Ziyang Wang¹, Lian Duan^1,2,*, Lei Kuang¹, Haibo Zhou¹, Ji’an Duan¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025

Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

Xianfeng Zhou^1,2,*, Ruiju Sun¹, Zhaojie Zhang¹, Yuanyuan Song¹, Lijiao Jin¹, Lin Yuan³

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 503-519, 2025, DOI:10.32604/phyton.2025.060827 - 06 March 2025

Abstract An accurate and robust estimation of leaf chlorophyll content (LCC) is very important to better know the process of material and energy exchange between plants and the environment. Compared with traditional remote sensing methods, abundant research has made progress in agronomic parameter retrieval using different CNN frameworks. Nevertheless, limited reports have paid attention to the problems, i.e., limited measured data, hyperspectral redundancy, and model convergence issues, when concerning CNN models for parameter estimation. Therefore, the present study tried to analyze the effects of synthetic data size expansion employing a Gaussian process regression (GPR) model for… More >

Petr Opěla^1,*, Josef Walek^1,*, Jaromír Kopeček²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >

Guoqing Xu¹, Changsen Xia¹, Jun Qian¹, Guo Ran³, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066 - 28 March 2023

Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and More >

B. Chinthamani^1,*, N. S. Bhuvaneswari², R. Senthil Kumar³, N. R. Shanker⁴

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 515-530, 2022, DOI:10.32604/iasc.2022.025410 - 15 April 2022

Abstract Motor Torque (MT) measurement plays a vital role for evaluating the performance of squirrel cage induction motor during operating conditions. Accurate and continuous measurements of MT provide information regarding driving load capacity, performance degradation of motor, reduces downtime and increases the efficiency. Traditional inline torque sensors-based measurement becomes inaccurate during abrupt change in load during starting condition of motor due to torque spikes. Mounting of torque sensor on motor is a major problem during torque measurement. Improper mounting of sensor acquires signals from other inefficient driveline components such as gearbox, couplings, and bearing. In this… More >

R. Amirthavalli^1,*, S. Thanga Ramya², N. R. Shanker³

Computer Systems Science and Engineering, Vol.41, No.2, pp. 829-847, 2022, DOI:10.32604/csse.2022.020307 - 25 October 2021

Abstract In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected in each node by various water column environmental factors such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity. High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and… More >

Annan Jiang^*, Xinping Guo, Shuai Zheng, Mengfei Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199 - 24 May 2021

Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock.… More >

Fahad Ahmad^1,*, Saleh N. Almuayqil², Mamoona Humayun², Shahid Naseem³, Wasim Ahmad Khan⁴, Kashaf Junaid⁵

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2265-2282, 2021, DOI:10.32604/cmc.2021.013067 - 28 December 2020

Abstract COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental… More >

Shantaram Parab¹, Shreya Srivastava², Pijush Samui³, A. Ramachandra Murthy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.101, No.2, pp. 139-158, 2014, DOI:10.3970/cmes.2014.101.139

Abstract This paper studies the applicability of Gaussian Process Regression (GPR) and Least Squares Support Vector Machines (LSSVM) to predict fracture parameters and failure load (P_max) of high strength and ultra-high strength concrete beams. Fracture characteristics include fracture energy (G_F), critical stress intensity factor (K_IC) and critical crack tip opening displacement (CTOD_C) Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split tensile strength, notch depth, modulus of elasticity and output fracture parameters. Four GPR and four LSSVM models have been developed using MATLAB… More >