Search Results (2,609)

This study presents an explainable knowledge graph (KG) framework that transforms continuous spindle monitoring time-series data into transparent, reasoning-ready diagnostic structures. Existing data-driven approaches, while accurate, often lack the interpretability required for high-stakes industrial decision-making and are sensitive to operating condition drifts. To address these limitations, we propose a two-level temporal segmentation method combining label transition detection and statistical drift analysis to identify meaningful state boundaries. Furthermore, a percentile-based discretization mechanism converts statistical features into interpretable semantic tags. A Neo4j-based state–event–feature schema captures lifecycle evolution and evidence relations, enabling attribution path reasoning that links failure events to salient precursor features. Experiments on real industrial spindle data demonstrate a fault detection accuracy of 84.97% and a false alarm rate of 3.43%, effectively capturing stable baselines and intermittent abnormal bursts. The proposed framework provides a distinct novelty in bridging the gap between numerical time-series and symbolic reasoning, offering a practical pathway for deploying explainable and maintainable spindle health analytics. Full article

With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance. Full article

Background/Objectives: This study compared the hemodynamic performance of fenestrated (FEVAR), branched (BEVAR), and chimney endovascular aortic aneurysm repair (chEVAR) in patients with complex aortic aneurysms. Methods: The pre- (native) and post-endovascular repair (endograft-defined) blood lumen was reconstructed from computed tomography angiographies of nine (9) elective patients treated with FEVAR (n = 3), BEVAR (n = 3), and chEVAR (n = 3). Computational fluid dynamics (CFD) simulations obtained blood flow properties. Velocity magnitude, wall shear stress (WSS), time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and local normalized helicity (LNH) were computed at peak systole and mid-diastole. The hemodynamic data were statistically analyzed to evaluate correlations between FEVAR, BEVAR, and chEVAR, focusing on targeted visceral arteries. Results: Only slight differences were observed regarding RRT, OSI, and TAWSS between FEVAR and BEVAR, whereas the chEVAR group demonstrated a marked deviation from both. In FEVAR, the postoperative helical flow structures appeared more compact, while in BEVAR they were more developed and exhibited a more rotational configuration. The LNH of the visceral vessel patterns exhibited similar qualitative features across groups. Regarding TAWSS, higher values were found in BEVAR, whereas chEVAR showed the lowest. Conclusions: FEVAR, BEVAR, and chEVAR improved postoperative blood flow characteristics toward near-physiological conditions, reducing undesired flow patterns and recirculation zones. FEVAR showed more stable visceral flow, and BEVAR demonstrated higher flow rates and fewer recirculation zones, while chEVAR exhibited more streamlined visceral artery flow with reduced regurgitation at bridging stent entries. Despite variations, all approaches effectively preserved visceral artery perfusion. Full article

Acoustic material classification under non-line-of-sight (NLOS) conditions—where direct sound paths are obstructed—is a challenging task due to echo attenuation, complex reflections, and noise effects. This study aims to improve NLOS material recognition by introducing a novel wavelet–acoustic hybrid feature fusion method integrated with deep recurrent neural network architectures. Echo signals from nine different materials were collected using the newly developed ANLOS-R (Acoustic Non-Line-of-Sight Recognition) dataset, which was specifically designed to simulate realistic NLOS propagation environments. From these recordings, time-domain acoustic features and multi-scale wavelet-based energy and entropy statistics were extracted using ten wavelet families. The resulting 70-dimensional hybrid feature set was used to train several deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network–LSTM (CNN–LSTM). Among these, the CNN–LSTM achieved the highest balanced accuracy and macro-F1 score of 0.99, showing strong generalization and convergence performance. SHapley Additive exPlanations (SHAP) analysis indicated that Mel-Frequency Cepstral Coefficients (MFCCs) and wavelet entropy–energy features play complementary roles in material discrimination. The proposed approach provides a robust and interpretable framework for real-time NLOS acoustic sensing, bridging data-driven deep learning with the physical understanding of acoustic material behavior. Full article

