Dynamic Time Research Articles

Integrating Dynamic Time Warping and K-means clustering for enhanced cardiovascular fitness assessment

The appropriate measurement of cardiovascular ’Performance Enhancement’ is critical for improving physical fitness and overall health. Traditional methods, effective as they are, often rely on complex, cumbersome equipment, limiting their practical use for real-time, dynamic exercise evaluation. Thus, understanding the intricate relationship between physiological responses and exercise ‘Performance Enhancement’ using wearable technology is essential for tailoring effective fitness regimes. This study involved 52 participants, utilizing Garmin Vivosmart 5 wearables to analyze heart rate time series during the YMCA Three-Minute Step Test, assessing fitness levels and characterizing personalized heartbeat dynamics. The study employed Dynamic Time Warping (DTW) for clustering these time series into high and low VO2max groups. Additionally, heart rate dynamics were examined using K-means clustering to identify distinct patterns during exercise—namely ‘Efficient Adaptation’, ‘Balance Under Pressure’, ‘Active Strain’, and ‘Efficiency Improvement’ clusters. This analysis demonstrated that non-trained individuals showed higher ‘Active Strain’ and ‘Efficiency Improvement’ dynamics and lower ‘Efficient Adaptation’ dynamics, indicating the exercise’s varied effectiveness based on training level. This method provides a novel approach for identifying individual fitness levels and the efficacy of specific exercises, enabling personalized physical activity planning.

Supporting Infants’ Motor Development through Water Activities: A Preliminary Case–Control Study

The first twelve months of an infant’s life are the most dynamic time in the development of motor activities. Water activities can positively stimulate the motor skills, visual perception, and cognitive abilities of infants. The purpose of this study was to evaluate the motor development of infants aged 3–12 months participating in water activities. Participants in the study included 43 infants aged 3–12 months who attended water activities classes at Warsaw City swimming pools (n = 21) and infants not attending any classes (n = 22). Two methods of assessing motor development were used in the study: the Alberta Infant Motor Scale (AIMS) and the Early Motor Development Questionnaire (EMQ). The raw scores of the AIMS test and the EMQ questionnaire were statistically significantly different (p < 0.05) in both groups between the first and second examinations. In contrast, there was a statistically significant improvement in motor development measured by AIMS and expressed in percentiles (p = 0.002) and in percentiles for Polish children (p = 0.030) in the water group. The age-independent total score of the EMQ before and after the intervention did not change significantly (p = 0.149). The water environment has the potential to support the motor development of infants aged 3–12 months.

BDS time spoofing detection method based on the dynamic time warping

BDS time spoofing detection method based on the dynamic time warping

A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.

Estimating travel time in the Helsinki region utilising sequential Bayesian inference

This paper explores application of Bayesian inference (BI) to dynamic travel time estimation in the Helsinki region. Accurate travel time prediction is crucial in a wide range of fields, including departure time and routing. Limited real-time data challenge modelling accuracy. To address this, this paper utilises BI, particularly sequential Bayesian inference (SBI) for evolving observed values. Incorporating 2018 real-time data and 2015 information as prior knowledge, travel time distribution will be updated. Validation yields a 4.0% mean absolute error between the updated 2018 distribution and actual travel time. Also, using the 2015 posterior distribution as prior by way of SBI yields a 4.6% mean absolute error. Results highlight that SBI is an effective tool for updating distributions. This paper underscores potential of BI in addressing data scarcity and enhancing accuracy of transportation models. By supplying precise travel time estimates, this approach benefits congestion relief and travel planning. With evolving travel time data, BI promises to advance transportation modelling.

The green Fintech paradox: understanding the dynamics of green brand positioning and user decision-making in the digital financial landscape

Purpose The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments. Design/methodology/approach This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights. Findings The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding. Research limitations/implications This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments. Originality/value This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.

