Search Results (115)

Smart meters are the core terminals for distribution network data acquisition in smart cities, yet their collected data commonly suffer from quality issues caused by harsh operating environments, communication failures, hardware degradation, and human factors. This paper presents a systematic review of anomaly detection and data repair methods for smart meter data based on a critical analysis of many publications. First, we characterize five typical anomalies—sudden jumps, reading stagnation, reverse readings, pulse spikes, and gradual drifts—from physical root causes to data manifestations and provide unified mathematical definitions with explicit traceability to the existing literature. Additional anomaly types including meter replacement jumps, data duplication from retransmission, complete missing segments, and timestamp errors are also discussed to present a more complete picture of operational data quality challenges. Second, existing anomaly detection methods are systematically reviewed and classified into four categories—statistical, machine learning, deep learning, and dedicated time-series methods—with representative studies, quantitative performance metrics, and scenario-specific applicability examined for each. Third, data repair approaches are reviewed across four categories—traditional interpolation, matrix completion, generative models, and time-series prediction—with systematic comparison of their accuracy and limitations across different anomaly types and durations. Based on the synthesized evidence, we identify three cross-cutting structural limitations that persist across method categories: the performance ceiling of data-only detection without physical constraint embedding, the open-loop architecture that separates detection from repair and allows error propagation, and the exclusive reliance on statistical error metrics that fails to distinguish physically plausible repairs from those violating conservation laws. To address these gaps, we discuss a physics-guided integrated framework incorporating physical constraint embedding, joint anomaly diagnosis, scenario-adaptive repair, and posterior verification as a promising forward-looking direction. Finally, open challenges and future research directions are outlined, including parameter adaptation in unlabeled scenarios, multi-source data fusion for physical disambiguation, new power system extensions, explainable AI integration, edge-computing deployment, and standardized benchmark development. This review provides a comprehensive theoretical reference and technical roadmap for smart meter data quality research in the context of smart city energy systems. Full article

Field vibration monitoring of EMU traction motor bearings is commonly constrained by weak or relative labels, fluctuations in operating conditions, and limited abnormal samples. Under these conditions, learning a normal boundary from a single bearing position may be unstable, and isolated score spikes may lead to unreliable alarms. To address these issues, this study proposes a multi-axle reference and temporal-consistency-enhanced Deep SVDD framework, termed MA-TC-Deep SVDD, for field anomaly detection of EMU traction motor bearings. Unlike closed-set fault diagnosis that requires known fault labels, the proposed framework focuses on identifying deviations from the stable operating regime. First, a compact 10-dimensional time-frequency representation is constructed from valid vibration segments. Second, stable samples from the target bearing position and screened stable samples from other monitored positions on the same EMU are organized as a multi-axle reference set for one-class normal-boundary learning. Third, feature recalibration, temporal-consistency regularization, reference-score standardization, causal smoothing, and consecutive-alarm judgment are incorporated to improve robustness against field disturbances. The anomaly-prior-guided health-state interpretation module is retained only as post hoc evidence for describing severity evolution and does not feed back into the anomaly detection threshold. Field data collected from an in-service EMU over D1–D5 are used for validation. The results show that bearing position 1 has low anomaly scores on D1–D2, exhibits transitional deviation on D3, and shows persistent state deviation on D4–D5, while the other monitored positions remain comparatively stable. Under the current weak-label evaluation protocol, MA-TC-Deep SVDD achieves higher average anomaly detection performance than the compared baseline methods, with AUC = 0.909, AP = 0.872, Precision = 0.887, Recall = 0.802, F1 = 0.843, and FAR = 0.047. These results indicate that the proposed framework can provide field anomaly-warning and severity-oriented interpretation under weak-label monitoring conditions. However, it should not be interpreted as a replacement for disassembly-confirmed fault-type diagnosis or remaining useful life prediction. Full article

