Open Access
EDITORIAL
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 Access
ARTICLE
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 Access
ARTICLE
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 Access
EDITORIAL
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 Access
ARTICLE
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 Access
ARTICLE
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 Access
ARTICLE
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 Access
ARTICLE
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 Access
ARTICLE
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 Access
ARTICLE
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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