from Offshore Gas Hydrate Reservoirs by CO2 Swapping

    Md Nahin Mahmood*, Boyun Guo

    Energy Engineering, Vol.120, No.12, pp. 2707-2720, 2023, DOI:10.32604/ee.2023.042996

    Abstract The energy industry faces a significant challenge in extracting natural gas from offshore natural gas hydrate (NGH) reservoirs, primarily due to the low productivity of wells and the high operational costs involved. The present study offers an assessment of the feasibility of utilizing geothermal energy to augment the production of natural gas from offshore gas hydrate reservoirs through the implementation of the methane-CO2 swapping technique. The present study expands the research scope of the authors beyond their previous publication, which exclusively examined the generation of methane from marine gas hydrates. Specifically, the current investigation explores the… More >

  • Open Access


    Capacity Optimization Configuration of Hydrogen Production System for Offshore Surplus Wind Power

    Yanshan Lu1, Binbin He1, Jun Jiang1, Ruixiao Lin2,*, Xinzhen Zhang2, Zaimin Yang3, Zhi Rao3, Wenchuan Meng3, Siyang Sun3

    Energy Engineering, Vol.120, No.12, pp. 2803-2818, 2023, DOI:10.32604/ee.2023.042328

    Abstract To solve the problem of residual wind power in offshore wind farms, a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power. By studying the mathematical model of wind power output and calculating surplus wind power, as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank, an innovative capacity optimization allocation model was established. The objective of the model was to achieve the lowest total net present value over the entire life cycle.… More >

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