Smart wearable electrocardiogram (ECG) monitoring enables continuous cardiac assessment beyond clinical settings, but ECG signals are often degraded by baseline wander, muscle artifacts, and electrode motion artifacts. At present, pure end-to-end spiking neural networks (SNNs), with the advantage of low computational complexity, have rarely been explored for ECG noise suppression, particularly under dynamic conditions. To address this gap, this study proposes Direct-Coding-Physics-Aware Spiking Neural Network (DCPA-SNN) for wearable ECG denoising. The proposed method integrates a direct-coding SNN, channel attention, residual noise learning, and a physics-aware multi-domain loss function to preserve diagnostically important waveform structures. Clean ECG signals from the MIT-BIH Arrhythmia Database and real-noise segments from the MIT-BIH Noise Stress Test Database were used to construct single-noise and mixed-noise evaluation scenarios with input SNRs ranging from −6 dB to 4 dB to reflect the noise characteristics of wearable devices. Experimental results demonstrate that DCPA-SNN achieves robust denoising performance under different noise conditions. In the mixed-noise scenario, which serves as the primary evaluation setting of this study, the average denoised SNR reached 5.80 dB, with an average SNR improvement of 6.80 dB, while the R-peak detection rate increased from 90.71% to 95.72%. These results demonstrate that the proposed model, DCPA-SNN, provides a promising approach for wearable ECG denoising with potential for low-power deployment. Full article

Automatic analysis of electroencephalography (EEG) recordings relies on large, high-quality labeled datasets. Manual segmentation by medical experts is resource-intensive and time-consuming. Moreover, to overcome potential subjectivity in labeling, independent annotation by at least two experts is required. Therefore, reliable automatic data labeling is essential for obtaining the large datasets needed to train robust AI models. In this paper, we show that a properly trained state-of-the-art deep neural network (DNN) achieves labeling performance comparable to inter-expert agreement in the task of segmenting epileptiform activity patterns. To this end, we first compiled a custom database of EEG recordings containing such patterns. Second, five experts based on part of these recordings independently assessed spike-wave index (SWI), which is a key diagnostic criterion that indicates the percentage of the EEG recording during which epileptic discharges are observed. Third, we compared the expert assessments with SWI calculated based on automatic segmentation by the trained DNN. Our results demonstrate that the 1D U-Net architecture achieves competitive overall performance and aligns well with both expert assessments and expert-derived SWI values. Thus, automated segmentation and analysis of EEG recordings holds great promise for accelerating diagnosis and developing targeted therapeutic strategies for epilepsy. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Background/Objectives: Heart failure (HF) imposes a significant healthcare burden in aging populations. The COVID-19 pandemic disrupted care, raising concerns about chronic disease management. We analyzed temporal trends in HF hospitalizations in Italy (2008–2022), assessing the influence of demographics, clinical complexity, seasonality, and the pandemic. Methods: Using national discharge records, we strictly identified hospitalizations with a primary HF diagnosis via ICD-9-CM codes. Admissions were stratified by age, sex, season, and clinical severity according to the Elixhauser Comorbidity Index. Temporal trends were analyzed using a Negative Binomial Generalized Linear Mixed Model with the time component modeled through a segmented regression to account for pre-pandemic, pandemic (2020), and late-pandemic dynamics. Results: We identified 3,162,075 primary HF admissions, yielding a crude hospitalization rate of 35.11 per 10,000 person-years. Patients with an intermediate comorbidity burden (Elixhauser 13–20) accounted for 59.3% of the total volume. Multivariable analysis identified male sex (RR = 2.24, p < 0.001 ***), age ≥ 75 years (RR = 95.04 vs. 25–44, p < 0.001 ***), and winter seasonality as strong independent predictors. Trend analysis revealed a structural long-term decline across all severity tiers, driven by a sharp drop in 2020 (RR = 0.80, p < 0.001 ***) coincident with a spike in in-hospital mortality. While patients with low-to-intermediate comorbidity exhibited a partial rebound in 2021–2022 (overall RR = 1.06, p < 0.001 ***), admissions for highly complex patients (score > 20) showed an accelerated late-pandemic decline. Conclusions: HF hospitalizations in Italy remain a substantial burden driven by advanced age and clinical comorbidity. Our 15-year population-level data indicate no sustained, structural late-pandemic surge in HF admissions. The observed fluctuations were likely driven by severe healthcare disruptions and patient care avoidance rather than a true epidemiological shift, highlighting the urgent need for resilient chronic care systems during emergencies. Full article

