Search Results (420)

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

The implementation of digital twin (DTw) in infrastructure management is becoming increasingly important. Although digitalization in the Architecture, Engineering, Construction, and Operations (AECO) sector is progressing slowly, enabling technologies such as Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT) and data management allow for more informed and efficient management of ageing and highly complex assets. With the aim of improving the operation and maintenance (O&M) of transport infrastructure, the use of an integrated BIM–GIS model is proposed as the basis for a future DTw for an existing highway, the M-30 urban ring road in Madrid. This study develops an as-built digital model based on real GIS data, point clouds and BIM (LOD 300), adapting it to existing management systems using a relational database with unique identifiers. The infrastructure is modelled in a segmented and georeferenced manner, incorporating roads, tunnels, bridges and equipment as independent entities. Access to the model is guaranteed through 3D GIS scenes, interactive panels and BIM viewers geared towards management. In addition, a cost–benefit analysis is carried out using a Return On Investment (ROI) that evaluates the implementation of BIM in the management of this infrastructure. Full article

Data-driven approaches have emerged as powerful tools for analyzing process data. This study focuses on two data-driven methods: n-gram chi-square feature selection for extracting key action segments and K-medoids clustering combined with Dynamic Time Warping (DTW) distance for identifying behavioral patterns. To address the limitations that arise when applying these methods to complex tasks where ambiguous raw actions often hinder interpretation, this study introduces distance-based effectiveness indicators to enhance both data-driven methods for analyzing actions in the context of complex problem-solving. The research examines how representing action sequences through state effectiveness ($d_s$) and transition effectiveness ($\mathsf{\Delta }{d}_{s\to s^{\prime }}$) indicators outperforms the use of raw actions alone within the complex collaborative problem-solving Balance Beam task. Results consistently demonstrated that effectiveness indicators significantly improved the sensitivity of n-gram feature selection, the performance of clustering, and the interpretability of both n-grams and resulting clusters. Specifically, state effectiveness representations ($d_s\to d_{s^{\prime }}$) yielded the best outcomes. These findings advocate for the integration of effectiveness indicators into data-driven process analytics to more effectively capture and explain behavioral patterns of problem-solving. Full article

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

An intelligent multi-step action recognition system was designed for long-term caregiver training and assessment. Leveraging MediaPipe for precise and real-time human pose estimation, the system extracts detailed spatiotemporal body and hand keypoints. Temporal convolutional networks are employed to effectively capture temporal dependencies and complex features from sequential motion data. Dynamic time warping provides robust sequence alignment, allowing flexible comparison between performed actions and standard templates despite temporal variations in execution speed or style. A finite state machine imposes logical constraints by modeling expected action step sequences, enabling accurate detection of sequence anomalies or deviations. This hybrid architecture supports comprehensive evaluation and real-time feedback, facilitating improved caregiver skill acquisition, process adherence, and quality control within long-term care settings. The system aims to advance digital transformation in healthcare education by providing a scalable, precise, and adaptive training solution. Full article

Electrocardiogram waveform delineation is a fundamental task for quantitative cardiac analysis, yet accurate and consistent estimation of waveform boundaries remains challenging due to heart rate variability, inter-subject morphological differences, and nonlinear temporal distortions across cardiac cycles. Conventional rule-based methods and pointwise Dynamic Time Warping approaches are sensitive to amplitude variations and baseline fluctuations, while deep learning–based models require large annotated datasets and often suffer from limited interpretability and generalization. In this study, we propose a morphology-oriented ECG waveform alignment framework based on Pearson correlation–based Dynamic Time Warping (PCDTW). By integrating window-level matching with a correlation-driven cost function, the proposed method explicitly emphasizes local morphological similarity rather than absolute amplitude differences. Each ECG record is aligned using a subject-specific reference cycle constructed from normalized RR intervals, enabling stable correspondence of waveform boundaries without any training process. The proposed method was evaluated on two publicly available databases, the QT Database (QTDB) and the Lobachevsky University Electrocardiography Database (LUDB). Experimental results show that PCDTW significantly reduces QT and QTcB estimation errors compared with conventional DTW variants, demonstrating improved temporal consistency and lower bias across cardiac cycles. In particular, the mean QTcB error was reduced to 28.14 ms, compared with 124.54 ms obtained using conventional DTW. In addition, on LUDB, the overall mean delineation error for the P wave, QRS complex, and T wave boundaries was 10.68 ms, showing comparable or superior performance to state-of-the-art deep learning–based methods despite requiring no external training data. These findings indicate that morphology-aware, correlation-based temporal alignment provides a robust and interpretable alternative for ECG waveform boundary detection under realistic physiological variability. Full article

