Dynamic Time Research Articles

Is there a correlation among landing stability, ankle dorsiflexion range of motion, and ankle stiffness during single-leg landing?

BackgroundEven though both landing stability and ankle dorsiflexion stiffness (ankle DR-stiffness) have been independently identified as risk factors associated with non-contact injuries, no study has specifically investigated the relationship between these two variables.MethodsTwenty male recreational athletes volunteered to participate in this study. Their ankle DF-ROM based on the weight-bearing lunge test, and landing stability based on the dynamic postural stability index (DPSI) and time to stabilization (TTS) were evaluated during a single-leg landing (SLL).ResultsThere was no statistically significant difference among ankle DF-ROM, ankle DF-stiffness, DPSI, and TTS during the SLL task (p > .05). Although a moderately positive relationship was observed between ankle DF-ROM and ankle DF-stiffness (p = .177; r = .354 [95% CI, -.153 to .653]) and a moderately negative relationship between ankle DF-ROM and TTS (p = .163; r = -.375 [95% CI, -.598 to .098]), these were not statistically significant.ConclusionThe findings indicate that ankle DF-stiffness as an independent variable, does not significantly affect landing stability based on the TTS and DPSI indexes. However, its interaction with other variables, such as sex, age, and the nature of the movement task, may influence landing stability. This study area warrants further research.

Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory

To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.

A novel dynamic machine learning-based eXplainable fusion monitoring: Application to industrial and chemical processes

Abstract The complexity and fusion dynamism of the modern industrial and chemical sectors have been increasing with the rapid progress of IR 4.0 to 5.0. The transformative characteristics of Industry 4.0 to 5.0 have not been fully explored in terms of the fundamental importance of explainability. Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. This study presents a novel dynamic Machine Learning (ML) based eXplainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. The methodology aims to detect faults, identify their key causes and feature variables, and analyze the root path of fault propagation with the time and magnitude of one cause variable to another impact. This study proposed using a time domain multivariate granger-entropy-aided dynamic independent component analysis (ICA) - distributed canonical correlation analysis (CCA) approach, incorporating the dynamics time wrapping supported time delay-signed directed graph. The proposed methodology utilized the application to industrial and chemical processes and verified using the Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP) as practical application benchmarks. The framework's validations and efficiency are evaluated using established techniques such as classic computed ICA and Dynamic ICA as standard model scenarios. The outcomes and results showed that the newly developed strategy is preferable to previous approaches regarding explainability and robust detection and identification of the actual root causes with high FDRs and low FARs.

Sampling from the random cluster model on random regular graphs at all temperatures via Glauber dynamics

Abstract We consider the performance of Glauber dynamics for the random cluster model with real parameter $q\gt 1$ and temperature $\beta \gt 0$ . Recent work by Helmuth, Jenssen, and Perkins detailed the ordered/disordered transition of the model on random $\Delta$ -regular graphs for all sufficiently large $q$ and obtained an efficient sampling algorithm for all temperatures $\beta$ using cluster expansion methods. Despite this major progress, the performance of natural Markov chains, including Glauber dynamics, is not yet well understood on the random regular graph, partly because of the non-local nature of the model (especially at low temperatures) and partly because of severe bottleneck phenomena that emerge in a window around the ordered/disordered transition. Nevertheless, it is widely conjectured that the bottleneck phenomena that impede mixing from worst-case starting configurations can be avoided by initialising the chain more judiciously. Our main result establishes this conjecture for all sufficiently large $q$ (with respect to $\Delta$ ). Specifically, we consider the mixing time of Glauber dynamics initialised from the two extreme configurations, the all-in and all-out, and obtain a pair of fast mixing bounds which cover all temperatures $\beta$ , including in particular the bottleneck window. Our result is inspired by the recent approach of Gheissari and Sinclair for the Ising model who obtained a similar flavoured mixing-time bound on the random regular graph for sufficiently low temperatures. To cover all temperatures in the RC model, we refine appropriately the structural results of Helmuth, Jenssen and Perkins about the ordered/disordered transition and show spatial mixing properties ‘within the phase’, which are then related to the evolution of the chain.

