Search Results (166)

In modern industrial environments, camera-based monitoring is essential for workflow optimization, safety, and process control, yet it raises significant privacy concerns when people are recorded. Realistic full-body anonymization offers a potential solution by obscuring visual identity while preserving information needed for automated analysis. Whether such methods also conceal biometric traits from human pose and gait remains uncertain, although these biomarkers enable person identification without appearance cues. This study investigates the impact of full-body anonymization on gait-related identity recognition using DeepPrivacy2 and a custom CCTV-like industrial dataset comprising original and anonymized sequences. This study provides the first systematic evaluation of whether pose-preserving anonymization disrupts identity-relevant gait characteristics. The analysis quantifies keypoint shifts introduced by anonymization, examines their influence on downstream gait-based person identification, and tests cross-domain linkability between original and anonymized recordings. Identification accuracy, domain transfer between data types, and distortions in derived pose keypoints are measured to assess anonymization effects while retaining operational utility. Findings show that anonymization removes appearance but leaves gait identity largely intact, indicating that pose-driven anonymization is insufficient for privacy protection. Effective privacy requires anonymization strategies that explicitly target gait characteristics or incorporate domain-adaptation mechanisms. Full article

Honey bees are essential both for many global ecosystems and apicultural production. The management of bee colonies remains labour-intensive, which drives a need for automated solutions. This work presents a proof-of-concept system to monitor honey bee activity by identifying the yearly lifecycle stages exhibited by the colony. A non-invasive vibration monitoring system was developed and placed on top of brood frames in Warsaw-type beehives to collect vibration data over a full apicultural season. The recorded vibration signals were analyzed using both Convolutional Neural Networks (CNNs) and classical machine learning approaches such as the extra trees method. Recursive Feature Elimination with Cross-Validation (RFECV) was performed to isolate the most important frequency bins for lifecycle period identification. The results demonstrate that the critical frequencies for recognizing yearly honey bee activity are concentrated below 1 kHz. The proposed machine learning models achieved a weighted accuracy score of over 95%. These findings have significant implications for future bee monitoring hardware design, indicating that sampling frequencies may be reduced to as low as 2 kHz without significantly compromising model accuracy. Full article

Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture. Full article

Extensive research on path planning and automated navigation has been carried out for weeding robots in fields such as corn, soybean, wheat, and sugar beet, but until now, no literature reports relative studies in turfs that are not cultivated using row-crop methods. This paper proposes a practical solution that comprises path planning and path tracking to minimize the weeding robot’s travel distance in turfs for the first time. An inter-sub-region scheduling algorithm is developed using the Traveling Salesman Problem (TSP) model, followed by a boundary-shifting-based coverage path planning algorithm to achieve full coverage within each weed subregion. For path tracking, a Real-Time Kinematic Global Positioning System (RTK-GPS) fusion positioning method is developed and combined with a dynamic pure pursuit algorithm featuring a variable preview distance to enable precise path following. After path planning based on real-world site data, the weeding robot traverses all weed subregions via the shortest possible path. Field experiments showed that the robot traveled along the shortest path at speeds of 0.6, 0.8, and 1.0 m/s; the root mean square errors of autonomous navigation deviation were 0.35, 0.81, and 1.41 cm, respectively. The proposed autonomous navigation solution significantly reduces the robot’s travel distance while maintaining acceptable tracking accuracy. Full article

Aiming at the technical challenges of large dust interference, complex measurement parameters, and high real-time requirements in the automated sampling scenario of iron powder transportation vehicles, a method for external dimension detection that integrates laser radar and multi-algorithm collaboration is proposed. By improving ICP point cloud registration, Moving Least Squares surface reconstruction (MLS+), and Gaussian mixture model (GMM-EM) algorithms, the full process automation measurement of carriage length/width/height, top angle coordinates, and reinforcement positions is achieved. Experiments have shown that the system maintains a stable measurement error within ±5 cm and a single-frame processing time of ≤2.1 s in environments with PM2.5 ≤ 500 μg/m³, providing an innovative solution for intelligent detection in industrial scenarios. Full article

