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  • 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


    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

  • Open Access


    Heterogeneous Ensemble Feature Selection Model (HEFSM) for Big Data Analytics

    M. Priyadharsini1,*, K. Karuppasamy2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2187-2205, 2023, DOI:10.32604/csse.2023.031115

    Abstract Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel… More >

  • Open Access


    A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

    Rajneesh Pareek1, Karthikeyan Rajagopal2, Himanshu Sharma1, Nidhi Gour1, Arun Kumar3, Sami Althahabi4, Haya Mesfer Alshahrani5, Mohamed Mousa6, Manar Ahmed Hamza7,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1637-1648, 2022, DOI:10.32604/cmc.2022.029563

    Abstract Multi and single carrier waveforms are utilized in cellular systems for high-speed data transmission. In The Fifth Generation (5G) system, several waveform techniques based on multi carrier waveforms are proposed. However, the Peak to Average Power Ratio (PAPR) is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the framework. The proposed article documents the study, progress, and implementation of PAPR reduction algorithms for the 5G radio framework. We compare the PAPR algorithm performance for advanced and conventional waveforms. The simulation results reveal that the advanced Partial Transmission More >

  • Open Access


    Model-Free Sliding Mode Control for PMSM Drive System Based on Ultra-Local Model

    Kaihui Zhao1,2, Wenchang Liu1, Tonghuan Yin3, Ruirui Zhou4, Wangke Dai1 and Gang Huang5,*

    Energy Engineering, Vol.119, No.2, pp. 767-780, 2022, DOI:10.32604/EE.2021.018617

    Abstract This paper presents a novel model-free sliding mode control (MFSMC) method to improve the speed response of permanent magnet synchronous machine (PMSM) drive system. The ultra-local model (ULM) is first derived based on the input and the output of the PMSM. Then, the novel MFSMC method is presented, and the controller is designed based on ULM and MFSMC. A sliding mode observer (SMO) is constructed to estimate the unknown part of the ULM. The estimated unknown part is feedbacked to MFSMC controller to perform compensation for parameter perturbations and external disturbances. Compared with the sliding More >

  • Open Access


    An EFSM-Based Test Data Generation Approach in Model-Based Testing

    Muhammad Luqman Mohd-Shafie1,*, Wan Mohd Nasir Wan Kadir1, Muhammad Khatibsyarbini1, Mohd Adham Isa1, Israr Ghani1, Husni Ruslai2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4337-4354, 2022, DOI:10.32604/cmc.2022.023803

    Abstract Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended… More >

  • Open Access


    Thin Plate Bending Analysis and Treatment of Material Discontinuities Using the Generalised RKP-FSM

    M. Khezri1, Z. Vrcelj1, M.A. Bradford1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.87, No.4, pp. 271-306, 2012, DOI:10.3970/cmes.2012.087.271

    Abstract A finite strip method (FSM) utilising the generalised reproducing kernel particle method (RKPM) [Behzadan, Shodja, and Khezri (2011)] is developed for the bending analysis of thin plates. In this innovative approach, the spline functions in the conventional spline finite strip method (SFSM) are replaced with generalised RKPM 1-D shape functions in the longitudinal direction, while the transverse cubic functions which are used in the conventional formulations are retained. Since the generalised RKPM is one of the class of meshfree methods which deal efficiently with derivative-type essential boundary conditions, its introduction in the FSM is beneficial… More >

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