Search Results (589)

In recent years, animation-based systems for human-computer interaction have attracted increasing attention. This work proposes a hybrid framework that combines mathematical modeling and bio-inspired optimization algorithms to generate motion sequences from skeletal data. The framework takes as input a complete skeletal sequence corresponding to a given action and optimizes both the number of key poses and the parameters of a homotopy-based formulation to generate transitions between consecutive poses. A homotopy-based approach is used to compute transitions between selected key poses. The homotopy parameter $\lambda$ serves as an indicator of the completeness of the transition between pairs of key poses. Four nature-inspired optimization algorithms: Genetic Algorithm, Micro Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization were evaluated to determine the number of key poses and homotopy parameters that enable feasible motion generation. Dynamic Time Warping (DTW) is used as an external metric to assess the similarity between generated and reference sequences. It is important to note that Dynamic Time Warping (DTW) should be considered as a sequence similarity measure, as it does not explicitly evaluate perceptual realism or biomechanical plausibility. The framework was evaluated on 18 action sequences, demonstrating its ability to generate feasible motion transitions in 16 of the 18 evaluated actions when using PSO and MicroGA. For each pair of key poses, a fixed number of intermediate frames is generated to provide a uniform temporal discretization of the motion. The results suggest that homotopy-based methods provide a feasible approach for animation-based interaction systems. Full article

Micro-expressions, subtle and often asymmetric facial movements, play a pivotal role in nonverbal emotional communication. Addressing the core challenges of temporal misalignment, fragmented feature extraction, and slow real-time detection in micro-expression recognition (MER), we propose a novel dual-branch spatiotemporal model for dynamic sequence MER. Leveraging MediaPipe for 3D facial feature extraction and Dynamic Time Warping (DTW) for sequence alignment, our method nonlinearly maps variable-length sequences to a fixed length. A hybrid data augmentation technique enhances model robustness, while the dual-branch network simultaneously captures local spatial features and global temporal dynamics. Experimental results on the CASMEII dataset demonstrate state-of-the-art performance with 99.22% accuracy, along with a significant improvement in real-time detection speed. This approach holds substantial practical value for applications in deception detection, mental health assessment, and human–computer interaction. Full article

Accurate prediction of seawall deformation is essential for further structural health assessment and early warning of potential instability. However, inclinometer monitored deformation data usually exhibit strong depth-dependent heterogeneity, nonlinear temporal evolution, and spatiotemporal coupling, which make conventional single-sequence prediction methods insufficient. To address this issue, this study proposes a spatiotemporal prediction method for seawall inclinometer-monitored deformation data based on Dynamic Time Warping (DTW) clustering and an improved seahorse optimization-based Transformer (ISHO-Transformer). First, considering that deformation sequences at different depths may present similar deformation trends with nonlinear temporal misalignment, DTW is employed to measure the similarity between depth-wise monitoring sequences, and hierarchical clustering is introduced to classify depths with similar deformation patterns. Subsequently, for each clustered depth group, a Transformer-based prediction model is constructed to characterize the coupled evolution of monitoring location, depth, and time. To further enhance model performance and reduce the uncertainty of manual parameter tuning, the improved seahorse optimization (ISHO) algorithm is used to adaptively optimize key Transformer hyperparameters. Ultimately, the proposed method is validated using measured seawall inclinometer data and compared with several benchmark models. The results demonstrate that the proposed framework can effectively improve prediction accuracy, providing a useful tool for seawall deformation analysis and safety monitoring. Full article

This study investigates the spatiotemporal evolution and migration path coupling of the “water–land–energy–carbon” nexus system in the Beijing–Tianjin–Hebei region from 2002 to 2023 using multi-source data. The Coefficient of Variation and Shannon entropy were employed to assess the stability of elements, while Dynamic Time Warping (DTW) was applied to couple their migration paths. The results reveal the following: (1) Terrestrial water and groundwater exhibited similar evolution patterns, though groundwater showed greater volatility. Land use remained stable, with primary conversion being cropland to impervious. Nighttime light intensity increased significantly in urban areas, reflecting growth in energy consumption. Carbon emissions increased in most areas but decreased in some urban centers. (2) Element centroids displayed differentiated migration: water resources and cropland shifted southwest, and ecological land expanded northwest, while impervious, carbon emissions, and nighttime light concentrated in the southeast and northeast. (3) Two strongly coupled paths were identified: “terrestrial water–groundwater–cropland,” reflecting agricultural dependence on water resources, and “impervious –nighttime light–carbon emissions,” revealing the linkage between urban expansion, energy consumption, and carbon emissions. This study reveals the migration patterns of factors driven by both natural factors and human activities, providing quantitative support for resource optimization and low-carbon development policies in the Beijing–Tianjin–Hebei region. Full article

