Search Results (1,851)

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3^{\circ }$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

This study presents one of the first empirical assessments of artificial intelligence (AI) and machine learning (ML) adoption within architectural academia and the Architecture, Engineering, and Construction (AEC) industry in Saudi Arabia. Using a cross-sectional survey of 113 respondents—60 academics and 53 industry professionals—the research examines familiarity, current usage, perceived benefits, challenges, and future readiness for AI/ML integration. Results show high familiarity and strong perceived importance across both sectors, yet actual implementation remains uneven. Very large firms demonstrate the highest adoption capacity, while small and medium-sized firms face financial and organizational constraints. Academic institutions exhibit moderate familiarity but limited curricular and research integration due to faculty expertise gaps, restricted access to tools, and traditional pedagogical structures. Despite these barriers, both sectors consistently identify AI/ML as critical for enhancing creativity, efficiency, and industry preparedness. The study highlights organizational capacity as the primary determinant of adoption. It concludes with recommendations for curriculum reform, faculty training, industry–academia collaboration, and national policy frameworks to accelerate digital transformation aligned with Saudi Vision 2030. This research establishes a foundational baseline for future longitudinal and comparative studies on AI/ML integration in the regional architectural ecosystem. Full article

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace manufacturing. This paper presents a structured literature review on the automated assembly of large-scale aerospace components, summarizing advances in three core domains: pose adjustment and positioning mechanisms, digital measurement technologies, and trajectory planning and control. Particular emphasis is placed on two cross-cutting themes: measurement uncertainty analysis and flexible assembly, which are critical for high-quality docking. The review classifies pose adjustment mechanisms into four categories (NC positioners, parallel kinematic machines, industrial robots, and novel mechanisms) and digital measurement into five branches (vision metrology, large-scale metrology, measurement field construction, uncertainty analysis, and auxiliary techniques). It also outlines five trajectory planning and control routes, covering traditional methods, multi-sensor fusion, digital twins, flexible assembly, and emerging intelligent approaches. The analysis reveals that current research suffers from fragmentation among mechanism design, metrology, and control, with insufficient integration of uncertainty propagation and flexible deformation modeling. Future systems will rely on heterogeneous equipment collaboration, uncertainty-aware closed-loop control, high-fidelity flexible modeling, and digital twin-driven decision-making. This review provides a unified framework and a technical reference for developing reliable, flexible, and scalable automated assembly systems for next-generation aerospace structures. Full article

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex Morlet Wavelet Phase-Based Video Magnification (CMW-PVM) algorithm. By extracting and manipulating the localized phase of 1D spatial signals, CMW-PVM effectively decouples structural dynamics from background noise while eliminating the computational redundancy associated with 2D spatial pyramid methods. Simulations demonstrate that CMW-PVM significantly extends the linear magnification range (up to $\alpha \approx 35$) while preserving exceptional structural fidelity (FSIM $>0.87$) under severe noise conditions (SNR = 10 dB). Experimental validation against a laser Doppler vibrometer (LDV) reveals near-perfect kinematic accuracy, with a relative amplitude error of only 1.65%. Furthermore, at a 100 Hz high-frequency excitation, the system successfully resolves microscopic displacements (≈10 μm) without temporal aliasing—enabled not by violating sampling theory but by leveraging the high physical line rate of the line-scan sensor. This establishes a robust, non-contact, and computationally efficient paradigm for broadband, micro-amplitude vibration monitoring in industrial environments. Full article

To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article

Deepfakes have emerged as one of the most significant developments in contemporary computational media, representing a sophisticated convergence of machine learning, computer vision, and audiovisual synthesis. Enabled primarily by deep neural networks such as generative adversarial networks (GANs) and transformer-based architectures, Deepfakes are realistic video fabrications through sound and image alteration and substitution that synthesises human likeness, speech, and behaviours. Deepfakes function simultaneously as creative tools, political instruments, security risks, and epistemic disruptors. They have generated widespread scholarly, regulatory, and public concern by contributing to the reshaping of visual communication and posing significant challenges to established norms of authenticity. This entry defines Deepfakes, outlines their technological foundations, synthesises insights from current research and assesses implications for media industries, journalism, documentary, disinformation, governance, and digital culture. Full article