Preface

It is a pleasure to welcome you to the 2024 International Symposium on New Energy Technologies and Power Systems (NETPS 2024). At this dynamic and innovative time, we were honored to have invite academic elites from all over the world to discuss the latest advances and cutting-edge research in new energy technologies and power systems. NETPS 2024 was held in Nanjing, China from March 22nd to 24th 2024 (virtual event). It focused on cutting-edge issues in new energy technologies and power systems, and provided a platform for academics to share knowledge, exchange views and promote collaboration. To make the program more informative and colorful, the Conference invited renowned scholars and experts from around the world, who brought us a series of fascinating thematic presentations. These reports covered multiple aspects of new energy technologies and power systems, including but not limited to hot topics such as New Energy Power Generation, Power System Communication and Control, Intelligent Energy Information Support Technology, Computational Intelligence in Electrical Engineering, Smart Grid, Big Data in Power Systems, etc. Experts and scholars including Prof. Ting Yang (Tianjin University, China), Prof. Pierluigi Siano (University of Salerno, Italy), Prof. Mohan Lal Kolhe, University of Agder (Norway), Norway, etc. performed dramatic presentations on their own researches. We believe that these reports have provided participants with both insight and inspiration, and promoted research and development in related fields. In addition, it featured oral presentations and poster presentations, providing an opportunity for graduate students and young researchers to present their research findings. It was an important platform for participants to exchange and interact with each other, helping to share experiences, promote cooperation and build new partnerships. We believe that through the interaction and cooperation in this Conference, we have deepened our understanding of relevant fields, promoted interdisciplinary integration, and provided new solutions to the complex problems facing the world today. Finally, we believe that the Proceedings of this Conference will be of great significance to the academic community. This Proceedings features outstanding papers from NETPS 2024, which represents the latest research results and innovative ideas in the fields. By publishing and sharing these papers, we are able to further expand the impact of our research, promote communication and collaboration among the academic community, and provide valuable references for future research. We would like to acknowledge all those who supported and participated in NETPS 2024. We look forward to have your active participation in the future series conference of NETPS! The Committee of NETPS 2024 List of Committee Member is available in this Pdf.

Comparison in aerodynamic modification and auxiliary damping device in mitigation of dynamic wind response of ‘V′ shaped tall building

This article proposes a unique method to find out dynamic response due to wind and also suggests best control method among aerodynamic modification and Auxiliary Damping Device. In this study, the major purpose is to explore the vibration mitigation performance of corner modification and Multiple Tuned Mass Damper (MTMD) installed in the V shaped tall building. Various cases have been made by considering different corner chamfered ratio and corner rounded ratio maintaining same plan area exposed to wind environment. The aerodynamic rms coefficients are calculated using numerical modelling based on RANS k-ε turbulence model from computational fluid dynamics (CFD). Dynamic wind time history in along and crosswind is obtained using maximum force coefficient. All corner modified model and basic V-shaped model with MTMD considering various mass ratio is analysed in along and crosswind direction under aforesaid dynamic wind. The dynamic responses are obtained for all models with corner modification and MTMD to investigate the effectiveness of corner modification and MTMD device. Finally, we found that corner modification mitigates the force as well as displacement however wind-induced vibration (acceleration) can be drastically reduced by installing MTMD, so combination of both modifications can improve the efficiency to minimize the dynamic response of a V plan shaped tall building.

Synchronization under saturable nonlinearity.

In this theoretical work, we introduce a nonlinear gain saturation law representative of the experimentally observed properties manifested by phenomena ranging from aeroacoustic shear layers in self-sustained cavity oscillations to flame heat release rate in thermoacoustic instabilities. Furthermore, this type of saturable gain may be relevant for a wider class of physical systems, such as active laser media in photonics. The nonlinearity discussed herein governs the fullscale behavior of a self-oscillator exhibiting linear loss under large amplitude perturbations, in contrast to the cubic damping and linear gain of the Van der Pol model. A distinctive characteristic of the proposed equation is the simple, well behaved gain term in the slow timescale dynamics.

AISClean: AIS data-driven vessel trajectory reconstruction under uncertain conditions

In maritime transportation, intelligent vessel surveillance has become increasingly prevalent and widespread by collecting and analyzing high massive spatial data from automatic identification system (AIS). The state-of-the-art AIS devices contain various functionalities, such as position transmission, tracking navigation, etc. Widely equipped shipboard AIS devices provide a large amount of real-time and historical vessel trajectory data for maritime management. However, the original AIS data often suffers from unwanted noise (i.e., poorly tracked timestamped points for vessel trajectories) and missing (i.e., no data is received or transmitted for a long term) data during signal acquisition, transmission, and analog-to-digital conversion. This degradation in data quality poses significant risks, including potential miscalculations in vessel collision avoidance systems, inaccuracies in emission calculations, and challenges in port management. In this work, a data-driven vessel trajectory reconstruction framework considering historical features is proposed to enhance the reliability of vessel trajectory. Specifically, a series of statistical methods are proposed to identify noisy data and missing data. Then, a model combining Geohash and dynamic time warping algorithms is developed to restore the trajectories degraded by random noise and missing data in vessel trajectories. Comparative experiments with baseline methods on multiple datasets verify the effectiveness of the proposed data-driven model.