Accurate engine fault classification with limited labeled data is critical for the safety and reliability of rotating machinery. This task is challenging because operating regimes are time-varying, and key variables must satisfy physical constraints, under which traditional feature classifier pipelines degrade and deep networks tend to overfit. We propose PhysGMM-MoE, a physics-aware Gaussian Mixture Model (GMM)-Mixture-of-Experts (MoE) framework for small-sample engine fault classification. At the data level, PhysGMM-MoE fits class-conditional, regime-aware GMMs and performs physically constrained, distance-based quality control to selectively augment minority classes while preserving engine operating semantics. At the model level, a heterogeneous pool of lightweight statistical experts and a lightweight Transformer-based deep expert (ECFT-Transformer) capture complementary neighborhood cues and high order multi-sensor correlations, and an L2-regularized logistic regression meta-learner fuses expert outputs via stacking. We evaluate fault classification on the 3500-DEFault diesel-engine dataset using the adopted eight-class cylinder-fault labeling (H, F1–F7) built from in-cylinder pressure statistics and torsional-vibration harmonics; although severity levels exist in the dataset, this study focuses on classification rather than severity estimation. With 40 training samples per class, PhysGMM-MoE achieves a mean accuracy of 0.9875, exceeding SMOTE+XGBoost by 0.0086, and attains the best macro precision/recall/F1 of 0.9878/0.9826/0.9889, demonstrating strong performance under the adopted small-sample setting. Full article

Small islands in developing countries often face infrastructural limitations, environmental fragility, and heavy economic dependence on tourism, making smart and sustainable innovation crucial. This study investigates what international tourists value in a destination to perceive it as a “smart island,” applying the smart city paradigm to the context of small island developing countries. A structured survey was conducted with 420 international tourists from diverse nationalities, using a five-point Likert scale to assess the importance of smart tourism attributes. Descriptive statistics, Pearson correlations, t-tests, and regression analyses were performed to identify significant predictors of overall satisfaction with smart tourism experiences. This study provides empirical evidence that international tourists primarily perceive destination smartness through core digital and infrastructural features rather than advanced technological sophistication. Real-time information systems emerged as the strongest predictor of perceived smartness, followed by free Wi-Fi access, sustainability-related technologies, and smart transport systems. The findings further reveal that demographic and cultural factors influence technology preferences, while immersive tools such as augmented reality play a secondary role. Overall, the results indicate that, in Small Island Developing Countries, smart tourism should be understood as a strategic approach to improving accessibility, connectivity, sustainability, and destination resilience rather than merely adopting high-end technologies. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

The further development of scalable fabrication for perovskite solar cells has been considerably constrained by strong process variability and the lack of a reliable real-time predictive mechanism during the thin-film formation process. Existing machine learning-based methods are incapable of capturing the inherent multi-stage kinetic characteristics and uncertainties of the perovskite crystallization process, as they rely on deterministic point prediction models and flatten time-series signals into static features, which necessitates more advanced modeling strategies. To address these challenges, an in situ process monitoring and predictive modeling framework based on a lightweight probabilistic Transformer is proposed for the scalable preparation of perovskite thin films. The strategically designed inputs, consisting of time-resolved photoluminescence (PL) and diffuse reflectance imaging signals acquired during the vacuum quenching process, enable the model to directly learn the conditional probability distribution of the final device performance metrics. Rather than producing a single predicted value, this method enables the explicit quantification of prediction uncertainty, providing statistical support for uncertainty-aware process assessment. Leveraging its advantages over feed-forward neural networks and traditional tree-based machine learning methods, the proposed Transformer architecture effectively captures the staged and non-stationary kinetic features of thin-film formation. Consequently, it exhibits higher robustness and superior uncertainty calibration capability during the early-stage prediction phase. The results demonstrate that the probabilistic Transformer-based modeling paradigm provides a viable pathway toward uncertainty-aware, data-driven process evaluation in perovskite manufacturing. This framework extends its application beyond perovskite photovoltaic device fabrication, providing a generalizable modeling strategy for real-time predictive assessment in the preparation of other complex materials governed by irreversible stochastic dynamics. Full article

This study investigates whether dedicated tumor segmentation for radiomics (TRAD) offers any advantage over gross tumor volume (GTV) in CT radiomics for predicting adenoid cystic carcinoma (ACC) progression after proton therapy (PT). Fifty-six patients with histologically proven salivary gland ACC were included, and 107 original features were extracted using PyRadiomics v3.1.0. Signatures were selected (n = 3) with sequential backward elimination using multiple classifiers, all optimized for improving cross-validated area under the ROC curve (AUC). Signature similarity was quantified using the Spearman correlation coefficient. Random forest (RF) yielded the best discriminative performance, with no statistical difference in AUCs between contour choices (GTV: 0.87 vs. TRAD: 0.80; ΔAUC_median = 0.0, p = 0.589). Time-to-event analysis confirmed both signatures stratified patients into distinct progression-free survival risk groups (Log-rank p < 0.0001) and demonstrated robust prognostic accuracy (GTV: C-index = 0.74, HR = 11.63; TRAD: C-index = 0.72, HR = 7.01). Biologically, GTV and TRAD signatures were borderline associated with perineural spread (p = 0.056) and solid tumor patterns (p = 0.053), respectively. Overall, CT-based radiomics models performed comparably across both segmentation strategies, supporting GTV as a practical and efficient alternative to TRAD for predicting ACC progression after PT. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

