Search Results (462)

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature (\(T^°_{{H_2}O})\), air temperature (\(T^°_{air})\), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, (\(T^°_{{H_2}O})\), and (\(T^°_{air})\) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article

The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach. Full article

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes. Full article

In addressing the potential anomalies in wind turbine monitoring data, it is essential to note that a single data source cannot independently ascertain the validity of data. This paper proposes a correlation coupling judgment algorithm designed to evaluate the validity of wind turbine monitoring data. By quantitatively analyzing the degree of correlation between various sensor data using the Pearson correlation coefficient, this study reveals that data characteristics significantly influence the correlation coefficient. The analysis also examines the effects of filtering, signal phase differences, and interference signals on correlation. The results indicate that effective data preprocessing, can dramatically enhance correlation, while phase shifts and noise interference significantly degrade it. Identifying and mitigating these interfering signals is thus established as a crucial prerequisite for defining reliable correlation criteria. Therefore, this study demonstrates that effective data preprocessing is a necessary step for any correlation-based validity assessment framework. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article

Autonomous vehicles (AVs) are rapidly becoming a reality, with a series of cities in the world currently testing applications. Despite these developments, the existing analyses in the literature concerning the impacts of such developments on urban transportation systems have yielded a body of evidence marked by significant divergence and contradictory conclusions. Such conflicting findings critically hamper the synthesis of a coherent understanding and the formulation of evidence-based strategies, a challenge exacerbated by the potentially multifaceted nature of these impacts. The potential disruptive technology and the game-changing force of automated vehicles make this lack of congruence in analytical outcomes severely complicate efforts to derive clear insights or actionable conclusions. The purpose of the paper is to explore and define the optimal strategies for implementing autonomous vehicle technologies, to predict their effects on anomalies, in the Kuhnian sense, of urban transportation, and to propose a desirable urban vision and a paradigm shift consisting of a decline of car ownership dependence and the rise of shared AVs. This study is undertaken to address the escalating crisis in urban transportation globally. Cities are facing unprecedented strain due to rapid urbanization, leading to severe traffic congestion, pervasive air and noise pollution, significant safety risks, and persistent accessibility gaps, all of which profoundly diminish urban quality of life and impede economic vitality. The new vision has been assessed based on a literature selection, some qualitative and quantitative analyses, and applications and projects currently in testing. The results are largely positive and promise to change urban transportation radically, as well as to resolve the mismatches between the vision, what the paradigm predicts, and what is revealed in the implementation. The success of the vision ultimately depends on policy and regulation to manage the way in which AVs are implemented in urban areas, if they are not to lead to a worsening of the urban environment, accessibility, and health. This thoughtful implementation should address all potential challenges through integrated planning of transportation, land use, and digital systems. Full article

Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise ratio (SNR) of the target signal, and some noise overlaps with the target signal in both time and frequency domains. Traditional methods exhibit poor compensation capability for such noise. To address these issues, this paper proposes an aeromagnetic compensation method based on a time–frequency joint representation neural network. This method combines continuous wavelet transform (CWT) and bidirectional long short-term memory (Bi-LSTM) to establish a prediction model. It uses wavelet transform to extract the frequency variation characteristics of the UAV’s magnetic interference, and it inputs these frequency characteristics along with the original time-domain data into the Bi-LSTM network to predict the UAV’s noise. Bi-LSTM can effectively extract the temporal logical connections in time-series signals, thereby improving the accuracy of the compensation model and ensuring high robustness. In this study, magnetic interference data from quadrotor UAV compensation flights were collected for experiments to evaluate the performance of the proposed method. Experimental results show that the neural network fused with time–frequency features, when applied to UAV aeromagnetic compensation, significantly enhances the accuracy and robustness of the compensation method. To verify the method’s effectiveness in removing UAV-generated noise during actual exploration, aeromagnetic survey data from a specific area were compensated using this method. Full article

