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  • Open Access

    ARTICLE

    Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

    Yuanjun Dai, Haonan Li, Baohua Li*

    Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255

    Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

  • Open Access

    ARTICLE

    Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

    Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955

    Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

  • Open Access

    ARTICLE

    Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements: Developments and Validation Using Double Edge-Notched Tensile Specimen

    Troy Myers1, Michael A. Sutton1,*, Hubert Schreier2, Alistair Tofts2, Sreehari Rajan Kattil1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1263-1298, 2024, DOI:10.32604/cmes.2024.048743

    Abstract To compare finite element analysis (FEA) predictions and stereovision digital image correlation (StereoDIC) strain measurements at the same spatial positions throughout a region of interest, a field comparison procedure is developed. The procedure includes (a) conversion of the finite element data into a triangular mesh, (b) selection of a common coordinate system, (c) determination of the rigid body transformation to place both measurements and FEA data in the same system and (d) interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates. For an aluminum Al-6061 double edge More >

  • Open Access

    ARTICLE

    Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

    Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398

    Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

  • Open Access

    CORRECTION

    Correction: Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

    Seoyun Kim1,#, Hyerim Yu2,#, Jeewoo Yoon1,3, Eunil Park1,2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 861-861, 2024, DOI:10.32604/csse.2024.053659

    Abstract This article has no abstract. More >

  • Open Access

    CORRECTION

    Correction: Fine-Tuned Extra Tree Classifier for Thermal Comfort Sensation Prediction

    Ahmad Almadhor1, Chitapong Wechtaisong2,*, Usman Tariq3, Natalia Kryvinska4,*, Abdullah Al Hejaili5, Uzma Ghulam Mohammad6, Mohana Alanazi7

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 855-856, 2024, DOI:10.32604/csse.2024.052412

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

    Maha Farouk Sabir1, Mahmoud Ragab2,3,*, Adil O. Khadidos2, Khaled H. Alyoubi1, Alaa O. Khadidos1,4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 627-643, 2024, DOI:10.32604/csse.2023.041551

    Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops… More >

  • Open Access

    ARTICLE

    Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

    Yinkong Wei1,2, Mucong Wu1,2,*, Wei Wei3, Paulo R. F. Rocha4, Ziyi Cheng1,2, Weifang Yao5

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 723-738, 2024, DOI:10.32604/csse.2023.036062

    Abstract Ultra-high voltage (UHV) transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment. The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid. Yet, the accurate prediction of the ground total electric field remains a technical challenge. In this work, we collected the total electric field data from the Ningdong-Zhejiang ±800 kV UHVDC transmission project, as of the Ling Shao line, and perform an outlier analysis of the More >

  • Open Access

    ARTICLE

    Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

    Do Nang Toan1,*, Lam Thanh Hien2, Ha Manh Toan1, Nguyen Trong Vinh2, Pham Trung Hieu1

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925

    Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

  • Open Access

    ARTICLE

    Cardiovascular Disease Prediction Using Risk Factors: A Comparative Performance Analysis of Machine Learning Models

    Adil Hussain1,*, Ayesha Aslam2

    Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277

    Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

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