Pipe leakage and bursts are the primary contributors to water losses in water distribution networks (WDNs). However, the use of object detection techniques for identifying such failures is underexplored. This study proposes a novel deep-learning-based framework for pipe burst detection and localization (PBD&L) within WDNs. The framework employs spatial encoding of pressure fields obtained from hydraulic simulations of normal and burst scenarios. These encoded images serve as inputs to a faster region-based convolutional neural network (Faster R-CNN) object detection model, specifically designed for infrastructure monitoring. The framework was tested on three WDNs—Fossolo, PB23, and CM53—under varying sensor coverages (100%, 75%, and 50%). The results indicate that the model consistently achieves high detection accuracy across different network configurations, even with limited sensor availability. For Fossolo and PB23, the model demonstrated stable performance; however, for the CM53 network, accuracy decreased at full sensor coverage, possibly owing to overfitting or signal redundancy. Overall, the proposed method presents a robust solution for PBD&L in WDNs, showcasing significant practical applicability. Its ability to maintain high performance under partial observability and diverse network conditions demonstrates its potential for integration into real-time smart water management systems, enabling automated monitoring, rapid response, and improved operational efficiency. Full article

Additive manufacturing (AM), commonly known as 3D printing, is revolutionizing modern dentistry, introducing high-precision, patient-specific, and digital-driven workflows across prosthodontics, orthodontics, implantology, and maxillofacial surgery. Extensive analysis explores the leading platforms in 3D printing such as stereolithography (SLA), fused deposition modeling (FDM), selective laser sintering (SLS), digital light processing (DLP), and PolyJet which all achieve superior performance across multiple areas including resolution capabilities, material compatibility options, clinical application readiness, and cost-effectiveness. Additionally, an extensive overview of common materials, including biocompatible polymers (PLA, PMMA, PEEK), metals (titanium, cobalt-chromium), and ceramics (zirconia, alumina, glass-ceramics), sheds light on the critical role of material selection for patient safety, durability, and functional performance. The review explores new advancements such as 4D printing with shape-adaptive smart biomaterials as well as artificial intelligence-enabled digital processes and prosthesis design for the transformation of regenerative dentistry and intraoral drug delivery operations into new domains and the automation of clinical planning. Equally groundbreaking are 3D printing applications in pediatric dentistry, surgical simulation, and dental education. However, full-scale adoption of AM technology is not without challenges, including material toxicity, regulatory hurdles for approval, high initial investments, and the need for extensive digital expertise training. Sustainability concerns are also being addressed, with recycled materials and circular economy models gaining traction. In conclusion, this article advocates for a future where dentistry is shaped by interdisciplinary collaboration, intelligent automation, and hyper-personalized biocompatible solutions, with 3D printing firmly established as the backbone of next-generation dental care. Full article

Accurate and automated credit assessment systems are fundamental to the integrity of financial ecosystems, underpinning responsible lending, risk mitigation, and sustainable economic growth. In light of persistent economic uncertainties and an increasing frequency of credit defaults, financial entities face urgent demands for robust and scalable risk evaluation tools. While a diverse array of statistical and machine learning techniques have been proposed for credit scoring, prevailing methods remain labor-intensive and operationally cumbersome. This paper introduces VeriCred, a novel credit evaluation framework that synergistically combines automated machine learning with blockchain-based oversight to overcome these limitations. The proposed approach incorporates a data augmentation strategy to enrich limited and heterogeneous credit datasets, thereby improving model generalization. A distinctive blockchain layer is embedded to immutably trace data provenance and model decisions, ensuring full auditability. By orchestrating the end-to-end workflow—including feature extraction, hyperparameter optimization, and model selection—within a unified AutoML pipeline, the system drastically reduces manual dependency. Architecturally, the framework introduces C-NAS, a neural architecture search mechanism customized for credit risk prediction, alongside A-Triplet loss, an objective function tailored to refine feature discrimination. To address opacity concerns, an interpretability component elucidates feature contributions and model reasoning. Empirical evaluations demonstrate that VeriCred achieves superior predictive accuracy with significantly reduced computational overhead, offering financial institutions a transparent, efficient, and trustworthy credit scoring solution. Full article

Pipeline joint coating is key to maintaining the integrity and service life of oil and gas pipelines. This study presents a novel full-process automatic joint-coating system, comprising a modular design of a universal chassis and four operational modules: abrasive blasting, medium-frequency heating, primer spraying, and heat-shrink-tape wrapping. The innovation lies in its axial obstacle-crossing mechanism, automated opening/closing device, and circumferential rotation system, enabling semi-automated joint-coating operations with the potential for full automation in future iterations. Finite element simulations confirmed the structural strength and safety margins of critical components under operational loads. Experimental validation demonstrated that pre-heating to 120 °C via 5 kHz heating took only 2 min (versus 3 min at 4 kHz and over 5 min at 3 kHz) and that primer-spraying parameters (nozzle height/travel speed) produced uniform coating thickness above 400 µm. Adhesion tests at pipe temperatures above 200 °C and rolling speeds ≤ 16 mm/s consistently exceeded 100 N/cm, while speeds above 20 mm/s caused defects. The system therefore offers a reliable engineering solution for high-efficiency, reproducible pipeline joint-coating operations. Full article

