Search Results (685)

To address the low-voltage violations that may occur in distribution networks with high penetration of distributed photovoltaic (PV) during sunset and evening peak periods under extreme high-temperature conditions, this paper establishes a source–grid–load electro-thermal coupling model that accounts for load thermal accumulation, transient conductor thermal inertia, temperature-dependent line impedance, and PV thermal derating. Based on a soft safety lower bound and a risk-preference utility function, the probability of voltage violation, violation depth, and expected violation duration are introduced to construct node-level and system-level comprehensive risk factors. The cumulant method combined with the Cornish–Fisher expansion is used to reconstruct the probability distribution of nodal voltages, enabling analytical risk calculation. Simulation results on the IEEE 33-bus system at 45 °C show that the proposed method can quantitatively reflect the temporal variations of nodal voltage distributions, physical violation depth, dimensionless severity utility, and expected violation duration, and identify weak nodes in the later part of the evening peak, providing a reference for risk early warning in distribution networks under extreme heat. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

This article presents the development and proof-of-concept testing of a vision-based safety monitoring tool for operators in simulated robotic cells in RoboDK. The proposed method uses a virtual camera placed above the cell and image processing techniques to analyze the relationship between the operator and the workspace swept by the robot. In an initial stage, the robot movement is recorded as a mask of the swept space, and areas irrelevant to the process can be excluded by user-defined polygons. In the monitoring stage, the operator is identified in the video stream by HSV segmentation, after which an adjustable clearance zone is generated around the detected contour. Based on the intersections between the operator, clearance, swept space mask and the mask of the current robot movement, the application provides four discrete states: SAFE, WARNING, DANGER and COLLISION. For the experimental validation in the virtual environment, the virtual contact moment is estimated separately, while the COLLISION state is defined as the intersection between the inflated operator contour and the current robot motion mask. Therefore, in this study, COLLISION does not indicate measured physical contact, but an image-based imminent-collision condition used for early warning. The test scenario was carried out in a virtual palletizing cell, which includes an articulated arm robot, conveyors, manipulated objects and a mobile dummy acting as an operator. The obtained results support the use of the method as an applicative simulation solution for the evaluation of the early detection of risk situations. The study is limited to the virtual environment and represents a basis for future research on the development of visual monitoring systems to increase safety in collaborative and industrial robotic cells. Full article

Wave height is a critical parameter for marine hazard warning systems and the structural safety of offshore engineering. The Bohai Sea and its surrounding region is an important economic hub in northern China and serves as the maritime route for the coastal provinces of North, Northwest, and Northeast China. Therefore, the sea state in the Circum-Bohai Sea has a significant impact on the Bohai economic circle. This study analyzes and summarizes the medium-term variation trends of waves in the Circum-Bohai Sea based on multi-source satellite data (AVISO/Copernicus dataset) from 2009 to 2025. The results indicate that the Significant wave height (SWH) in the Circum-Bohai Sea is mainly dominated by wind waves, exhibiting significant seasonal variation characteristics. The significant wave height in winter exhibited a consecutive decline from 2014 to 2018, with a reduction of approximately 14%. Spectral analysis reveals the existence of one-year, half-year, and two-year cyclical variation signals in the SWH of the Circum-Bohai Sea. This study provides a scientific foundation for marine hazard early warning systems, offshore engineering safety assessments, and climate change adaptation strategies in the Bohai Economic Rim. Full article

Six fatigue tests were performed on W6×15 steel beams fabricated from A572 Grade 50 steel, each 4 m in length and subjected to sinusoidal bending with stress amplitudes ranging from 0.10 Fy to 0.70 Fy at 4 Hz. In five of the six specimens, a Charpy V-notch-type defect was introduced at mid-span on the lower flange to initiate localized damage. Cyclic loading was applied until fatigue failure occurred. Throughout testing, two primary parameters were continuously monitored: (i) strain and (ii) surface magnetic flux density. Analysis of the magnetic flux evolution revealed distinctive signal patterns that emerged as fatigue damage progressed, particularly near the point of failure. These magnetic variations correlate with the accumulation of microstructural damage and enable the estimation of a safe-life prediction for each specimen under cyclic loading. Furthermore, a qualitative relationship between the fractographic features and the corresponding magnetic response was identified. The results demonstrate that monitoring surface magnetic flux provides a reliable early-warning indicator of fatigue damage in full-scale steel members, offering a promising tool for structural health monitoring and public safety in elements of steel infrastructure such as bridges. Full article

