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  • Open Access

    ARTICLE

    Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

    Ahmed M. Elmogy1,2,*, Wael M. Elawady2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.032349

    Abstract One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty. Many researchers have been working on developing such type of controllers. One of the most efficient techniques employed to develop such controllers is sliding mode control (SMC). However, the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications. In this paper, the drawbacks of low order traditional sliding mode control (FOTSMC) are resolved by presenting a… More >

  • Open Access

    ARTICLE

    Structural Optimization of Metal and Polymer Ore Conveyor Belt Rollers

    João Pedro Ceniz, Rodrigo de Sá Martins, Marco Antonio Luersen*, Tiago Cousseau

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 601-618, 2022, DOI:10.32604/cmes.2022.021011

    Abstract Ore conveyor belt rollers operate in harsh environments, making them prone to premature failure. Their service lives are highly dependent on the stress field and bearing misalignment angle, for which limit values are defined in a standard. In this work, an optimization methodology using metamodels based on radial basis functions is implemented to reduce the mass of two models of rollers. From a structural point of view, one of the rollers is made completely of metal, while the other also has some components made of polymeric material. The objective of this study is to develop… More >

  • Open Access

    ARTICLE

    Simulation of Oil-Water Flow in a Shale Reservoir Using a Radial Basis Function

    Zenglin Wang1, Liaoyuan Zhang1, Anhai Zhong2, Ran Ding2, Mingjing Lu2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1795-1804, 2022, DOI:10.32604/fdmp.2022.020020

    Abstract Due to the difficulties associated with preprocessing activities and poor grid convergence when simulating shale reservoirs in the context of traditional grid methods, in this study an innovative two-phase oil-water seepage model is elaborated. The modes is based on the radial basis meshless approach and is used to determine the pressure and water saturation in a sample reservoir. Two-dimensional examples demonstrate that, when compared to the finite difference method, the radial basis function method produces less errors and is more accurate in predicting daily oil production. The radial basis function and finite difference methods provide More >

  • Open Access

    ARTICLE

    A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

    S. Sathishkumar1,*, R. Devi Priya2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 129-148, 2023, DOI:10.32604/iasc.2023.023817

    Abstract Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the… More >

  • Open Access

    ARTICLE

    Visualization Detection of Solid–Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

    Ningbo Jing1, Mingqiao Li1, Lang Liu2,*, Yutong Shen1, Peijiao Yang1, Xuebin Qin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 465-476, 2022, DOI:10.32604/cmes.2022.018965

    Abstract During mine filling, the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion. Therefore, the visualization of the inner mine filling of the solid–liquid two-phase flow in the pipeline is important. This paper proposes a method based on capacitance tomography for the visualization of the solid–liquid distribution on the section of a filling pipe. A feedback network is used for electrical capacitance tomography reconstruction. This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance… More >

  • Open Access

    ARTICLE

    Thermogram Adaptive Efficient Model for Breast Cancer Detection Using Fractional Derivative Mask and Hybrid Feature Set in the IoT Environment

    Ritam Sharma1, Janki Ballabh Sharma1, Ranjan Maheshwari1, Praveen Agarwal2,3,4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 923-947, 2022, DOI:10.32604/cmes.2022.016065

    Abstract In this paper, a novel hybrid texture feature set and fractional derivative filter-based breast cancer detection model is introduced. This paper also introduces the application of a histogram of linear bipolar pattern features (HLBP) for breast thermogram classification. Initially, breast tissues are separated by masking operation and filtered by Grmwald–Letnikov fractional derivative-based Sobel mask to enhance the texture and rectify the noise. A novel hybrid feature set using HLBP and other statistical feature sets is derived and reduced by principal component analysis. Radial basis function kernel-based support vector machine is employed for detecting the abnormality… More >

  • Open Access

    ARTICLE

    Forecasting of Trend-Cycle Time Series Using Hybrid Model Linear Regression

    N. Ashwini1,*, V. Nagaveni2, Manoj Kumar Singh3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 893-908, 2022, DOI:10.32604/iasc.2022.022231

    Abstract Forecasting for a time series signal carrying single pattern characteristics can be done properly using function mapping-based principle by a well-designed artificial neural network model. But the performances degraded very much when time series carried the mixture of different patterns characteristics. The level of difficulty increases further when there is a need to predict far time samples. Among several possible mixtures of patterns, the trend-cycle time series is having its importance because of its occurrence in many real-life applications like in electric power generation, fuel consumption and automobile sales. Over the mixed characteristics of patterns,… More >

  • Open Access

    ARTICLE

    Optimal Data Placement and Replication Approach for SIoT with Edge

    B. Prabhu Shankar1,*, S. Chitra2

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 661-676, 2022, DOI:10.32604/csse.2022.019507

    Abstract Social networks (SNs) are sources with extreme number of users around the world who are all sharing data like images, audio, and video to their friends using IoT devices. This concept is the so-called Social Internet of Things (SIot). The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources, and this task demands an efficient storage procedure. For this kind of large volume of data storage, the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low… More >

  • Open Access

    ARTICLE

    Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks

    Hongfei Wei1,*, Hui Zhao2, Xinlong Shi1, Shuang Liang3

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 51-65, 2022, DOI:10.32604/csse.2022.017739

    Abstract A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An in-depth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages… More >

  • Open Access

    ARTICLE

    User Interaction Based Recommender System Using Machine Learning

    R. Sabitha1, S. Vaishnavi2,*, S. Karthik1, R. M. Bhavadharini3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1037-1049, 2022, DOI:10.32604/iasc.2022.018985

    Abstract In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User… More >

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