Home / Journals / CMES / Vol.134, No.2, 2023
Table of Content
  • Open AccessOpen Access

    EDITORIAL

    Introduction to the Special Issue on Hybrid Intelligent Methods for Forecasting in Resources and Energy Field

    Wei-Chiang Hong1,*, Yi Liang2
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 763-766, 2023, DOI:10.32604/cmes.2022.023022
    (This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    ARTICLE

    Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

    Tingshun Li1, Jiaohui Xu1,*, Zesan Liu2, Dadi Wang2, Wen Tan1
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 767-782, 2023, DOI:10.32604/cmes.2022.020702
    (This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
    Abstract The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are labeled, normalized, and balanced. The… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128
    (This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
    Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized… More >

  • Open AccessOpen Access

    EDITORIAL

    Introduction to the Special Issue on Advances in Neutrosophic and Plithogenic Sets for Engineering and Sciences: Theory, Models, and Applications

    S. A. Edalatpanah1,*, Florentin Smarandache2
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 817-819, 2023, DOI:10.32604/cmes.2022.024060
    (This article belongs to this Special Issue: Advances in Neutrosophic and Plithogenic Sets for Engineering and Sciences: Theory, Models, and Applications (ANPSESTMA))
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    ARTICLE

    Image Representations of Numerical Simulations for Training Neural Networks

    Yiming Zhang1,*, Zhiran Gao1, Xueya Wang1, Qi Liu2
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 821-833, 2023, DOI:10.32604/cmes.2022.022088
    Abstract A large amount of data can partly assure good fitting quality for the trained neural networks. When the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice, numerical simulations can provide a large amount of controlled high quality data. Once the neural networks are trained by such data, they can be used for predicting the properties/responses of the engineering objects instantly, saving the further computing efforts of simulation tools. Correspondingly, a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks… More >

  • Open AccessOpen Access

    ARTICLE

    Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models

    Mohammad Sadegh Barkhordari1, Danial Jahed Armaghani2,*, Panagiotis G. Asteris3
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840
    (This article belongs to this Special Issue: Soft Computing Techniques in Materials Science and Engineering)
    Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are… More >

  • Open AccessOpen Access

    ARTICLE

    Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

    Huiwen Xue1, Jiwei Wen1,*, Akshya Kumar Swain1, Xiaoli Luan1
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 857-871, 2023, DOI:10.32604/cmes.2022.020127
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore,… More >

  • Open AccessOpen Access

    ARTICLE

    State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

    Wentao Liu, Junxia Ma, Weili Xiong*
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565
    (This article belongs to this Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
    Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance. Furthermore, to improve the performance of… More >

  • Open AccessOpen Access

    ARTICLE

    Refined Sparse Representation Based Similar Category Image Retrieval

    Xin Wang, Zhilin Zhu, Zhen Hua*
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287
    (This article belongs to this Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)
    Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

    Passent El-kafrawy1,2, Maie Aboghazalah2,*, Abdelmoty M. Ahmed3, Hanaa Torkey4, Ayman El-Sayed4
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.… More >

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