The COVID-19 pandemic has reshaped global infrastructure, highlighting the importance of effective infectious disease management. Identifying when and where infection trends change abnormally can aid strategic planning; yet, existing change point detection methods struggle due to the non-linear nature of infection trends, spatial and temporal dependencies, regional demographic and healthcare variations, and differing preventive measures. To address this issue, we propose a Bayesian method that can detect candidate regional disease-related change periods while overcoming these challenges. Specifically, we develop a Bayesian function-on-function regression model that learns from infection trends across multiple regions by incorporating both time-invariant features and the historical effect of time-dependent functional covariates. Temporal dependence in the covariate effects is captured through neighborhood-based spike-and-slab priors, whose latent binary inclusion indicators are, in turn, modeled by Ising priors. A Gibbs sampling framework is derived to approximate the joint posterior distribution of the model parameters. We compared the performance of the proposed framework against two widely used change-point detection methods, BCP and Segmented. In our simulation studies, BLIDE achieves an F1-score of $1.000$ under high signal-to-noise conditions and maintains an F1-score above $0.95$ even when noise dominates the trends, substantially outperforming BCP (F1-scores $0.454$ and $0.131$, respectively) and Segmented (F1-scores below $0.05$ across all scenarios). Full article

In automotive crash testing, trustworthy crash-test curves are essential for reliable crashworthiness assessment, yet automated anomaly detection is difficult due to limited labeled abnormal cases, event-level data scarcity, and distribution shifts across vehicle models and sensor configurations. This paper proposes MVCA-AD (Multi-View Context Augmentation for Anomaly Detection) for single-channel crash-test curves. MVCA-AD generates multiple context-rich views using deterministic time- and frequency-domain transformations to amplify subtle anomalous patterns under limited labeled supervision. A trend-aware modulation module and cross-view attention fuse these views to improve sensitivity to critical segments such as impact spikes and gradual transitions while remaining robust to noise. Experiments on three subsets derived from physical full-scale crash tests show that MVCA-AD improves Precision, Recall, F1-score, and area under the ROC curve (AUC) over strong baselines and achieves stable performance under event-level grouped evaluation across heterogeneous head and B-pillar crash-test signals. The proposed approach supports crash-test data quality control by automatically identifying abnormal curves for downstream crashworthiness assessment workflows. Full article

To address the longitudinal vibration issues in high-speed elevators induced by external excitations, this study constructs a high-precision multi-degree-of-freedom (MDOF) dynamic model to systematically analyze vertical dynamic response characteristics. Utilizing the substructure method, the complex traction system is decomposed into several subsystems, including the traction device, tensioning device, car and car frame, counterweight system, and segmented wire ropes. By integrating Lagrange’s equations with Newton’s second law, the governing differential equations of motion for each component are derived, establishing an adaptable global dynamic model. The forced vibration analysis focuses on the impacts of periodic excitation from traction sheave eccentricity, piecewise reverse braking torque, and vertical impacts from guide rail joints on car vibration response and wire rope dynamic stress. The results indicate that: traction sheave eccentricity leads to periodic fluctuations in car acceleration, with vibration peaks decreasing as the payload increases; reverse braking torque triggers impulsive acceleration overshoots, where the peak value under full-load conditions increases by approximately 15% compared to the no-load condition, accompanied by a longer duration of low-frequency vibrations; guide rail joint impacts produce instantaneous acceleration spikes, which increase by about 18% under high-speed operating conditions; and the wire rope stress exhibits significantly higher sensitivity to load variations within the low-load range of 0–0.2. Full article