Cavity Controlled Upconversion in CdSe Nanoplatelet Polaritons.

Exciton-polaritons provide a versatile platform for investigating quantum electrodynamics effects in chemical systems, such as polariton-altered chemical reactivity. However, using polaritons in chemical contexts will require a better understanding of their photophysical properties under ambient conditions, where chemistry is typically performed. Here, we used cavity quality factor to control strong light-matter interactions and in particular the excited state dynamics of colloidal CdSe nanoplatelets (NPLs) coupled to a Fabry-Pérot optical cavity. With increasing cavity quality factor, we observe significant population of the upper polariton (UP) state, exemplified by the rare observation of substantial UP photoluminescence (PL). Excitation of the lower polariton (LP) states results in upconverted PL emission from the UP branch due to efficient exchange of population between the LP, UP and the reservoir of dark states present in collectively coupled polaritonic systems. In addition, we measure time scales for polariton dynamics ∼100 ps, implying great potential for NPL based polariton systems to affect photochemical reaction rates. State-of-the-art quantum dynamical simulations show outstanding quantitative agreement with experiments, and thus provide important insight into polariton photophysical dynamics of collectively coupled nanocrystal-based systems. These findings represent a significant step toward the development of practical polariton photochemistry platforms.

Individual differences and motor planning influence self-recognition of actions.

Although humans can recognize their body movements in point-light displays, self-recognition ability varies substantially across action types and participants. Are these variations primarily due to an awareness of visually distinct movement patterns, or to underlying factors related to motoric planning and/or individual differences? To address this question, we conducted a large-scale study in self-action recognition (N = 101). We motion captured whole-body movements of participants who performed 27 different actions across action goals and degree of motor planning. After a long delay period (~ 1 month), participants were tested in a self-recognition task: identifying their point-light action amongst three other point-light actors performing identical actions. We report a self-advantage effect from point-light actions, consistent with prior work in self-action recognition. Further, we found that self-recognition was modulated by the action complexity (associated with the degree of motor planning in performed actions) and individual differences linked to motor imagery and subclinical autism and schizotypy. Using dynamic time warping, we found sparse evidence in support of visual distinctiveness as a primary contributor to self-recognition, though speed distinctiveness negatively influenced self-recognition performance. Together, our results reveal that self-action recognition involves more than an awareness of visually distinct movements, with important implications for how the motor system may be involved.

An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systems

Advanced production systems, such as multi-step assembly processes, predominantly comprise repetitive operations. The repetitive manual or human-robot integrated production operations call for new real-time process management technologies as the increasing use of sensors and the development of system intelligence. Conventional process monitoring and management methods, which are often labor-intensive, fall short in providing immediate and actionable insights. To tackle this limitation, we develop an unsupervised embedding method to automatically delineate the process into different stages and predict real-time progress information. We propose a Contrastive Variational Autoencoder (CVAE) as a feature extractor to adeptly embed repetitive processes into a Gaussian Mixture Model (GMM). Based on the extracted features, we propose an adaptive change-point detection and an Iterative Dynamic Time Wrapping (IDTW) algorithm to identify and segment multiple standardized process stages automatically. Theoretically, we establish the asymptotic optimality of the detected change-points associated with the given precision of image and feature extractors, ensuring the high-quality process stage separation and labeling. The proposed method autonomously extracts essential features encapsulating progress information from a limited set of unlabelled process videos. Through four diverse case studies including an actual aircraft spoiler production, our method exhibits very promising performance. Specifically, it achieves an average of 98.14% accuracy in predicting production progress and 0.9202 area under the curve (AUC) in predicting progress deviation across three distinct production environments. The proposed process monitoring method in repetitive production systems has the potential to significantly improve productivity, promote standardization of repetitive operations, and predict production deviations.