Real-time processing demands for massive IoT sensor data necessitate reliance on distributed microservice systems within edge clusters. However, pinpointing the root cause of anomalies within these edge microservice clusters poses a critical challenge for intelligent IoT operation and maintenance. To address the issue, a spatio-temporal graph propagation model ST-GraphRCA is proposed for root cause analysis in IoT edge environments. Our approach begins by resolving the fundamental issue of time-series asynchrony across distributed multi-source metrics. A PCA-DTW hybrid feature extraction method is introduced with a dynamic alignment strategy to mitigate the effects of random network delays and data deformation without requiring prior synchronization. Subsequently, ST-GraphRCA constructs a stream-based forward propagation graph based on the flow conservation principle. By integrating dynamic edge weights with node-level input–output anomaly scores, ST-GraphRCA precisely infers fault propagation pathways and identifies potential root cause candidates through causal reasoning. Finally, a topology-constrained high-utility mining algorithm filters these candidates. Using a constraint matrix, the algorithm filters out unreachable service combinations to locate low-frequency and high-risk root causes. Experimental results indicate that ST-GraphRCA achieves an F1-Score of 0.89, outperforming existing methods. In resource-constrained edge scenarios, its average localization time is merely 238.8 ms, representing a six-fold improvement over key benchmarks. Thus, ST-GraphRCA not only provides an efficient anomaly fault tracing solution for large-scale IoT systems but also offers technical support for the intelligent operation and maintenance of distributed microservice systems. Full article

This paper presents a pilot study on cross-device variability in online signature dynamics captured on consumer Samsung devices using S-Pen technology. Signature data were acquired on two devices, a Galaxy Ultra smartphone and a Galaxy Tab S6 Lite tablet, through a unified web-based interface designed to ensure consistent capture across platforms. The acquisition process recorded timestamped x–y trajectories, stroke events, and pressure information when available, preserving temporal structure for dynamic analysis. Genuine signatures were systematically divided into reference and test sets, and comparisons were performed under intra-device conditions (enrollment and verification on the same device) and cross-device conditions (enrollment and verification on different devices). Similarity was evaluated using Dynamic Time Warping (DTW) on multivariate time series, with analysis focused on how differences in form factor and writing area influence signature behavior. This problem is directly relevant to mobile biometric authentication workflows, where users frequently enroll on one device and later verify on another; under this mismatch scenario, reduced separability between genuine and impostor scores can affect decision reliability. Consistent with this interpretation, the results show lower dissimilarity in intra-device comparisons and higher distances with ROC degradation under cross-device mismatch. These findings provide exploratory evidence that device heterogeneity is a practical factor in mobile signature verification and support the need for cross-device-aware design in authentication systems used for digital transactions and document authorization in real-world mobile environments. Full article

The increasing share of photovoltaic (PV) generation in building energy systems highlights the importance of understanding not only the magnitude but also the temporal structure of energy mismatch between PV production and building demand. This study proposes a Dynamic Time Warping (DTW)-based framework for the analysis of daily temporal mismatch patterns in a building-integrated photovoltaic system using high-resolution measurement data. Daily temporal signatures are constructed from normalized PV generation and building load profiles, allowing the analysis to focus exclusively on temporal deformation rather than absolute energy values. Pairwise DTW distances are used to construct a distance matrix that captures similarities between daily mismatch structures over an entire month. The resulting DTW distance matrix enables not only pairwise comparison of daily mismatch patterns, but also the identification of representative, transitional, and extreme days through ranking and hierarchical organization of temporal signatures. Hierarchical clustering with average linkage reveals distinct families of days characterized by similar types of temporal deformation, while a ranking based on average DTW distance provides a compact diagnostic summary of monthly variability. The findings demonstrate that PV–building energy matching is inherently time-structured, forming recurrent temporal families of days that cannot be identified using aggregate energy metrics alone. The proposed framework provides a robust diagnostic layer for time-aware energy analysis and supports the development of advanced control and management strategies that explicitly address temporal mismatch in building-integrated photovoltaic systems. Full article