Ensuring food safety and quality is critical in the food-processing industry, where the detection of contaminants remains a persistent challenge. This study assesses the feasibility of hyperspectral imaging (HSI) for detecting foreign objects on pork belly meat. A Specim FX17 hyperspectral camera was used to capture data across various bands in the near-infrared spectrum (900–1700 nm), enabling identification of contaminants that are often missed by traditional visual inspection methods. The proposed solution combines a segmentation approach based on a lightweight Vision Transformer with specific pre- and post-processing strategies to distinguish contaminants from meat, fat, and conveyor belt, while emphasizing on a low false-positive rate. On a test set of 55 images with contaminants, the method retained most true positives; on 183 clean images, the full pipeline eliminated all false positives. Across 208 additional images acquired under production-line temperature variation (10–55 °C), only one image exhibited small false positives, and on a challenging 95-image set with fat-like spectra the pipeline produced zero false positives. These results demonstrate high detection accuracy and training efficiency while addressing issues such as noise, temperature drift, and spectral similarity. The findings support the feasibility of real-time HSI for automated quality control. Full article

Cloud-centric IoT frameworks remain dominant; however, they introduce major challenges related to data privacy, latency, and system resilience. Existing open-source solutions often lack standardized principles for scalable, local-first deployment and do not adequately integrate fault tolerance with hybrid automation logic. This study presents a practical and extensible local-first IoT architecture designed for full operational autonomy using open-source components. The proposed system features a modular, layered design that includes device, communication, data, management, service, security, and presentation layers. It integrates MQTT, Zigbee, REST, and WebSocket protocols to enable reliable publish–subscribe and request–response communication among heterogeneous devices. A hybrid automation model combines rule-based logic with lightweight data-driven routines for context-aware decision-making. The implementation uses Proxmox-based virtualization with Home Assistant as the core automation engine and operates entirely offline, ensuring privacy and continuity without cloud dependency. The architecture was deployed in a real-world office environment and evaluated under workload and fault-injection scenarios. Results demonstrate stable operation with MQTT throughput exceeding 360,000 messages without packet loss, automatic recovery from simulated failures within three minutes, and energy savings of approximately 28% compared to baseline manual control. Compared to established frameworks such as FIWARE and IoT-A, the proposed approach achieves enhanced modularity, local autonomy, and hybrid control capabilities, offering a reproducible model for privacy-sensitive smart environments. Full article

This paper addresses the challenge of improving the thermal performance of building envelopes in hot arid climates by identifying optimal configurations for biomimetic opaque ventilated façade (OVF) designs. To overcome the complexity of parameter interactions in such systems, a multi-objective optimization framework is developed using computational fluid dynamics (CFD) simulations integrated with parametric modeling and machine learning surrogate models. A central contribution of this research is the application of machine learning-based surrogate models to predict CFD simulation outcomes with high accuracy. This predictive capability enables the rapid generation and evaluation of thousands of façade design alternatives without the need for full-scale CFD runs, significantly reducing computational effort and time. The proposed workflow establishes a direct connection between parameterized biomimetic geometries and thermal performance indicators, allowing for a comprehensive exploration of the design space through automated optimization. The optimization process relies on response surface modeling to approximate system behavior and evaluate design performance across multiple objectives. The final results reveal that the computationally optimized biomimetic façades achieved superior thermal performance compared to the initial bio-inspired design. To validate and extend the findings, additional simulations were carried out to evaluate the performance of selected designs under varying wind conditions and solar exposures. The larger wide mound configuration consistently performed best, offering a strong balance across the defined objectives. This solution was then applied to three-floor and five-floor commercial buildings in Riyadh, Saudi Arabia, where it showed a clear reduction in the average inner skin surface temperature of the OVF. The design proved suitable for construction with conventional methods and could be integrated into a range of architectural styles without major changes to the façade. These results reinforce the potential of combining biomimetic design strategies with computational optimization to develop high-performance façade systems for hot desert climates. The novelty of this work lies in combining biomimetic design principles with machine learning-driven optimization to systematically explore the design space and identify configurations that balance thermal efficiency with material economy. Full article