Background: Evening meal-timing chronotypes often exhibit lower calcium intake; however, whether this relationship remains significant after accounting for overall diet quality remains unclear. This study examined the association between meal-timing chronotypes and calcium intake and evaluated whether this association is maintained after adjusting for overall diet quality. Methods: This cross-sectional study analyzed 3465 adults aged 30–49 years from the 2016–2018 Korea National Health and Nutrition Examination Survey. Meal-timing chronotypes were identified using dynamic time warping-based K-means clustering of 24-h energy intake distributions. Survey-weighted linear regression assessed the association between meal-timing chronotype and calcium intake and tested their interaction with the Korean Healthy Eating Index (KHEI; excluding dairy) to evaluate the moderating effect of diet quality. Multinomial logistic regression was conducted to estimate odds ratios (ORs) for low calcium intake according to meal-timing chronotypes. Models were adjusted for age, sex, education, occupation, household income, and physical activity. Results: After adjusting for sociodemographic and lifestyle factors, the evening meal-timing chronotype was significantly associated with higher odds of low calcium intake (OR = 2.2, p < 0.001). A significant interaction between chronotype and KHEI tertiles on calcium intake was observed (p < 0.001). Specifically, while calcium intake generally decreased as diet quality declined, individuals with an evening preference consistently showed significantly lower calcium intake across all KHEI tertiles compared to the morning preference group (β = −7.9, p < 0.001). Conclusions: The evening meal-timing chronotype showed a significant association with lower calcium intake, which remained significant even after accounting for overall diet quality. These findings suggest that circadian-related eating patterns, rather than just overall diet quality, play a structural role in determining calcium intake. Full article

This study investigated the immobilization of Chlorella sp. in a nylon matrix to analyze its retention behavior and monitor biomass adhesion. Image capture and processing techniques were combined with capacitance measurements over time, using a Python-based data analysis code. The experiment was carried out in a 2 L photobioreactor under controlled conditions (24 °C, continuous aeration at 9.31 L/min, and light intensity of 71 μmol m⁻² s⁻¹). The methodology allowed for quantification of biomass distribution on the matrix surface, as well as changes in the capacitance and optical density of the microalgal culture. The results indicated maximum growth around day 15, showing a strong correlation between optical density (absorbance at 686 nm), image analysis of the matrix, and capacitance records. At this point, absorbance reached 3.913, coverage of 24.56% on the nylon matrix, and capacitance of 375.9 μF. Capacitance measurement proved to be a useful indirect tool to estimate biomass adhesion, while image analysis provided spatial distribution. The observed upward trend in process variables highlights the potential of electrical parameters, such as capacitance, for monitoring microalgal immobilization in suspended systems without altering biofilm structure. This approach supports future applications in scaling processes for bioactive compound production or environmental treatment systems. Full article

As global energy demand continues to grow, the inherent safety requirements for natural gas long-distance pipelines are becoming increasingly stringent. Therefore, accurately analyzing the trends in pipeline defects using multi-round internal inspection data is of great significance for enhancing pipeline inherent safety levels and reducing the risk of pipeline medium leakage. However, existing pipeline in-line inspection data alignment methods for long-distance multi-round pipeline data alignment suffer from cumbersome alignment procedures and low computational efficiency. This paper proposes an adaptive threshold dynamic time warping defect alignment method (Adaptive Dynamic Threshold-Dynamic Time Warping, ADTH-DTW) for rapidly matching multi-round in-line inspection data. A new multi-round in-line inspection data alignment framework based on valve-weld-defect is established. By integrating the DTW algorithm into each alignment stage, unnecessary manual effort is avoided, significantly improving data alignment efficiency. First, the ADTH method is used to clean redundant weld seam data in the in-line inspection data. By dynamically generating expected values and combining an intelligent point selection strategy, the method accurately identifies and removes interfering data. Additionally, valve chamber data is used to correct the overall mileage, providing a data foundation for subsequent defect alignment. Second, the dynamic time warping algorithm is used to align weld seam data and establish a data mapping table. Finally, relative displacement methods are employed to achieve defect matching. The validation results from three rounds of in-vehicle inspection data tested on-site indicate that the ADTH-DTW algorithm achieves an average 23.08% improvement in alignment accuracy compared to methods such as DTW, KL divergence, JS divergence, and linear interpolation, with computational efficiency nearly tripled. This effectively addresses the issue of incompatible computational efficiency and accuracy in existing data alignment algorithms, thereby enhancing the intrinsic safety level of natural gas long-distance pipelines. Full article