The Interplay Between Muscular Activity and Pattern Recognition of Electro-Stimulated Haptic Cues During Normal Walking: A Pilot Study

This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill. Findings revealed variable recognition patterns across participants, with the BB showing more variability during walking due to its minimal activity compared to the actively engaged GL. Dynamic time warping (DTW) was used to assess the similarity between muscle activation and electro-stimulated haptic perception. The DTW distance between electromyography (EMG) signals and muscle recognition patterns was significantly smaller for the GL (4.87 ± 0.21, mean ± SD) than the BB (8.65 ± 1.36, mean ± SD), showing a 78.6% relative difference, indicating that higher muscle activation was generally associated with more consistent haptic perception. However, individual differences and variations in recognition patterns were observed, suggesting personal variability influenced the perception outcomes. The study underscores the complexity of human neuromuscular responses to artificial sensory stimuli and suggests a potential link between muscle activity and haptic perception.

Enrollment Management in Dynamic Times: Strategies from the Pandemic Era to Improve Community College Recruitment, Enrollment, and Retention

ABSTRACT The onset of the COVID-19 pandemic was followed by steep declines in community college enrollment in the United States, requiring institutions to quickly adapt to changing economic conditions and student needs. Through interviews and focus groups with 11 enrollment management leaders from community colleges across the country, this paper explores the strategies that community colleges employed to try to increase enrollment and retention during the first two years of the pandemic. We found that participants instituted a wide variety of targeted recruitment and enrollment tactics, while their institutions also made changes to academic programs and student support services to improve enrollment and retention. These strategies were often dependent on their institution’s local context and were in many cases only possible with the support of pandemic relief funding, leaving open questions about the long-term sustainability of pandemic-era enrollment management practices.

Multivariate Analysis of the Sustainable Development of the Silver Economy in the European Union Countries

The ageing of societies is one of the biggest challenges of the modern world. The response to this new reality is the development of the silver economy. It is a multidimensional concept that is an extremely important factor in sustainable development. The aim of this article is to compare EU countries according to selected aspects of the development of the silver economy. This study covered the years 2009–2021. It was conducted on the basis of data from the Eurostat database. This study used multivariate statistical analysis methods: k-means, COmplex PRoportional ASsessment (COPRAS) in a dynamic approach, Dynamic Time Warping (DTW) and hierarchical clustering. EU countries differ in terms of the studied aspects of the silver economy in a north–south direction. In the period under study, there were significant changes in the country rankings and these changes were not the same for the EU countries. Also, most EU countries indicated improvements in terms of analyzed aspects of the silver economy. An innovative approach is the use of the COPRAS method in a dynamic approach, thanks to which it is possible to develop not only country rankings, but also to analyze the dynamics of changes.

A switching based forecasting approach for forecasting sales data in supply chains

Forecasting future demand has been a challenging task for supply chain practitioners, which is further exacerbated due to the recent pandemic effects. While the literature suggests a potential for improved accuracy with ML/AI approaches compared to probabilistic distribution-based traditional forecasting methods, the extent of this enhancement may vary based on the specific case. It is recognized that traditional probabilistic forecasting approaches are often considered less accurate and may lead to errors, potentially influencing the estimation of overall business costs. Meanwhile, with the advancement of artificial intelligence (AI) approaches, such as machine learning (ML) and deep learning (DL), this misestimation of cost can be reduced by forecasting demand more accurately from historical data. Consequently, this paper applies several AI-based approaches to predict demand data. Since no fixed AI approach works best for all datasets, a switching-based forecasting approach (SBFA) is proposed to exploit the merit of different advanced ML/DL approaches for different days ahead of prediction. Based on the performance of validation data, the proposed system automatically switches between different approaches to determine a more appropriate forecasting approach. A two-echelon supply chain model with different attributes is developed to validate the proposed SBFA against a few traditional forecasting approaches. The reorder points of this supply chain model are calculated based on the predictions from conventional/ML/DL forecasting approaches. Predictions from SBFA and other approaches are analysed by calculating overall supply chain cost. Based on overall supply chain costs under static and dynamic lead time settings, the effectiveness and applicability of the proposed SBFA against traditional forecasting approaches are demonstrated.

Research on Fast Prediction Methods of Ice Shape in Ice-tolerant Airfoil Design

Abstract Ice accretion damages the aerodynamic performance of aircraft during flight, so the airfoil design based on the concept of ice-tolerant has become a brand-new research direction. In order to shorten the Computational Fluid Dynamics (CFD) time of ice shape in the design of ice-tolerant airfoil, this paper studies a fast prediction method of ice shape based on the Class function/Shape function Transformation (CST) method and Kriging model. Firstly, airfoils are parameterized by the CST method to establish the airfoil database. Secondly, the ice shape database is obtained by CFD calculations, and then the ice shapes mapping on airfoils are generated by the Kriging model. Finally, the sample number, CST order, POD energy ratio and other relative parameters are studied based on the NACA0012 airfoil. Results show that the CFD time can be extensively saved in ice-tolerant design for airfoils, and further, for three-dimensional optimization problems with variable icing conditions.