This paper presents FRESCO (Fiber REinforced Strengthening COmposite Database), a comprehensive open-source database designed to systematically organize experimental data on infilled RC frame systems that can be strengthened with advanced composite materials, such as Fiber-Reinforced Polymers (FRP), Textile-Reinforced Mortars (TRM), and other fiber-based solutions. The database employs open source practices while providing high-quality output that is fully compatible with leading commercial software packages such as ANSYS 2022R2. It uses Python3 as the main programming language and FreeCAD v1.0 as the model generation engine, with a systematic 13-section structure that ensures complete documentation of all parameters necessary for numerical modeling and validation of analytical methods. Two types of databases are provided: in comma-separated format (.csv) for common everyday interaction and in JSON format (.json) for easy programmatic access. The database features automated 3D modeling capabilities, converting experimental data into detailed finite element models with solid RC frame geometry, reinforcement details, and infill configurations. Validation through three comprehensive examples demonstrates that numerical models generated from the database closely match experimental results, with response curves that closely match the initial stiffness, the peak loading and the post-peak stiffness degradation phase across different loading conditions. The database focuses on RC frame systems with unreinforced brick infill. Reflecting the term FRESCO, which in Greek (φρέσκο) means “fresh”, the database is designed as a dynamic, evolving resource, with future versions planned to include RC walls and full buildings. Full article

Planetary exploration missions have acquired a growing amount of remote sensing data, offering a reliable basis for studying the geological evolution of planetary bodies such as Mars. In recent years, machine learning models have emerged as powerful tools for remote sensing by providing scalable and adaptive solutions for planetary science. We present a machine learning approach to map the spatial distribution of minerals on Mars, representing a step toward large-scale automated mineral mapping. Although existing CRISM dimensionality reduction methods are useful, the feature space remains high-dimensional, and relying on RGB overlays limits the ability to preserve and detect complex relationships, increasing the risk of missing important spectral patterns. Our framework utilises the Self-Organising Map (SOM) model and k-means clustering to identify clusters of spectral signatures, which may correspond to distinct minerals. It reduces dimensionality to a two-dimensional grid while preserving key high-dimensional patterns and relationships, providing a more reliable and interpretable basis for semi-automated analysis than RGB overlays. Although the clusters can be labelled by referencing a spectral library, our framework does not require labelled data and can operate in an unsupervised manner. The framework retains full spectral dimensionality of input features. The results indicate that our framework can identify the spatial distribution of minerals on Mars, even in complex spectral environments with overlapping features. Moreover, the SOM model output is interpretable rather than a black box, providing intuitive guidance for mineral exploration when applied in a semi-automated workflow. Full article

This study introduces an explainable neuro-symbolic and large language model (LLM)-driven framework for intelligent interpretation of corneal topography and precision surgical decision support. In a prospective cohort of 20 eyes, comprehensive IOLMaster 700 reports were analyzed through a four-stage pipeline: (1) automated extraction of key parameters—including corneal curvature, pachymetry, and axial biometry; (2) mapping of these quantitative features onto a curated corneal disease and refractive-surgery knowledge graph; (3) Bayesian probabilistic inference to evaluate early keratoconus and surgical eligibility; and (4) explainable multi-model LLM reporting, employing DeepSeek and GPT-4.0, to generate bilingual physician- and patient-facing narratives. By transforming complex imaging data into transparent reasoning chains, the pipeline delivered case-level outputs within ~95 ± 12 s. When benchmarked against independent evaluations by two senior corneal specialists, the framework achieved 92 ± 4% sensitivity, 94 ± 5% specificity, 93 ± 4% accuracy, and an AUC of 0.95 ± 0.03 for early keratoconus detection, alongside an F1 score of 0.90 ± 0.04 for refractive surgery eligibility. The generated bilingual reports were rated ≥4.8/5 for logical clarity, clinical usefulness, and comprehensibility, with representative cases fully concordant with expert judgment. Comparative benchmarking against baseline CNN and ViT models demonstrated superior diagnostic accuracy (AUC = 0.95 ± 0.03 vs. 0.88 and 0.90, p < 0.05), confirming the added value of the neuro-symbolic reasoning layer. All analyses were executed on a workstation equipped with an NVIDIA RTX 4090 GPU and implemented in Python 3.10/PyTorch 2.2.1 for full reproducibility. By explicitly coupling symbolic medical knowledge with advanced language models and embedding explainable artificial intelligence (XAI) principles throughout data processing, reasoning, and reporting, this framework provides a transparent, rapid, and clinically actionable AI solution. The approach holds significant promise for improving early ectatic disease detection and supporting individualized refractive surgery planning in routine ophthalmic practice. Full article