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  • Open Access

    ARTICLE

    Enhancing Power Enterprise Inspection and Supervision: A LoRA-Based Lightweight LLM Framework Integrating Retrieval-Augmented Generation and Prompt Engineering

    Jianfeng Liu1, Yongjiao Yang1, Kangyi Yang1, Changhua Hu1, Zijia Xu1, Qingguo Shi2, Yi Su2,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082804 - 15 June 2026

    Abstract Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base, enhancing output traceability, consistency, and task generalization. A key contribution is the introduction of a Sensitive Information Control Gate, More >

  • Open Access

    ARTICLE

    Attention and Mamba Based Iterative Registration Network for Low-Overlap and Large-Scale Point Cloud

    Haotian Cao1,2, Qingsheng Zhu1,2,3,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081695 - 15 June 2026

    Abstract Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity 𝒪(n2d), leading to rapid increases in computational cost with large-scale point cloud data. To address these problems, an iterative PCR method named Attention and Mamba Based Iterative Registration Network (AMBIR) is proposed, overcoming the shortcomings of the current PCR method on low-overlap and large-scale scenarios. Specifically, an… More >

  • Open Access

    ARTICLE

    Adversarial Example Transfer Method for Vision-Language Pre-Training Models Based on Negative Sample Feature Perturbation

    Zhichao Pei, Ou Ye*, Panyu Yang, Kaiwen He

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081490 - 15 June 2026

    Abstract To address the issue of insufficient transferability of existing adversarial example generation methods for vision-language pre-training (VLP) models, this paper proposes an adversarial example transfer method for VLP models based on negative sample feature perturbation. First, a novel cross-modal collaborative perturbation strategy is constructed. By introducing negative samples into the cross-modal perturbation mechanism, the strategy explores more perturbation directions, breaks the original modal alignment constraints and avoids the local focus of adversarial perturbations. Then, to reduce the computational cost, a dynamic threshold attack strategy is built to measure the modal similarity of the generated adversarial… More >

  • Open Access

    ARTICLE

    An Intelligent Thermal Monitoring Platform for Manufacturing Workshop Power Distribution Systems

    Junyi Wang1, Jianghai Geng1,*, Jiaqi Liu2, Haibin Zhu3

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080895 - 15 June 2026

    Abstract In intelligent manufacturing and remanufacturing systems, the thermal safety of the power distribution infrastructure is crucial for ensuring production continuity, equipment reliability, and operational resilience. Traditional temperature monitoring methods often have problems such as high deployment costs, strong environmental sensitivity, or limited physical interpretability in distributed workshop environments. To address these limitations, this study proposes a physically information-driven intelligent thermal color-changing fault identification framework. Based on thermochromic experiments, irreversible color-changing coatings are selected, which are combined with a visual-based computing pipeline for autonomous overheating detection. The framework proposes a thermal fault temperature identification algorithm based… More >

  • Open Access

    REVIEW

    A Survey of Surface Defect Detection in Machine Vision: Addressing Core Challenges, Methodologies, and Dataset Analysis

    Langyue Zhao1,2, Yubin Yuan3,*, Yiquan Wu2,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080232 - 15 June 2026

    Abstract This paper presents a systematic survey of machine vision-based surface defect detection technologies, focusing on five core challenges in the field: interference from complex backgrounds, small object detection, class imbalance, dynamic scene modeling, and cross-scenario generalization. It reviews key technical approaches corresponding to these challenges over the past five years. Furthermore, a dataset characterization analysis framework is established around these challenges, summarizing and comparing the characteristics of over 40 publicly available datasets across more than ten scenarios, including PCB, photovoltaic, metal, and pavement surfaces. Quantitative selection metrics (such as the small target coefficient and texture More >

  • Open Access

    ARTICLE

    Accurate Real-Time Measurement of Small and Irregular Road Abandoned Objects Using a Lightweight Vision-Based Framework

    Ying Tang1, Chuanyi Ma2, Feng Guo1,*, Wenhao Sun1

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079851 - 15 June 2026

    Abstract Road Abandoned Objects (RAOs) pose significant threats to traffic safety, particularly due to their small size, irregular shapes, and unpredictable distribution in complex road environments. The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment. To achieve this, we propose RAO-YOLO, a lightweight vision-based detection framework built upon an enhanced YOLO architecture. Specifically, a Mixed Aggregation Network (MANet) is introduced to improve multi-scale feature representation, and a Lightweight Shared Detail-Enhanced Detection (LSDD) head is designed to enhance localization accuracy for More >

  • Open Access

    ARTICLE

    NeuroVision: Multimodal Emotion Recognition via Dynamic Frame Enhancement and EEG-Guided Fusion

    Ramakrishna Gandi1,*, Geetha A.1, Ramasubbareddy B.2

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.077569 - 15 June 2026

    Abstract In the fields of affective computing, human-computer interaction, and psychological evaluation, the capacity to recognize emotions is crucial. Unimodal systems in the form of visual systems or of the physiological type are usually not designed to capture the complexity that exists in emotional states. The paper proposes NeuroVision: Multimodal Emotion Recognition System, combining facial video frames information and electroencephalogram (EEG) based information to enhance the accuracy and stability of the system. The system applies ResNet50 on the spatial information of facial expressions, Vision Transformer (ViT) on the temporal movements in the video, and an EEG-MLP… More >

  • Open Access

    EDITORIAL

    Introduction to the Special Issue on Recent Advances in Signal Processing and Computer Vision

    Bo Yang1,*, Chao Liu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083726 - 27 May 2026

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Monitoring of Drill-and-Blast Workflows at the Tunnel Face Using Computer Vision and Context Reasoning

    Chuanjiang Chen1, Junyong Zhou1,*, Binbin Du1, Miaosi Dong2,*, Liwen Zhang1, Bitang Zhu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081546 - 27 May 2026

    Abstract Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a… More >

  • Open Access

    ARTICLE

    GreenShield: A Lightweight and Robust Vision Transformer Framework in Retinal Disease Classification

    Munthir Qasaimeh1, Mostafa Ali1, Qasem Abu Al-Haija2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

    Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

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