This paper presents RehabHub, a home-based exergaming system that integrates standardized physical assessment directly into gameplay by using a common webcam and MediaPipe for real-time pose estimation. The system quantifies upper-limb movement quality, specifically abduction, shoulder flexion, and elbow flexion based on FMA-UE guidelines, by applying Dynamic Time Warping (DTW) together with a Z-score-based scoring model that relies on data from non-clinical adult participants. A pilot study, which included movements simulated with a 5-kg resistance band, evaluated three feature-extraction methods. The findings indicate that the single-angle method provides the clearest distinction between normal and abnormal movements, particularly for abduction and elbow flexion. In the case of shoulder flexion, the score separation was less distinct because of movement variability and posture-related angle fluctuations, which suggests that further refinement of feature design is needed. The cloud-based platform supports remote monitoring and gives caregivers access to both performance scores and recorded exercise videos. Overall, the results demonstrate the feasibility of a low-cost webcam-based assessment integrated into exergaming, and they highlight important trends for improving abnormal-movement detection in home rehabilitation systems. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders where early detection is vital. However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability. To address these limitations, this study proposes a cross-subject, single-channel electroencephalography (EEG)-based method that uses Multi-Entropy Feature Concatenation (MEFC) to classify AD and FTD. First, single-channel EEG is processed through the Discrete Wavelet Transform (DWT) to extract five rhythms: delta, theta, alpha, beta, and gamma. Subsequently, Permutation Entropy (PE), Singular Spectrum Entropy (SSE), and Sample Entropy (SE) are calculated for each rhythm and concatenated to form a combined MEFC to characterize the non-linear dynamic properties of EEG. Lastly, Dynamic Time Warping (DTW), Pearson Correlation Coefficient (PCC), Wavelet Coherence (WC), and Hilbert Transform Correlation (HTC) are employed to measure the similarity between unknown rhythmic MEFC and those from AD, FTD, and Healthy Control (HC) groups, performing a data-driven classification via similarity measurement. Experimental results on 88 subjects in the AHEPA dataset demonstrate that the beta-rhythm with PCC yields a three-class accuracy of 76.14% using single-channel FP2. In another dataset, the Florida-Based dataset, involving 48 subjects, theta-rhythm with WC achieves a two-class accuracy of 83.33% using FP2. Furthermore, a MATLAB R2023b-based toolbox is developed using the proposed method. Such outcomes are impressive, given the limited data per individual (data-efficient), reliable performance across new subjects (cross-subject), and compatibility with wearable devices (single-channel), providing a novel entropy-based approach for EEG-based applications in biomedical engineering. Full article

Material circularity in construction requires material information at the end of life for the trading of materials. Different digital technologies (DTs) are essential for such information management. This research aims to review key aspects of developing a blockchain-based material passports (MPs) system when integrating with key DTs used for MPs. This research is based on a critical literature review, with an integrative approach that synthesises both academic and grey literature. The literature search was initiated using chosen keywords relevant to the topic to first identify the key literature. This was followed by using a snowballing technique to expand the search with further relevant literature. Building Information Modelling (BIM), digital twin (DTw) and blockchain technology (BCT) were identified as key technologies for material information management. BIM and DTw are central to the management process as all the information created and collected is modelled, visualised, analysed and stored using BIM platforms. However, existing MP platforms utilising centralised databases to store data were found to be unreliable for managing material data in an industry like construction with a dispersed supply chain and typically longer lifecycle. BCT was realised as necessary for information management in construction, as it allows us to manage information in a more decentralised, transparent and immutable manner. Furthermore, examining current research about blockchain application for information management in construction led to the conclusion that, although the studies on blockchain-based MP platforms covering the entire industry supply chain prevail, the management of material data at the built asset level throughout its lifecycle using such MP systems is underexplored. Thus, building on the literature review, a conceptual model of blockchain-based MP system is proposed in this paper, describing integration with BIM and DTw, and with relevant processes and actors to manage MP information throughout the building lifecycle. Acknowledging the limitations of a subjective literature review, the conceptual model and the ideas are proposed as a foundation for further research and develop MP system with empirical validation. Although theoretically, this study identifies the suitability of blockchain technology for managing product lifecycle information in industry like construction and provides ground for further theoretical research for planning and policy required for blockchain-based MP development and implementation. Full article