Qian Zhu¹, Qian Kang¹, Tao Xu², Dengxiu Yu^3,*, Zhen Wang¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1855-1879, 2025, DOI:10.32604/cmc.2025.061913 - 16 April 2025

Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >

Chongzhen Zhang^1,2,*, Zhichen Liu³, Xiangrui Xu³, Fuqiang Hu³, Jiao Dai³, Baigen Cai¹, Wei Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 385-404, 2025, DOI:10.32604/cmes.2024.055596 - 17 December 2024

Abstract In the realm of Intelligent Railway Transportation Systems, effective multi-party collaboration is crucial due to concerns over privacy and data silos. Vertical Federated Learning (VFL) has emerged as a promising approach to facilitate such collaboration, allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data. However, existing works have highlighted VFL’s susceptibility to privacy inference attacks, where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client. This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems. In… More >

Li Wang^1,*, Xiaoyong Wang²

Energy Engineering, Vol.121, No.12, pp. 3953-3979, 2024, DOI:10.32604/ee.2024.056705 - 22 November 2024

Abstract Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and… More >

Haojie Lian¹, Jiaqi Wang¹, Leilei Chen^2,*, Shengze Li³, Ruochen Cao⁴, Qingyuan Hu⁵, Peiyun Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549 - 16 April 2024

Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of More >

Cong Jin¹, Jinjie Huang^1,2,*, Yuanjian Chen¹, Yuqing Gong¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1547-1568, 2024, DOI:10.32604/cmc.2023.047489 - 27 February 2024

Abstract In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a… More >

Qi Liu^1,2, Jing Li^1,2,*, Xianmin Wang^1,*, Wenpeng Zhao¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600 - 21 June 2023

Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further… More >

H. Shalma, P. Selvaraj^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1145-1161, 2023, DOI:10.32604/iasc.2023.036853 - 29 April 2023

Abstract The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images. A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map. 3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps. This work uses the consistency check method to find an accurate depth map for identifying occluded pixels. The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation. The improved… More >

Huma Israr^1,*, Safdar Abbas Khan¹, Muhammad Ali Tahir¹, Muhammad Khuram Shahzad¹, Muneer Ahmad¹, Jasni Mohamad Zain^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814 - 31 March 2023

Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen… More >

Zhengtao Xi¹, Chaofan Song², Jiahui Zheng³, Haifeng Shi³, Zhuqing Jiao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2243-2266, 2023, DOI:10.32604/cmes.2023.023544 - 23 November 2022

Abstract The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution… More > Graphic Abstract

Yi Wu, Zhuojia Fu^*, Jian Min

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 377-393, 2023, DOI:10.32604/cmes.2022.023205 - 29 September 2022

Abstract This paper proposes a modified formulation of the singular boundary method (SBM) by introducing the combined Helmholtz integral equation formulation (CHIEF) and the self-regularization technique to exterior acoustics. In the SBM, the concept of the origin intensity factor (OIF) is introduced to avoid the singularities of the fundamental solutions. The SBM belongs to the meshless boundary collocation methods. The additional use of the CHIEF scheme and the self-regularization technique in the SBM guarantees the unique solution of the exterior acoustics accurately and efficiently. Consequently, by using the SBM coupled with the CHIEF scheme and the More >