The aim of this study is to propose an uncertainty-aware dynamic cascading framework based on spiking neural network (UDC-SNN) for multimodal emotion recognition, particularly to address the inherent trade-off between recognition performance and energy efficiency. An asymmetric dynamic routing mechanism was proposed to enable demand-driven activation of the high-power electroencephalogram (EEG) branch, coupled with preliminary inference on a low-power electrocardiogram (ECG) branch and uncertainty quantification via Shannon entropy. Meanwhile, a parameter-free log-linear aggregation strategy was developed to transform modality-specific entropy into dynamic Bayesian weights through an exponential decay function, effectively mitigating the negative transfer effects induced by unimodal noise. The UDC-SNN was evaluated on the multimodal affective dataset DREAMER, comprising 23 subjects (170,660 segments). The averaged recognition accuracy and energy consumption across the three dimensions of valence, arousal, and dominance were 90.75% and 4.62 $\mathsf{\mu }$J, respectively. The obtained results suggest that the proposed framework could potentially achieve a favorable balance between high emotion recognition and low energy consumption, thereby establishing its applicability for real-time monitoring in resource-constrained scenarios. Full article

Neuronal activity and cerebral blood flow are tightly coupled to support the high metabolic demands of the brain. Disruption of neurovascular coupling is a defining feature of many neurodegenerative disorders such as Alzheimer’s disease, stroke, small vessel disease, Parkinson’s disease, and aging. Progress in understanding the mechanisms underlying neurovascular coupling requires experimental approaches that can simultaneously measure neuronal activity and vascular dynamics with high spatial and temporal resolution, while also enabling targeted perturbations of the system. Here, we present a methodological framework that combines chronic electrophysiological recordings with two-photon imaging of cerebral blood flow and optogenetic manipulation of the vasculature in vivo. Using a chronically implanted flexible electrode array, we obtain measurements of the single- and multi-unit spiking activity, as well as local field potentials. Concurrently, two-photon microscopy enables high-resolution measurements of vessel diameter and blood flow within individual vascular segments. In addition, optogenetic control of vascular smooth muscle cells allows for rapid and reversible manipulation of the vessel diameter through the same cranial window while simultaneously recording the neural and vascular activity. We provide detailed protocols for surgical implantation, data acquisition, and analysis, and discuss experimental considerations and limitations. This combined platform offers a powerful tool for mechanistic studies of neurovascular coupling and its dysfunction in disease models. Full article

Reliable high-precision positioning in railway tunnels is essential for intelligent train operation and safety monitoring, yet GNSS signals are severely degraded by blockage and multipath. This paper proposes a deployment-oriented numerical framework to optimize pseudolite layouts in tunnels by explicitly modeling visibility obstruction and controlling worst-case geometry along the train trajectory. A high-fidelity 3D tunnel–train model is established, in which line-of-sight (LoS) availability is screened under vehicle occlusion and trajectory-level geometric quality is evaluated accordingly. Instead of optimizing only the average PDOP, the proposed framework minimizes the trajectory 90th-percentile PDOP (qPDOP) to suppress tail-risk geometric degradation, while interpreting PDOP as an error amplification factor that directly affects positioning reliability under measurement noise and local multipath. The core contribution is a Voronoi-partition-constrained improved genetic algorithm (IGA) for tunnel pseudolite deployment. Voronoi partitioning enforces segment-wise coverage by requiring at least one pseudolite in each partition cell and avoids clustering-induced blind zones. Meanwhile, the IGA incorporates improved search and constraint-handling mechanisms to satisfy practical engineering requirements, including feasible installation regions, minimum spacing, mounting-face balance (ceiling/side walls), communication range, and continuous satellite visibility. Comparative simulations and ablation studies demonstrate that the proposed method achieves more uniform coverage and significantly improves full-trajectory geometric stability, reducing high-quantile PDOP and mitigating local spikes in occlusion-sensitive sections under cost-constrained sparse deployments. The proposed framework provides a practical and flexible toolchain for designing positioning-oriented pseudolite infrastructures in underground transportation environments. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article