Lei Zheng^1,*, Hong Pan^2,3, Yuelei Ruan^2,4, Guoxin Zhang¹, Lei Zhang^1,*, Jianda Xin¹, Zhenyang Zhu¹, Jianyao Zhang^2,5, Wei Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651 - 23 December 2025

Abstract As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to More >

Chen Pan, ChangGyoon Lim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025

Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

Xian Yu, Jianxun Zhang^*, Siran Tian, Xiaobao He

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1883-1897, 2025, DOI:10.32604/cmc.2025.065529 - 29 August 2025

Abstract In recent years, diffusion models have achieved remarkable progress in image generation. However, extending them to text-to-video (T2V) generation remains challenging, particularly in maintaining semantic consistency and visual quality across frames. Existing approaches often overlook the synergy between high-level semantics and low-level texture information, resulting in blurry or temporally inconsistent outputs. To address these issues, we propose Dual Consistency Training (DCT), a novel framework designed to jointly optimize semantic and texture consistency in video generation. Specifically, we introduce a multi-scale spatial adapter to enhance spatial feature extraction, and leverage the complementary strengths of CLIP and More >

Qinglei Zhang, Bai Hu^*, Jiyun Qin, Jianguo Duan, Ying Zhou

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1257-1273, 2025, DOI:10.32604/cmc.2025.059955 - 26 March 2025

Abstract Grasping is one of the most fundamental operations in modern robotics applications. While deep reinforcement learning (DRL) has demonstrated strong potential in robotics, there is too much emphasis on maximizing the cumulative reward in executing tasks, and the potential safety risks are often ignored. In this paper, an optimization method based on safe reinforcement learning (Safe RL) is proposed to address the robotic grasping problem under safety constraints. Specifically, considering the obstacle avoidance constraints of the system, the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process (CMDP). The Lagrange multiplier… More >

Lin Lang¹, Shuang Zhou¹, Minjuan Zhong^1,*, Guang Sun¹, Bin Pan¹, Peng Guo^1,2

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3503-3513, 2023, DOI:10.32604/cmc.2023.023596 - 31 October 2022

Abstract Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in… More >

Kanupriya Mittal^*, V. Mary Anita Rajam

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1907-1921, 2023, DOI:10.32604/iasc.2023.029037 - 19 July 2022

Abstract An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed More >