Series arc faults often occur in aging or faulty electrical systems due to insulation degradation, poor contact, or corrosion. These faults typically generate low current signatures, which are difficult to detect with traditional overcurrent protection methods. To address this measurement challenge, this paper proposes a systematic fault detection framework that combines discriminative feature extraction, statistical validation, and optimized classification. To comprehensively characterize arc fault signals, a diverse set of time- and frequency-domain features is extracted, and composite multiscale entropy is introduced to quantify nonlinear and transient fault dynamics more effectively. The MRMR (Maximum Relevance Minimum Redundancy) algorithm is applied to select features with high information content and low redundancy, thereby improving model generalization. A random search algorithm is used to adaptively optimize the random forest hyperparameters, establishing a high-accuracy fault diagnosis model. The experimental setup was established based on the UL1699B standard using a 115 V/400 Hz arc fault platform, and 1800 sets of data under nine different load types were collected for training and validation. Experimental results show that the proposed method outperforms five mainstream machine learning algorithms in terms of fault detection accuracy and performance. The results confirm its metrological robustness and its potential for deployment in waveform-based fault electrical monitoring systems. Full article

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while machine learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C-/N₀-based crowdsourcing metric. Experiments show detection precision up to 88% for jamming and 97% for spoofing, with false positive rates around 1–2% and an average detection time of 10 s. Full article

Within industrialised and emerging industrialised economies people typically spend over 95% in industrialised and emerging industrialised economies typically spend over 95% of their time in built environments, yet the neurophysiological impact of architectural design remains poorly understood. While previous studies link visual patterning to cortical activity, the cortical-to-autonomic stress pathway remains largely unexplored—a key omission given that chronic stress contributes to allostatic overload. This study examined how architectural façade design influences neurophysiological stress through a multimodal approach combining functional near-infrared spectroscopy (fNIRS) to monitor occipital cortical activity with heart rate variability (HRV) as an index of autonomic regulation. Eighteen participants provided HRV data and subjective ratings for nine systematically varied façade images characterised by their deviation with respect to natural statistics, while a subset of twelve completed fNIRS recording due to signal acquisition constraints. Façade identity significantly affected discomfort, complexity, and interest ratings ($p<0.001$), and deviation from natural statistics predicted all three measures ($p<0.01$). Façade type also showed a small but significant effect on HRV ($p=0.003$), although variance was dominated by individual differences. No stimulus-specific occipital fNIRS differences were observed. However, due to the limited sample size, further research is needed to verify this observed result. Whilst global generalisations cannot be drawn due to the small sample size, these pilot research findings indicate that façades deviating from natural image statistics influence perceptual comfort and may modestly modulate autonomic balance. However, the present data does not provide clear evidence of stimulus-specific cortical effects, which, if present, likely remain below the detection thresholds of the current protocol given its methodological constraints. This study highlights methodological hurdles and establishes a scalable framework for linking computational visual metrics to physiological responses, informing future investigations into how architectural features influence human health. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Unmanned aerial vehicle (UAV) applications are growing fast in different sectors, such as agricultural, commercial, academic, leisure, and health fields. However, drones pose a significant threat to public safety due to their ability to transmit information, particularly when used in an unauthorized or malicious manner. In fact, in order to protect citizens’ privacy and prevent accidents in high-traffic areas due to poorly controlled flights, no-fly zones for drones have been established in the legislation of a number of countries. Most common UAV detection techniques are based on radio frequencies, which identify drones and their models by monitoring radio frequency signals. However, differentiating between multiple UAVs of the same model is their main limitation. This article fills this gap by proposing a method for physically tracking the communication frames of a registered UAV in the presence of another UAV of the same model. A measurement campaign was conducted to collect real-world RF communication signals from two DJI MAVIC 2 Zoom, two DJI Air2S, and two DJI Phantom drones. This measurement was performed inside and outside an anechoic chamber in order to study the UAV’s communication without any interference and in the presence of other communications. Through detailed statistical analysis, we characterized features such as communication duration, time intervals between communications, signal strength, and patterns in communication timing sequences. Our analysis revealed unique, identifiable patterns for each UAV, even within identical models. Based on these results, we developed an automated system that links communication frames to the corresponding registered drones. The proposed method fills gaps in drone detection and surveillance models, providing valuable information for applications in the fields of security and airspace management. This research lays the foundation for drone identification solutions, thereby addressing a major limitation of current detection technologies. Full article