Semantic Web Techniques for Extracting and Analyzing of Cropland Abandonment in Hilly Areas

Due to rising rural labor costs, farmland abandonment is common in China's hilly areas. Timely, accurate extraction of its spatiotemporal changes is crucial for sustainable farmland use. This study presents a novel approach for extracting abandoned cropland using NDVI time series from Sentinel-2 satellite images, exploiting the difference in vegetation phenology between abandoned and cultivated croplands. Dynamic Time Warping (DTW) algorithm was used to determine the similarity between NDVI time-series curves of different cropland types. The similarity metrics were used to find the optimal NDVI threshold for abandoned cropland through F1-score evaluation. The method's overall accuracy is 92.4%, higher than comparison methods at 84.60% and 75.6%. The approach captures year-round vegetation changes, expands time dimension data, and improves accuracy. Spatiotemporal analysis revealed decreased patch size, increased shape complexity, and more dispersed distribution of abandoned cropland over time. Major factors were proximity to settlements, roads, water bodies.

Research on automatic identification of coal mining subsidence area based on InSAR and time series classification

Large-scale mining of coal resources has caused surface subsidence, threatening the ecological environment and surface safety and hindering the sustainable development of coal resource-based cities seriously. In order to identify coal mining subsidence area more accurately and quickly, a novel method for automatic identification of coal mining subsidence area was proposed in this study, which is based on InSAR technology and time series classification algorithm. The method comprehensively considered the quality of data acquisition and the degree of automation of algorithm implementation, realizing automatic identification of large-space coal mining subsidence area in the case of small samples. Firstly, the unclassified subsidence dataset is constructed by SAR data around time series combined with InSAR technology. Secondly, the degree of change of time series subsidence is used as the basis of classification, and it is divided into two categories: regular type of coal mining subsidence and irregular type of non-coal mining subsidence. Finally, Dynamic Time Warping (DTW) algorithm is used to calculate the similarity distance between the time series curves of each image element of the subsidence dataset to be tested and the standard coal mining subsidence type curves, which aim at identifying the coal mining subsidence area. Through utilizing the multitude filtering algorithm to filter the recognition results, the automatic recognition results of coal mining subsidence area is finally obtained. The results show that this method can still be used to efficiently identify and circle coal mining subsidence area with only 640 samples, and the average Overall Accuracy (OA) and Kappa Coefficient (KC) of coal mining subsidence area are 0.91 and 0.83, respectively. The method can not only identify the existing and potential subsidence area in the mining area, but provide technical support for ecological environment restoration and early warning of subsidence damage.

Multiplier and investment in modern Russia in the context of stimulating economic development in the context of sanctions

Aim. To analyze the degree of investment impact on economic growth in modern Russia.Objectives. To study the multiplier effect as a key tool for stimulating economic activity and creating new jobs, developing innovations and reducing social inequality; to identify its significance for the national economy in modern conditions of inherent uncertainty.Methods. The research is based on the empirical study of the multiplier effect and gas pedal effect on the materials of the modern Russian economy. The analysis is based on the use of dynamic time series, definition of dependent and independent variables in the context of identified correlations between them. The data of literary sources on the assessment of the impact of the investment multiplier effect on economic growth were also studied.Results. The multiplier effect can have a significant impact on the formation of the structure of the economy, contributing to an increase in production, reducing unemployment and improving the standard of living of the population. The study of this effect allows us to better understand the mechanisms of economic functioning and develop recommendations for optimizing investment policy to achieve sustainable economic growth, which is important for modern Russia developing under sanctions pressure. The study of paired regression revealed a mathematical relationship between the increase in investment in fixed capital and the growth of gross domestic product (GDP), as well as between GDP growth and the increase in investment in fixed capital. The high correlation coefficient (r = 0.945896) and coefficient of determination (R² = 89.47 %) confirm that there is a strong relationship between fixed capital investment and GDP. This indicates that most of the changes in output can be explained through changes in fixed capital investment.Conclusions. The results of the analysis confirm the importance of investment in fixed capital as a key factor of economic growth and development in modern Russia. Strengthening investment activity allows not only to increase the level of economic development as a whole, but also contributes to the creation of favorable conditions for sustainable growth of income of the population, innovation activity, etc. In this regard, investment stimulation should become one of the priorities in the current Russian economic policy.

