Mehdi Jamei¹, Nadjem Bailek^2,*, Kada Bouchouicha³, Muhammed A. Hassan⁴, Ahmed Elbeltagi⁵, Alban Kuriqi⁶, Nadhir Al-Ansar⁷, Javier Almorox⁸, El-Sayed M. El-kenawy^9,10

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1625-1640, 2023, DOI:10.32604/cmc.2023.031406 - 22 September 2022

Abstract Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF),… More >

Hongfei Ma^1,*, Wenqi Zhao², Yurong Zhao¹, Yu He¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498 - 20 September 2022

Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by More >

Aishah Alrashidi, Ali Mustafa Qamar^*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 1973-1988, 2023, DOI:10.32604/csse.2023.024633 - 01 August 2022

Abstract Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for… More >

Guowei Zhu^*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1521-1528, 2022, DOI:10.32604/fdmp.2022.020942 - 27 May 2022

Abstract In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. More >

Jiefeng Chen¹, Lisha Ding¹, Pengyu Wang¹, Weijin Zhang², Jie Li³, Badr A. Mohamed⁴, Jie Chen¹, Songqi Leng¹, Tonggui Liu¹, Lijian Leng^2,*, Wenguang Zhou^1,*

Journal of Renewable Materials, Vol.10, No.6, pp. 1555-1574, 2022, DOI:10.32604/jrm.2022.018625 - 20 January 2022

Abstract Biomass is a carbon-neutral renewable energy resource. Biochar produced from biomass pyrolysis exhibits preferable characteristics and potential for fossil fuel substitution. For time- and cost-saving, it is vital to establish predictive models to predict biochar properties. However, limited studies focused on the accurate prediction of HHV of biochar by using proximate and ultimate analysis results of various biochar. Therefore, the multi-linear regression (MLR) and the machine learning (ML) models were developed to predict the measured HHV of biochar from the experiment data of this study. In detail, 52 types of biochars were produced by pyrolysis… More >

Seyoung Kim¹, Jeonghoon Choi¹, Kwang Ryel Ryu^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 207-220, 2022, DOI:10.32604/iasc.2022.021752 - 26 October 2021

Abstract Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation More >

Michal Prauzek^*, Tereza Paterova, Jaromir Konecny, Radek Martinek

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2601-2618, 2022, DOI:10.32604/cmc.2022.019705 - 27 September 2021

Abstract Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which… More >

Simon Martinez¹, Jürgen Lenz¹, Hans Schindler^1,2, Willi Wendler¹, Stefan Rues³, Karl Schweizerhof^1,*, Sophia Terebesi², Nikolaos Nikitas Giannakopoulos², Marc Schmitter²

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1259-1281, 2021, DOI:10.32604/cmes.2021.017422 - 25 November 2021

Abstract The occlusal design plays a decisive role in the fabrication of dental restorations. Dentists and dental technicians depend on mechanical simulations of mandibular movement that are as accurate as possible, in particular, to produce interference-free yet chewing-efficient dental restorations. For this, kinetic data must be available, i.e., movements and deformations under the influence of forces and stresses. In the present study, so-called functional data were collected from healthy volunteers to provide consistent information for proper kinetics. For the latter purpose, biting and chewing forces, electrical muscle activity and jaw movements were registered synchronously, and individual More >

Yuji Saito^*, Keigo Yamada, Naoki Kanda, Kumi Nakai, Takayuki Nagata, Taku Nonomura, Keisuke Asai

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 1-30, 2021, DOI:10.32604/cmes.2021.016603 - 24 August 2021

Abstract A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches: a greedy method based on D-optimality and a noise-robust greedy method in this paper. Extensions of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement. More >

Jiaxiang Luo^1,2, Yu Li², Weien Zhou², Zhiqiang Gong², Zeyu Zhang¹, Wen Yao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 823-848, 2021, DOI:10.32604/cmes.2021.016737 - 11 August 2021

Abstract Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years. However, the loss function of the above method is mainly based on pixel-wise errors from the image perspective, which cannot embed the physical knowledge of topology optimization. Therefore, this paper presents an improved deep learning model to alleviate the above difficulty effectively. The feature pyramid network (FPN), a kind of deep learning model, is trained to learn the inherent physical law of topology optimization itself, of which the loss function is composed of pixel-wise errors and physical More >