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  • Open Access


    Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

    Yifan Huang1, Zikang Zhou1,2, Mingyu Li1, Xuedong Luo1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3147-3165, 2024, DOI:10.32604/cmes.2024.045947

    Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of More >

  • Open Access


    Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

    S. Sridevi*, Jeevaa Katiravan

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 223-233, 2023, DOI:10.32604/iasc.2023.031928

    Abstract Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth… More >

  • Open Access


    Tuning Rules for Fractional Order PID Controller Using Data Analytics

    P. R. Varshini*, S. Baskar, M. Varatharajan, S. Sadhana

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1787-1799, 2022, DOI:10.32604/iasc.2022.024192


    Flexibility and robust performance have made the FOPID (Fractional Order PID) controllers a better choice than PID (Proportional, Integral, Derivative) controllers. But the number of tuning parameters decreases the usage of FOPID controllers. Using synthetic data in available FOPID tuners leads to abnormal controller performances limiting their applicability. Hence, a new tuning methodology involving real-time data and overcomes the drawbacks of mathematical modeling is the need of the hour. This paper proposes a novel FOPID controller tuning methodology using machine learning algorithms. Feed Forward Back Propagation Neural Network (FFBPNN), Multi Least Squares Support Vector Regression

    More >

  • Open Access


    A Prediction Method of Fracture Toughness of Nickel-Based Superalloys

    Yabin Xu1,*, Lulu Cui1, Xiaowei Xu2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 121-132, 2022, DOI:10.32604/csse.2022.022758

    Abstract Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity. To solve these problems of complexity, time-consuming, and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments. A combination prediction model is proposed based on the principle of materials genome engineering, the fracture toughness values of nickel-based superalloys at different temperatures, and different compositions can be predicted based on the existing experimental data. First, to solve the problem of insufficient feature extraction based on manual experience, the Deep Belief Network (DBN) is used to… More >

  • Open Access


    Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

    Yasemin Onal*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 141-156, 2022, DOI:10.32604/csse.2022.020367

    Abstract Predicting the power obtained at the output of the photovoltaic (PV) system is fundamental for the optimum use of the PV system. However, it varies at different times of the day depending on intermittent and nonlinear environmental conditions including solar irradiation, temperature and the wind speed, Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve. In this study, a Gaussian kernel based Support Vector Regression (SVR) prediction model using multiple input variables is proposed for estimating the maximum power obtained from… More >

  • Open Access


    Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

    Amrul Faruq1, Aminaton Marto2, Shahrum Shah Abdullah3,*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 297-306, 2021, DOI:10.32604/csse.2021.017468

    Abstract The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were… More >

  • Open Access


    Grain Yield Predict Based on GRA-AdaBoost-SVR Model

    Diantao Hu, Cong Zhang*, Wenqi Cao, Xintao Lv, Songwu Xie

    Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317

    Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising… More >

  • Open Access


    An Application of Support Vector Regression for Impact Load Estimation Using Fiber Bragg Grating Sensors

    Clyde K Coelho, Cristobal Hiche, Aditi Chattopadhyay

    Structural Durability & Health Monitoring, Vol.7, No.1&2, pp. 65-82, 2011, DOI:10.3970/sdhm.2011.007.065

    Abstract Low velocity impacts on composite plates often create subsurface damage that is difficult to diagnose. Fiber Bragg grating (FBG) sensors can be used to detect subsurface damage in composite laminates due to low velocity impact. This paper focuses on the prediction of impact loading in composite structures as a function of time using a support vector regression approach. A time delay embedding feature extraction scheme is used since it can characterize the dynamics of the impact using the sensor signals. The novelty of this approach is that it can be applied on complex geometries and… More >

  • Open Access


    A Robust Inverse Method Based on Least Square Support Vector Regression for Johnson-cook Material Parameters

    Hu Wang1, Weiyi Li1, Guangyao Li1

    CMC-Computers, Materials & Continua, Vol.28, No.2, pp. 121-146, 2012, DOI:10.3970/cmc.2012.028.121

    Abstract The purpose of this study is to propose a robust inverse method for estimating Johnson-Cook material parameters. The method is shown through illustrative examples for two different advanced high strength steel (AHSS) materials (DP980 and TRIP780) using set of data from impact experiments with different velocities. Compared with widely mixed numerical experimental methods, the suggested inverse method has the capability to guarantee the robustness of the obtained parameters by considering uncertainties. The inverse problem is converted into multi-objective optimization problems. Furthermore, in order to improve the performance in efficiency and accuracy, metamodeling techniques and global More >

  • Open Access


    Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

    Sifan Long1, Ming Zhao2,*, Xinfu He3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 727-760, 2019, DOI:10.32604/cmc.2019.04454

    Abstract With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient… More >

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