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

To address the problems of false negatives, false positives, and delayed response in traditional fire detection systems, this paper proposes a warehouse fire detection scheme based on multi-sensor information fusion. By constructing a ZigBee wireless sensor network and integrating temperature, CO concentration and smoke sensors, fire simulation data are collected in the warehouse. At the data processing level, an improved Grubbs criterion is innovatively adopted to eliminate outliers, and the median is used instead of the average to effectively suppress the same-side shielding effect. At the feature layer fusion stage, a BP neural network model optimized by the cosine decreasing inertia weight particle swarm optimization algorithm (CIW-PSO) is designed. By dynamically adjusting the learning factors (c₁, c₂) and inertia weight (w), the convergence speed and global optimization ability are significantly improved. At the decision-making level, a fuzzy logic reasoning mechanism is introduced to integrate multi-parameter membership functions, thereby reducing the probability of misjudgment. Field tests have verified that the system can achieve early fire warning in a 50 m × 100 m warehouse environment, with a false alarm rate reduced by 42% compared to a single sensor and a response time shortened by 35%, providing an efficient and reliable intelligent solution for warehouse fire safety. Full article

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves $R^2=0.940$, RMSE $=42.0$ mm, MAE $=28.7$ mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems. Full article

Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe class imbalance between normal and faulty samples further degrades prediction performance, particularly for minority fault types. To address these challenges, this paper proposes a novel fault prediction model, M²ANet, using SCADA data within a 30-min pre-fault window. The model combines a dual-memory module with progressive dilated convolutions to efficiently capture multi-scale temporal dependencies from high-dimensional operational variables. An entropy-bias penalty is further introduced into the loss function to adaptively regularize the predicted probability distribution, alleviating overconfidence under imbalanced data conditions and improving the recognition of minority faults. Experiments on a real-world wind farm dataset show that M²ANet achieves an overall accuracy of 90.73% and a weighted F1-score of 90.62% in multi-class fault prediction, outperforming 10 representative baseline models. In addition to these aggregate metrics, per-class evaluation confirms the model’s robustness under class imbalance. Notably, for yaw system faults, which account for only 1.9% of the samples, M²ANet achieves a recall of 95.92% with a 30-min-ahead warning. These results demonstrate its effectiveness and reliability for early fault prediction in practical wind turbine applications. Full article

Rock–concrete composites are critical load-bearing elements in geotechnical engineering applications such as slope support. Their mechanical response and damage evolution after fatigue disturbances, such as blasting and mechanical operations, govern the long-term stability and safety of engineered structures. To fully capture these complex behaviors, this study presents a novel multi-scale approach by integrating uniaxial compression tests with three-dimensional digital image correlation and discrete element modeling. This combined experimental–numerical framework is employed to systematically examine the macro- and meso-scale mechanical behavior, crack evolution, and energy response of composites with varying interface angles after quasi-static cyclic loading. The results reveal that as the interface angle increases, the peak strength declines markedly while the brittleness index increases, reflecting a distinct transition in the failure mode from plastic-dissipation-dominated to elastic-energy-storage-dominated. Consequently, the dominant failure mechanism shifts from tensile to shear-slip control. Furthermore, fatigue disturbances exacerbate material degradation, inducing a composite “interface shear–end tension” failure in specimens with higher interface angles and significantly raising the proportion of shear cracks. Energy analysis indicates that cyclic loading enhances the elastic energy storage capacity, and the energy conversion threshold rises continuously with the interface angle. These findings clarify the multi-scale control mechanisms of interface geometry on fatigue-induced failure, providing a theoretical foundation for predicting fatigue life and enabling early pre-warning of failures in rock–concrete engineering structures. Full article

Extreme weather events exacerbated by global warming pose severe threats to urban safety, underscoring the urgent need for highly accurate precipitation nowcasting. Short-term local heavy precipitation remains a particular challenge for traditional forecasting due to its suddenness and high disaster potential. To address this, we propose a multi-modal fusion framework that integrates ground-based GNSS-derived Precipitable Water Vapor (GNSS-PWV) and ground-based Radar Composite Reflectivity (CR). While GNSS-PWV keenly captures pre-convective atmospheric water vapor accumulation, radar CR details the morphological distribution of hydrometeors. Specifically, we developed the Spatio-Temporal Enhanced Attention Swin U-Net (STEA-Swin) model to synergize these heterogeneous datasets over the Beijing–Tianjin–Hebei region. High-precision PWV was retrieved from 250 Continuously Operating Reference Stations (CORS) using the dual-frequency ionosphere-free Precise Point Positioning (PPP) method, achieving a strong correlation (>0.97) with ERA5 reanalysis data. Validated against measured data from the 2025 flood season, the STEA-Swin model achieved a Probability of Detection (POD) of 0.68 for torrential rain events at a +1 h forecast lead time. Notably, compared to single-source models, the Critical Success Index (CSI) and POD for torrential rain improved by 18.5% and 21.5%, respectively. These findings demonstrate that coupling deep learning with ground-based GNSS-derived atmospheric thermodynamic information can significantly enhance early warning capabilities, providing a promising technical approach for regional disaster prevention and climate resilience. Full article