This study introduces a novel unsupervised framework, ICR-LLS, for detecting pathological gait patterns using instantaneous center of rotation (ICR) trajectories of the shank in the sagittal plane. ICR trajectories were computed from two-dimensional kinematic data captured at the lateral femoral epicondyle and lateral malleolus for both shanks, producing four-dimensional multivariate time series for each gait trial. Pairwise trajectory dissimilarities were quantified using circularly aligned Dynamic Time Warping (DTW), preserving temporal and spatial structure. The resulting dissimilarity matrix was embedded into a three-dimensional space using a force-directed network layout, enabling intuitive visualization of inter-subject gait relationships. Density-based clustering (DBSCAN), enhanced with a consensus-based ensemble approach, was employed to automatically identify clusters representing typical (healthy) gait patterns and outliers corresponding to pathological deviations. The framework is evaluated on a public dataset comprising individuals with Parkinson’s disease (PD) and healthy controls, achieving a normalized mutual information (NMI) of 0.449 and a Separation-to-Compactness Ratio (SCR) of 6.754, indicating a meaningful cluster structure. In addition, classification-oriented metrics yield an accuracy of 90%, sensitivity of 70%, and specificity of 96.7%, supporting the method’s effectiveness in distinguishing pathological gait. By combining minimal 2D kinematic inputs with unsupervised learning, ICR-LLS provides an interpretable framework for the exploratory analysis of gait variability, and although further validation is required, the findings suggest that ICR trajectories may serve as a meaningful biomechanical descriptor for characterizing pathological locomotion. Full article

Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement. 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

Imaginary handwriting is an important research paradigm in the field of brain-controlled typing. Neural signals exhibit high complexity, low signal-to-noise ratio, and strong temporal and environmental variability, leading to significant inter-trial differences in the temporal dynamics of character-related signals. These factors pose significant challenges for segmenting character-related signals and accurately decoding imaginary handwriting. To address these issues, this study proposes a Dynamic Time Warping Independent Component Analysis (DTWICA) framework. This framework employs FastDTW to construct individualized warping functions for each trial, followed by FastICA-based decomposition to separate the signal into distinct temporal and neuronal factors. The decomposed temporal factors are then mapped and transformed using the warping function and subsequently merged with the neuronal factors to reconstruct the signal. A sliding time window is then applied for adaptive processing, yielding the transformed signal. Finally, the transformed signals from multiple trials are averaged to generate a template for each character. Results based on a publicly available neural signals dataset for imaginary handwriting indicate that, compared with mainstream time warping models such as Shift, Linear, Piecewise, and TWPCA, the proposed model improves the character decoding accuracy for 31 characters by 14%, 13%, 7%, and 2%, respectively. This study not only constructs effective character signal templates but also facilitates accurate character segmentation during unlabeled imagined typing in an offline setting, providing a promising methodological basis for future real-time imagined typing decoding systems. Full article

With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential default behaviors in decentralized markets. This paper proposes a novel co-optimized scheduling model for urban MMG systems, centered on a unified “Social–Economic–Physical” coupling framework. To ensure transaction integrity, a robust reputation evaluation framework is developed using Root Mean Square Error (RMSE), mean absolute error (MAE), plus Dynamic Time Warping (DTW). This framework effectively identifies fraudulent data or contractual breaches. Furthermore, to enhance fairness while promoting decarbonization, the model integrates a dynamic network pricing strategy based on the Shapley value. It works alongside a reputation-weighted reward–penalty step-type carbon trading scheme. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved using MATLAB R2025b with CPLEX 12.10. Simulation results demonstrate that the integrated approach significantly optimizes system performance. Total carbon emissions are reduced by 49.6 tons. Meanwhile, revenues for the MMG Alliance, individual microgrids, and shared energy storage operators increase by 4.08% to 33.00%. The proposed framework provides a practical governance solution for Smart City multi-microgrid systems, effectively addressing the “trust-risk” challenge in decentralized urban energy markets. The findings validate that the proposed mechanism effectively fosters a trustworthy trading environment, achieving a “win-win” outcome for economic profitability and urban energy resilience. Full article

