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  • Open Access

    ARTICLE

    Surge Fault Detection of Aeroengines Based on Fusion Neural Network

    Desheng Zheng1, Xiaolan Tang1,*, Xinlong Wu1, Kexin Zhang1, Chao Lu2, Lulu Tian3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 815-826, 2021, DOI:10.32604/iasc.2021.017737 - 01 July 2021

    Abstract Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor More >

  • Open Access

    ARTICLE

    Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

    Ajla Kulaglic1,*, Burak Berk Ustundag2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3577-3593, 2021, DOI:10.32604/cmc.2021.014768 - 06 May 2021

    Abstract Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using… More >

  • Open Access

    ARTICLE

    Multi-Span and Multiple Relevant Time Series Prediction Based on Neighborhood Rough Set

    Xiaoli Li1, Shuailing Zhou1, Zixu An2,*, Zhenlong Du1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3765-3780, 2021, DOI:10.32604/cmc.2021.012422 - 01 March 2021

    Abstract Rough set theory has been widely researched for time series prediction problems such as rainfall runoff. Accurate forecasting of rainfall runoff is a long standing but still mostly significant problem for water resource planning and management, reservoir and river regulation. Most research is focused on constructing the better model for improving prediction accuracy. In this paper, a rainfall runoff forecast model based on the variable-precision fuzzy neighborhood rough set (VPFNRS) is constructed to predict Watershed runoff value. Fuzzy neighborhood rough set define the fuzzy decision of a sample by using the concept of fuzzy neighborhood.… More >

  • Open Access

    ARTICLE

    Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

    Bilal Shoaib1, Yasir Javed2, Muhammad Adnan Khan3,*, Fahad Ahmad4, Rizwan Majeed5, Muhammad Saqib Nawaz1, Muhammad Adeel Ashraf6, Abid Iqbal2, Muhammad Idrees7

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1413-1427, 2021, DOI:10.32604/cmc.2021.015099 - 05 February 2021

    Abstract For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and… More >

  • Open Access

    ARTICLE

    Brent Oil Price Prediction Using Bi-LSTM Network

    Anh H. Vo1, Trang Nguyen2, Tuong Le1,3,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1307-1317, 2020, DOI:10.32604/iasc.2020.013189 - 24 December 2020

    Abstract Brent oil price fluctuates continuously causing instability in the economy. Therefore, it is essential to accurately predict the trend of oil prices, as it helps to improve profits for investors and benefits the community at large. Oil prices usually fluctuate over time as a time series and as such several sequence-based models can be used to predict them. Hence, this study proposes an efficient model named BOP-BL based on Bidirectional Long Short-Term Memory (Bi-LSTM) for oil price prediction. The proposed framework consists of two modules as follows: The first module has three Bi-LSTM layers which… More >

  • Open Access

    ARTICLE

    An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

    Jieren Cheng1,2, Ruomeng Xu1,*, Xiangyan Tang1, Victor S. Sheng3, Canting Cai1

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

    Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and… More >

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