This article addresses a two-dimensional inverse problem of magnetotelluric sounding under the assumption of E-polarized electromagnetic fields. The main focus is on the construction of a forward numerical model based on the Helmholtz equation with a complex coefficient, and the recovery of electrical conductivity from boundary measurements. The second-order finite difference method is employed for numerical simulation, providing stable approximations of both the direct and the conjugate problems. The inverse problem is formulated as a minimization of a data misfit functional, and solved using Nesterov’s accelerated gradient descent method, which ensures fast convergence and robustness to noise. Numerical experiments are presented for a synthetic model featuring a smooth background conductivity and a localized anomaly. Comparison between the exact and reconstructed solutions demonstrates the high accuracy and efficiency of the proposed algorithm. The developed approach can serve as a foundation for constructing practical inversion schemes in geophysical exploration problems. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Anomaly detection is a pressing issue, particularly in industrial images. Detecting weld defects in radiographic images is a challenge due to the small signal-to-noise ratio (SNR) and the limited availability of data. In this paper, we propose an automated weld defect detection method using Normalizing Flows (NFs). We employed various state-of-the-art NF architectures with different feature extractors to detect defects in radiographic images of welds, comprehensively comparing the results with radiographic images of welded steel pipes collected from industrial sites. The results show that the combination of CFlow-AD with a wide residual network-50-2 (WRN-50-2) outperformed the other methods, indicating its effectiveness in anomaly detection. Full article

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterogeneity of social data, we leverage recent advances in graph theory, natural language processing, and anomaly detection. Our framework combines clustering for community detection, sentiment analysis for emotional state inference, and centrality metrics for influence estimation, while integrating multimodal data—including textual and visual content—for richer behavioural insights. Experimental results demonstrate that the proposed approach effectively extracts actionable knowledge, supporting mental well-being and strengthening digital social ties. Furthermore, we emphasise the role of privacy-preserving methods, such as federated learning, to ensure ethical analysis. These findings lay the groundwork for responsible and effective applications of machine learning in social network analysis. Full article

Weakly supervised video anomaly detection (WSVAD) aims to identify unusual events using only video-level labels. However, current methods face several key challenges, including ineffective modelling of complex temporal dependencies, indistinct feature boundaries between visually similar normal and abnormal events, and high false alarm rates caused by an inability to distinguish salient events from complex background noise. This paper proposes a novel method that systematically enhances feature representation and discrimination to address these challenges. The proposed method first builds robust temporal representations by employing a hierarchical multi-scale temporal encoder and a position-aware global relation network to capture both local and long-range dependencies. The core of this method is the dual-stream attention-enhanced memory network, which achieves precise discrimination by learning distinct normal and abnormal patterns via dual memory banks, while utilising bidirectional spatial attention to mitigate background noise and focus on salient events before memory querying. The models underwent a comprehensive evaluation utilising solely RGB features on two demanding public datasets, UCF-Crime and XD-Violence. The experimental findings indicate that the proposed method attains state-of-the-art performance, achieving 87.43% AUC on UCF-Crime and 85.51% AP on XD-Violence. This result demonstrates that the proposed “attention-guided prototype matching” paradigm effectively resolves the aforementioned challenges, enabling robust and precise anomaly detection. Full article

Structural parameters, such as strain or deflection, were collected by sensors and analyzed to assess the bridge’s structural condition and obtain a reliable reference for bridge maintenance. In the data acquisition and transmission process, sensor data inevitably contains noise and interference, resulting in anomalies, especially data distortion during wireless transmission. These anomalies significantly impact data analysis and structural evaluation. To mitigate the effects of these abnormalities, we conducted the cause analysis. The Sanxia Viaduct was used to design a strain monitoring method as a bridge model. We analyzed vibrating string sensor data collected in the cold environment using the Nair method to eliminate outlier data. The analysis results of strain and temperature trends showed that the data fusion method developed in this study showed high precision and stability and effectively reduced the impact of noise and data anomalies. By monitoring actual bridges, the effectiveness and practicality of the method were validated. The model provides significant information on the development and application of bridge health monitoring technology. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

Geothermal power generation offers a high availability factor and independence from weather conditions, yet steam production in geothermal wells often declines over time due to factors such as pressure depletion and scale deposition. To enable early detection of production anomalies and optimize maintenance, this paper proposes an anomaly detection framework based on Singular Spectrum Analysis (SSA). First, a Butterworth low-pass filter reduces high-frequency noise; then, SSA decomposes the time series, focusing on the largest singular value’s corresponding vectors. An anomaly score measures the deviation between current and historical singular vectors, and Non-Maximum Suppression (NMS) aggregates consecutive peaks to reduce false positives. We apply this method to 14 years of data from nine geothermal wells, comparing two threshold strategies: a unified threshold and well-specific thresholds. Results show that while a unified threshold simplifies deployment, individual thresholds can improve detection in certain wells, underscoring the impact of well characteristics and class imbalance. Our findings demonstrate that SSA-based anomaly detection, combined with NMS and threshold optimization, can effectively support maintenance decisions in geothermal power plants. Full article