Background: In rehabilitation medicine, efficient gait analysis is crucial for evaluating postoperative recovery and frailty, especially given the increasing burden on clinicians due to an aging population. Objectives: This study aims to conduct preliminary validation of an automated linear walking evaluation system using 2D AI posture tracking. By evaluating the basic accuracy of the system on healthy individuals, we aim to establish a technical foundation for future introduction into clinical rehabilitation settings. Methods: In this observational study, we utilized a standard visible light camera for practical use. To evaluate accuracy, we compared 2D AI tracking against a gold-standard three-dimensional (3D) motion capture system during normal walking trials with 10 healthy participants. Specifically, we employed Dynamic Time Warping (DTW) to temporally align the asynchronous data streams from the 2D and 3D systems, ensuring precise comparison of joint angles. Results: Following the DTW-based alignment, the similarity with the 3D system was 0.806 ± 0.094 overall (Left: 0.797 ± 0.101, Right: 0.814 ± 0.086). Conclusions: In this preliminary validation, the proposed 2D AI posture tracking showed good agreement with the gold standard 3D motion capture for gait in healthy individuals. While the average systematic bias was within clinically acceptable limits, the observed limits of agreement suggest that this system is currently optimal as a foundational tool for gait screening. These results establish a technical foundation for the clinical application of this system. Full article

Air pollution is one of the fundamental environmental problems that directly threaten public health, ecosystems, and sustainable urban life in regions with high industrialization and urbanization density. This study aims to investigate whether the air pollution dynamics in Gaziantep and Kilis, two neighboring cities in Turkey, exhibit distinctive city-specific characteristics in their time series. In this context, Dynamic Time Warping (DTW) distance matrix and hierarchical clustering approaches were applied to compare the temporal behavior of pollutants from daily time series of PM10, SO₂, CO, and O₃ measurements across provinces between 2021 and 2025. Random Forest (RF), XGBoost, and Support Vector Machines (SVM) models were then developed to examine the separability of cities based solely on pollutant concentrations. The results revealed that the RF and XGBoost models successfully classified the two cities with over 93% accuracy. Additionally, SHAP analysis was used to interpret the contribution of each pollutant within the classification models, indicating that PM10 and SO₂ have relatively higher importance in distinguishing between the two cities. It should be noted that SHAP provides model-based interpretability rather than a direct representation of physical or atmospheric mechanisms. The findings suggest that pollutant time series may exhibit statistically distinguishable structures even between neighboring cities. Full article

Accurate short-term photovoltaic (PV) power forecasting is crucial for maintaining grid stability. However, existing hybrid deep learning models suffer from inherent limitations owing to static feature-weighting mechanisms, often displaying significant phase lag and peak clipping under severe meteorological fluctuations. To address this, in this study, we adopt a hybrid CNN–LSTM–Attention forecasting framework incorporating an SE-based attention strategy. Field validation at a 150 kW PV power plant in Ningxia, China, demonstrated that the adopted model achieved a Root Mean Square Error (RMSE) convergence of 2.157 kW. Notably, this represented a 41.92% reduction in error compared to the standard LSTM benchmark and a further 16.46% improvement over the suboptimal CNN-LSTM baseline, explicitly confirming the specific contribution of the SE-based attention mechanism. Moreover, multi-weather evaluations and ablation studies confirm the framework’s robustness. Dynamic Time Warping (DTW) and Diebold–Mariano (DM) tests establish its statistical superiority and the reduction in phase lag against baselines. Residual analysis reveals a leptokurtic distribution with white noise properties, confirming the reduction in systematic bias. Consequently, this high-fidelity tracking allows precise minute-level ramping detection and decreases spinning reserve demands in practical power dispatch. Full article