Multi-objective optimization design of rail grinding profile in Curve Section of Subway based on wear Evolution and representative worn profile

Curve rail grinding has always been one of the key points of subway maintenance and repair. The appropriate grinding can effectively reduce wear. A multi-objective optimization design method for grinding profiles in curved sections is proposed. Firstly, representative grinding profiles are selected as the initial population using the dynamic time regularization algorithm (DTW). Then, the optimal design range is determined based on wear characterization analysis, and mathematical expressions of wheel profiles are chosen as design variables to establish a parametric model. Next, the prediction model considering the evolution of wheel wear is incorporated into the multi-objective function, and objective function adaptive weight adjustment coefficient factors are introduced to establish the multi-objective optimization model for wheel profiles. The Latin hypercubic sampling method is employed to establish the RBF agent model for simulation calculation, and the optimization design of wheel profiles is carried out using the TS-NSGA-II multi-objective algorithm.

Accelerating time series similarity search under Move-Split-Merge distance via dissimilarity space embedding

The ubiquity sensory devices across scientific and industrial settings have made time-series data mining an important research topic in the IoT domain. Among all time-series mining techniques, the similarity search is considered the most essential and noteworthy because it is frequently employed as a fundamental subroutine for other procedures. The Dynamic Time Warping (DTW) was once considered the best similarity measure in time-series data mining community; however, two recent studies have demonstrated that the Move-Split-Merge (MSM) performs better than DTW in both supervised and unsupervised settings. Computing MSM between two time-series incurs costly computation because MSM has quadratic time complexity. Regrettably, there is little research on accelerating time-series similarity search under MSM. This paper provides an efficient method named DSEMSM for speeding up this procedure. DSEMSM utilizes dissimilarity space embedding to transform time-series into low-dimensional vectors. The filter-and-verification framework is subsequently employed to reduce the large number of actual MSM computations and to identify the final search results from the candidate set. The furthest prototype selection strategy and lower bound based technique are applied in DSEMSM to improve its retrieval accuracy and efficiency, respectively. We validate the effectiveness of the proposed method on a large number of datasets. The experimental results indicate that, in comparison to its relevant competitors, DSEMSM achieves a more stable speedup rate and higher retrieval accuracy.

Assessment of the seismic failure of reinforced concrete structures considering the directional effects of ground motions

Seismic intensity measures at different levels significantly impact the seismic damage of building clusters. Reinforced concrete (RC) structures are a type of building widely used in different regions worldwide. A large amount of field seismic damage observation data indicates that within the same seismic intensity zone, the directional effects of varying ground motions lead to significant differences in damage to RC structures on different floors. However, relatively little research has been conducted on estimating the seismic fragility and vulnerability of RC structures considering the directional effects of seismic action. To further study the directional effect of seismic motion on the damage of different floors, this study conducted dynamic time history and spectral analyses on 1472,379 acceleration records in multiple directions obtained from monitoring at nine actual seismic stations during the 2008 Wenchuan earthquake in China. This paper innovatively reports on the actual damage features of RC structures during the Mw6.2 magnitude Jishishan earthquake in Gansu Province, China, on December 18, 2023 (the most severe destructive earthquake in China in 2023) and analyses the influence of earthquake directionality on the damage to different floors of RC structures. A three-dimensional numerical model of a four-story RC frame structure was established using numerical simulation and the finite element method. Different intensity measures were taken to input seismic excitations of different intensities into the RC structure in the 0°, 30°, 60°, and 90° directions, and damage simulation analysis was conducted. The nonlinear dynamic time history curves of structures under multiple directions of seismic excitation in different intensity zones have been developed. Using the interstory stiffness and interstory displacement angle as engineering demand parameters, damage assessment curves and stress clouds for RC structures with 1–4 floors considering different seismic directional effects were generated. The analysis results and major findings indicate that the seismic sequence in the 0° direction has a relatively heavy impact on the first floor of the RC structure in zone IX, which is consistent with the actual field observation results.

Understanding multiple timescales in quantum dissipative dynamics: Insights from quantum trajectories

Understanding multiple timescales in quantum dissipative dynamics: Insights from quantum trajectories