Peicheng Shi^1,*, Yueyue Tang¹, Yi Li¹, Xinlong Dong¹, Yu Sun², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3321-3343, 2025, DOI:10.32604/cmc.2025.065297 - 03 July 2025

Abstract In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the… More >

Nan Li^1,2,*, Jingxiong Dong², Liang Tao³, Liang Huang³

Energy Engineering, Vol.122, No.7, pp. 2957-2975, 2025, DOI:10.32604/ee.2025.062667 - 27 June 2025

Abstract With the continuous expansion and increasing complexity of power system scales, the binary classification for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation. Therefore, this paper proposes a multi-class transient stability assessment model based on an improved Transformer. The model is designed with a dual-tower encoder structure: one encoder focuses on the time dependency of data, while the other focuses on the dynamic correlations between variables. Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction… More > Graphic Abstract

Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1839-1861, 2025, DOI:10.32604/cmc.2025.064489 - 09 June 2025

Abstract Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator… More >

Mingen Zhong¹, Kaibo Yang^1,*, Ziji Xiao¹, Jiawei Tan², Kang Fan², Zhiying Deng¹, Mengli Zhou¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1055-1071, 2025, DOI:10.32604/cmc.2025.063383 - 09 June 2025

Abstract With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video… More >

Irbek Morgoev¹, Roman Klyuev^2,*, Angelika Morgoeva¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025

Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

Jing He¹, Haonan Zhu², Chenhao Zhao¹, Minrui Zhao^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4551-4573, 2025, DOI:10.32604/cmc.2025.062437 - 19 May 2025

Abstract Self-supervised monocular depth estimation has emerged as a major research focus in recent years, primarily due to the elimination of ground-truth depth dependence. However, the prevailing architectures in this domain suffer from inherent limitations: existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions. These assumptions are often violated in real-world scenarios due to dynamic objects, non-Lambertian reflectance, and unstructured background elements, leading to pervasive artifacts such as depth discontinuities (“holes”), structural collapse, and ambiguous reconstruction. To address these challenges, we propose a novel framework that integrates scene dynamic pose estimation into… More >

Zhengdao Yang¹, Xuewei Wang², Yuling Chen^1,*, Hui Dou¹, Haiwei Sang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5181-5197, 2025, DOI:10.32604/cmc.2025.060564 - 19 May 2025

Abstract Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these More >

Kun Liu¹, Chen Bao^1,*, Sidong Liu²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025

Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >

Thinh D. Le, Duong Q. Nguyen, Phuong D. Nguyen, H. Nguyen-Xuan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1459-1479, 2025, DOI:10.32604/cmes.2024.056753 - 27 January 2025

Abstract Research on reconstructing imperfect faces is a challenging task. In this study, we explore a data-driven approach using a pre-trained MICA (MetrIC fAce) model combined with 3D printing to address this challenge. We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction. Our results demonstrate high accuracy, evaluated by the geometric loss function and various statistical measures. To showcase the effectiveness of the approach, we used 3D printing to create a model that covers facial wounds. The More >

Jiawei Tian¹, Yu Zhou¹, Xiaobing Chen², Salman A. AlQahtani³, Hongrong Chen⁴, Bo Yang^4,*, Siyu Lu⁴, Wenfeng Zheng^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 209-229, 2025, DOI:10.32604/cmes.2024.057032 - 17 December 2024

Abstract Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination, hindering accurate three-dimensional lesion reconstruction by surgical robots. This study proposes a novel end-to-end disparity estimation model to address these challenges. Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions, integrating multi-scale image information to enhance robustness against lighting interferences. This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison, improving accuracy and efficiency. The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot, comprising More >