Zhiyuan Zhang¹, Yongjun Wu¹, Xiqin Li¹, Minghui Song¹, Guangwu Zhang², Ziren Wang^3,*, Wei Li³

Energy Engineering, Vol.122, No.5, pp. 1919-1948, 2025, DOI:10.32604/ee.2025.063178 - 25 April 2025

Abstract The park-level integrated energy system (PIES) is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration. However, current carbon trading mechanisms lack sufficient incentives for emission reductions, and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling. To address these issues, this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration, hydrogen utilization, and the Secretary Bird Optimization Algorithm (SBOA). Key innovations include: (1) A dynamic reward-penalty carbon trading mechanism with coefficients (μ = 0.2,… More >

Mengjiao Wei^1,*, Wenyu Liu²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2505-2523, 2025, DOI:10.32604/cmc.2025.060887 - 16 April 2025

Abstract In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the… More >

Qingrong Zeng, Xiaochen Liu, Xuefeng Zhu^*, Xiangkui Zhang, Ping Hu

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2065-2085, 2024, DOI:10.32604/cmes.2024.052620 - 31 October 2024

Abstract Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we More >

Jie Ma¹, Pengcheng Yue², Haozheng Yu¹, Yuqing Zhang³, Youwen Zhang¹, Cuiping Li³, Junhui Li^3,*, Wenwen Qin³, Yong Guo¹

Energy Engineering, Vol.121, No.7, pp. 1823-1846, 2024, DOI:10.32604/ee.2024.048225 - 11 June 2024

Abstract In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system… More >

Huaxin Zhou^1,2, Ziying Fang³, Yilin Wang⁴, Mengjun Tong^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 175-194, 2023, DOI:10.32604/cmc.2023.037342 - 08 June 2023

Abstract Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data.… More >

Louay Abd Al-Azez Mahdi, Mohammed A. Fayad, Miqdam T. Chaichan^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2201-2214, 2023, DOI:10.32604/fdmp.2023.027166 - 16 May 2023

Abstract A theoretical study based on the Penalty factor (PF) method by Cavallini et al. is conducted to show that the pressure drop occurring in a wire-on-tube heat exchanger can be converted into a temperature difference for two types of refrigerants R-134a and R-600a typically used for charging refrigerators and freezers. The following conditions are considered: stratified or stratified-wavy flow condensation occurring inside the smooth tube of a wire-on-tube condenser with diameter 3.25, 4.83, and 6.299 mm, condensation temperatures 35°C, 45°C, and 54.4°C and cover refrigerant mass flow rate spanning the interval from 1 to 7 kg/hr.… More > Graphic Abstract

Wenyuan Wang^1,#, Shuili Luo^1,#, Everett L. Worthington Jr², Haijiang Li^1,3,*

International Journal of Mental Health Promotion, Vol.25, No.4, pp. 539-550, 2023, DOI:10.32604/ijmhp.2023.025634 - 01 March 2023

Abstract Previous studies have found that self-control training was effective in improving an individual’s self-control, which plays an important role in inhibiting negative emotions. However, it is unclear whether self-control training can facilitate refraining from retaliation. This study randomly assigned participants (N = 55) to a training condition (building self-control by avoiding sweets) or a control condition. Before and after training, participants completed the Transgression-Related Interpersonal Motivations Inventory-18 (TRIM-18) and a modified Taylor aggression task once each. Participants in the training condition inflicted more low-intensity penalties on the previous offender compared to control participants. Participants in the training More >

Anubha¹, Ravneet Preet Singh Bedi², Arfat Ahmad Khan^3,*, Mohd Anul Haq⁴, Ahmad Alhussen⁵, Zamil S. Alzamil⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1331-1351, 2023, DOI:10.32604/cmc.2023.033181 - 06 February 2023

Abstract Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while… More >

Wang Ning^1,*, Siliang Chen^2,*, Fu Qiang², Haitao Tang², Shen Jie²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5951-5965, 2023, DOI:10.32604/cmc.2023.035558 - 28 December 2022

Abstract With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly,… More >

Xiaojun Zhu^1,2,3, Heming Huang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2155-2172, 2023, DOI:10.32604/cmes.2023.021453 - 23 November 2022

Abstract Recently, speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals. However, the training of Generative Adversarial Networks has such problems as convergence difficulty, model collapse, etc. In this work, an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed, and some improvements have been made in order to get faster convergence speed and better generated speech quality. Specifically, in the generator coding part, each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales; a gated More >