The issues of income inequality and poverty are intrinsically linked and represent persistent global governance challenges. China faced significant hurdles, including absolute rural poverty and a widening urban–rural development gap. The “Targeted Poverty Alleviation” policy (TPA), implemented from 2014 onward, employed comprehensive measures, including household registration, industrial support, and skills training. By the end of 2020, this policy successfully eradicated absolute rural poverty under the prevailing standard, contributing a Chinese solution to global poverty reduction. Beyond addressing absolute deprivation, whether this policy has impacted relative rural poverty and urban–rural inequality remains a subject of debate in existing literature. Utilizing microdata from the China Family Panel Studies (CFPS) from 2014 to 2020, this study employs the Kakwani measure to measure relative deprivation levels, thereby identifying income disparities both within rural areas and between urban and rural regions. Combining empirical tools, including a Difference-in-Differences (DID) framework, Propensity Score Matching (PSM), and Entropy Balancing method, the analysis finds that the Targeted Poverty Alleviation policy significantly curbs income inequality both within rural areas and across the urban–rural divide. Further investigation reveals that this effect operates through three primary mechanisms: promoting diversified rural livelihoods, improving incomes for impoverished households, and bridging knowledge and information gaps. Heterogeneity analysis indicates that the inequality-reducing effect of the policy is more pronounced in non-major grain-producing regions, low-income provinces, and among vulnerable groups such as the elderly, low-income individuals, and women. This study addresses the lack of detailed micro-level measurement, deepens the explanatory analysis of mechanisms and heterogeneity, and provides a basis for formulating differentiated policies in line with the vision of common prosperity. Full article

The increasing deployment of robotic systems in complex and high-stakes environments, such as advanced manufacturing, healthcare, space exploration, and service robotics, requires robust strategies to ensure operational reliability, safety, and predictive maintenance. Real-time prognostics and health management, supported by recent advances in artificial intelligence, has emerged as a powerful approach for monitoring system health, detecting faults, and predicting failures before they occur. Unlike earlier review studies that mainly summarize traditional machine learning applications, the novelty of this paper lies in presenting a comprehensive taxonomy and critical synthesis of state-of-the-art AI-driven PHM techniques designed specifically for robotic systems. We evaluate a wide range of approaches, beginning with conventional machine learning models and extending to recent deep learning advancements, including transformers, vision transformers, and self-supervised learning frameworks. Furthermore, a novel contribution of this study is the rigorous benchmarking of their real-time feasibility, computational complexity, scalability, and performance trade-offs in practical robotic applications. In addition, this review introduces widely used benchmark datasets and highlights representative industrial case studies that demonstrate the practical effectiveness of AI-enabled PHM systems. The study also discusses important research gaps, including challenges related to model interpretability addressed through eXplainable AI, data privacy supported by federated learning, and the integration of cloud and edge computing within cloud robotics frameworks. Through a comprehensive gap matrix and quantitative comparative evaluations, this review provides insights to support the development of resilient, interpretable, and intelligent PHM systems for next-generation robotic applications. Full article

Industrial pouring processes operate under highly dynamic conditions where small deviations can lead to defects, scrap, and production losses. Although modern foundries are equipped with multiple sensors and visual inspection systems, most monitoring approaches remain fragmented, unimodal, and difficult to interpret. Furthermore, annotated anomalous samples in industrial settings are scarce, hindering the development of traditional methods. As a result, many critical pouring anomalies are detected too late or lack sufficient contextual information for effective decision making. In this work, we propose a multimodal framework for industrial scene characterisation that combines visual information and process signals through an explainable Mixture-of-Experts (MoE)-style expert-fusion strategy. First, we deploy an ensemble of specialised modules that collaborate to identify regions of interest, assess pouring quality, and contextualise events within the production process, thereby generating an interpretable description of pouring events. Second, we introduce a novel anomaly detection method for multimodal video data, combining a self-supervised transformer with an outlier-aware clustering algorithm. Our approach effectively identifies rare anomalies without requiring extensive manual labelling. The resulting information is structured into a digital twin-ready representation, supporting synchronisation between the physical system and its virtual counterpart. This solution provides a scalable, deployable pathway to transform heterogeneous industrial data into actionable knowledge, supporting advanced monitoring, anomaly detection, and quality control in real foundry environments. Full article

Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achieved in residential NILM applications, commercial and industrial buildings remain largely underexplored despite their substantial contribution to global energy consumption. This study addresses this gap by developing and evaluating multiple artificial intelligence approaches for energy disaggregation across residential, commercial, and industrial buildings under a unified experimental protocol. We implement and compare several AI-based models, including Vision Transformer (ViT), Variational Autoencoder (VAE), Random Forest (RF), and custom architectures inspired by TimeGPT and Prophet, alongside traditional baseline methods. The proposed framework is validated using three benchmark datasets representing residential (AMPds), commercial (COmBED), and industrial (IMDELD) environments. Experimental results demonstrate that architecture–load interactions, rather than model complexity alone, are the primary determinants of disaggregation accuracy: the ViT-small configuration achieves superior performance for complex industrial loads with R² values exceeding 0.94, Random Forest proves most effective for finite-state commercial HVAC systems with R² up to 0.97, and the Prophet-inspired model excels in capturing seasonal patterns in residential appliances. These findings provide evidence-based guidelines for selecting appropriate AI models based on load characteristics, signal-to-noise ratio, and building type, contributing to the practical deployment of NILM in heterogeneous building environments. Full article

Few-shot industrial anomaly detection technology has received more and more attention because it does not require a large number of abnormal samples to train. Recent few-shot industrial anomaly detection methods commonly fuse multi-stage features from frozen vision transformers for anomaly scoring. However, we find that such direct fusion suffers from cross-stage representation misalignment—shallow and deep features differ significantly in scale and semantic granularity, leading to inconsistent anomaly maps and degraded localization. To address this problem, we propose Calibrated Feature Fusion (CFF), a lightweight adapter that enhances feature fusion via cross-stage representation alignment. The CFF module can be integrated into existing state-of-the-art frameworks and operates effectively in few-shot settings. Experiments on MVTec AD and VisA show that CFF consistently improves the state-of-the-art method across 1/2/4-shot settings, achieving gains of up to +1.6% AUROC and +4.1% AP in pixel-level segmentation. Notably, CFF enhances both precision and recall in four-shot scenarios. Ablation studies confirm that cross-stage alignment is key to stable multi-stage fusion. Full article

Wafer defect analysis is important for semiconductor manufacturing, but labeled data are limited, and class distributions are highly imbalanced. We present a semi-supervised framework with two lightweight hybrid CNN–Transformer models for wafer defect classification and segmentation. For classification, HybridCNN-ViT combines CNN-based local feature extraction with Transformer-based global context modeling, and adopts a three-stage progressive pseudo-labeling strategy to leverage unlabeled samples. The pseudo-label selection mechanism is systematically calibrated to improve pseudo-label reliability under limited labeled data. For segmentation, ConvoFormer-UNet integrates convolution-enhanced embeddings with Transformer blocks to balance boundary detail and global context. On the public WM-811K dataset, HybridCNN-ViT achieves 98.72% accuracy and 0.9985 macro-AUC under the semi-supervised setting for classification, while ConvoFormer-UNet reaches 99.19% IoU for segmentation with fewer parameters than several baselines. We also report efficiency on a single GPU to illustrate practical inference speed. Full article

We developed a Python-integrated closed-loop industrial safety system that bridges real-time helmet-compliance detection with immediate machine control. The custom Python application serves as critical middleware, orchestrating the complete pipeline from You Only Look Once Version 8 computer vision inference to industrial automation by trans-lating AI detection results into Object Linking and Embedding for Process Control Unified Architecture communications with a Mitsubishi programmable logic controller (PLC). The Python framework implements configurable safety policies through polygonal zones with authorized helmet colors, incorporates persistence filtering to prevent nuisance trips, and ensures deterministic translation from probabilistic AI outputs to Boolean PLC con-trol signals. Validation demonstrates reliable, low-latency safety actuation with clear ar-chitectural separation between vision processing, Python-mediated policy enforcement, and PLC-based deterministic control. Full article

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article