Sudipta Debnath¹, Zahir Uddin Ahmed², Muhammad Ikhlaq^3,4,*, Md. Tanvir Khan⁵, Avneet Kaur⁶, Kuljeet Singh Grewal¹

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 71-94, 2025, DOI:10.32604/fhmt.2024.059734 - 26 February 2025

Abstract Impinging jet arrays are extensively used in numerous industrial operations, including the cooling of electronics, turbine blades, and other high-heat flux systems because of their superior heat transfer capabilities. Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution, which can lead to improved system performance and energy savings. This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system. The governing equations are resolved employing the commercial computational fluid dynamics (CFD) software ANSYS Fluent v17. The study focuses on four… More > Graphic Abstract

Hongliang Hao¹, Caifeng Wen^2,3, Feifei Xue^2,*, Hao Qiu¹, Ning Yang², Yuwen Zhang¹, Chaoyu Wang¹, Edwin E. Nyakilla¹

Energy Engineering, Vol.122, No.1, pp. 285-306, 2025, DOI:10.32604/ee.2024.057720 - 27 December 2024

Abstract Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources. This paper proposes a wind-solar hybrid energy storage system (HESS) to ensure a stable supply grid for a longer period. A multi-objective genetic algorithm (MOGA) and state of charge (SOC) region division for the batteries are introduced to solve the objective function and configuration of the system capacity, respectively. MATLAB/Simulink was used for simulation test. The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system, with a combination of a 300 More >

Umapathi Krishnamoorthy^1,*, Shanmugam Jagan², Mohammed Zakariah³, Abdulaziz S. Almazyad^4,*, K. Gurunathan⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024

Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >

Van-Truong Nguyen^1,*, Dai-Nhan Duong¹, Dinh-Hieu Phan¹, Thanh-Lam Bui¹, Xiem HoangVan², Phan Xuan Tan³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2337-2356, 2024, DOI:10.32604/cmc.2024.055412 - 18 November 2024

Abstract This paper proposes an adaptive nonlinear proportional-derivative (ANPD) controller for a two-wheeled self-balancing robot (TWSB) modeled by the Lagrange equation with external forces. The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative (NPD) controller and a genetic algorithm, in which the proportional-derivative (PD) parameters are updated online based on the tracking error and the preset error threshold. In addition, the genetic algorithm is employed to adaptively select initial controller parameters, contributing to system stability and improved control accuracy. The proposed controller is basic in design yet simple to implement. The… More >

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

Yuezheng Lan¹, Yu Zhao^2,*, Zhiping Zhai¹, Meihua Fan², Fushun Li²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2565-2583, 2024, DOI:10.32604/fdmp.2024.053362 - 28 October 2024

Abstract The quality of crushing, power consumption, and discharging performance of a straw crusher are greatly influenced by the characteristics of its internal flow field. To enhance the straw crusher’s flow field properties and improve the efficiency with which crushed material is discharged, first, the main structural parameters influencing the air flow in the crusher are discussed. Then, the coupled gas-solid flow field in the straw crusher is numerically calculated through solution of the Navier-Stokes equations and application of the discrete element method (DEM). Finally, the discharge performance index of the crusher is examined through detailed More >

Guo Zhao^1,2, Chi Zhang^1,2,*, Qiyuan Ren^1,2

Energy Engineering, Vol.121, No.11, pp. 3355-3379, 2024, DOI:10.32604/ee.2024.053130 - 21 October 2024

Abstract In the context of China’s “double carbon” goals and rural revitalization strategy, the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids. Considering the operational characteristics of rural microgrids and their impact on users, this paper establishes a two-layer scheduling model incorporating flexible loads. The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid, while the lower-layer aims to minimize the total electricity cost for rural users. An Improved Adaptive Genetic Algorithm (IAGA) is proposed to solve the model. Results show that the two-layer scheduling model with More >

Jingfa Ma, Hu Liu^*, Lingxiao Chen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 443-469, 2024, DOI:10.32604/cmc.2024.056209 - 15 October 2024

Abstract Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO₂ emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling… More >

Sayat Ibrayev¹, Batyrkhan Omarov^1,2,3,*, Arman Ibrayeva¹, Zeinel Momynkulov^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 229-249, 2024, DOI:10.32604/cmc.2024.053075 - 15 October 2024

Abstract This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II (Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II) for solving complex multi-objective optimization problems, with a particular focus on robotic leg-linkage design. The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II, aiming to enhance the efficiency and precision of the optimization process. Through a series of empirical experiments and algorithmic analyses, the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from… More >

Zakir Hussain Ahmed^1,*, Maha Ata Al-Furhood², Abdul Khader Jilani Saudagar³, Shakir Khan⁴

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1113-1131, 2024, DOI:10.32604/csse.2024.053574 - 13 September 2024

Abstract The generalized travelling salesman problem (GTSP), a generalization of the well-known travelling salesman problem (TSP), is considered for our study. Since the GTSP is NP-hard and very complex, finding exact solutions is highly expensive, we will develop genetic algorithms (GAs) to obtain heuristic solutions to the problem. In GAs, as the crossover is a very important process, the crossover methods proposed for the traditional TSP could be adapted for the GTSP. The sequential constructive crossover (SCX) and three other operators are adapted to use in GAs to solve the GTSP. The effectiveness of GA using More >