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Online Learning of Temporal Association Rule on Dynamic Multivariate Time Series Data

Regime switching in coupled nonlinear systems: Sources, prediction, and control—Minireview and perspective on the Focus Issue

Regime switching, the process where complex systems undergo transitions between qualitatively different dynamical states due to changes in their conditions, is a widespread phenomenon, from climate and ocean circulation, to ecosystems, power grids, and the brain. Capturing the mechanisms that give rise to isolated or sequential switching dynamics, as well as developing generic and robust methods for forecasting, detecting, and controlling them is essential for maintaining optimal performance and preventing dysfunctions or even collapses in complex systems. This Focus Issue provides new insights into regime switching, covering the recent advances in theoretical analysis harnessing the reduction approaches, as well as data-driven detection methods and non-feedback control strategies. Some of the key challenges addressed include the development of reduction techniques for coupled stochastic and adaptive systems, the influence of multiple timescale dynamics on chaotic structures and cyclic patterns in forced systems, and the role of chaotic saddles and heteroclinic cycles in pattern switching in coupled oscillators. The contributions further highlight deep learning applications for predicting power grid failures, the use of blinking networks to enhance synchronization, creating adaptive strategies to control epidemic spreading, and non-feedback control strategies to suppress epileptic seizures. These developments are intended to catalyze further dialog between the different branches of complexity.

Price discovery redux—Analyzing energy spot and futures prices using a dynamic programming approach

We employ dynamic time warping (DTW), a non-parametric pattern recognition technique based on a dynamic programming algorithm, to analyze whether futures markets for crude oil and natural gas have facilitated price discovery over the last decade. Should futures prices absorb and reflect information before spot prices, they will move first and lead spot prices, suggesting that they dominate spot prices and play an important role in price discovery. The results show that natural gas futures prices led spot prices more frequently between 2019 and 2023 than during the five years preceding this turbulent period. In the case of crude oil, however, futures prices lagged spot prices more often than leading them. The evidence suggests that futures prices have not consistently fulfilled their price discovery role in the two energy markets. The results also indicate that short-term futures contracts play a more dominant role in price discovery than long-term contracts. Additionally, we demonstrate the advantages of DTW: it lends itself well to analyzing small samples with different orders of integration; it can discover linear and nonlinear relationships between time series; notably, it can detect period-to-period changes in the duration and direction of lead-lag associations between two series and present the results intelligibly.

Estimation of Remaining Useful Life (RUL) for Pneumatic Actuator without apriori RUL History: A Hybrid Prognostic Approach

Predicting the Remaining Useful Life (RUL) of an industrial pneumatic actuator is crucial for enhancing maintenance strategies, reducing downtime and optimizing resource allocation. However, estimation becomes challenging when no historical RUL data is available for modeling. In this paper, a novel hybrid prognostic approach that combines Dynamic Time Warping (DTW), Exponential Degradation Model (EDM) and Random Forest Regressor (RFR) is proposed to estimate the RUL of pneumatic actuators under the absence of apriori RUL history. The DTW technique is employed to identify the onset of potential degradation. By aligning the healthy and faulty data, DTW provides a robust measure of distance and time at the point of deviation as the threshold value. Subsequently, the EDM is introduced to capture the degradation pattern in the actuator behavior. The EDM accounts for the relationship between threshold value, operating conditions, degradation rate and exponential coefficients through curve fitting methods. To further enhance prediction accuracy, RFR is employed to predict the RUL based on input features of aligned data from DTW and the derived degradation rates from EDM. In the simulation studies, the proposed methodology is applied to a synthetic dataset and benchmark DAMADICS dataset of the industrial pneumatic actuator in sugar processing unit to estimate RUL. The estimated RUL for each health indicator is quantified and the severity of each fault is discussed. The proposed method is implemented on a real time laboratory setup. The results are also validated on the benchmark NASA turbo-engine dataset by comparing the actual and estimated RULs, achieving 82.5% range-based accuracy.

A novel adaptive cost-sensitive convolution neural network based dynamic imbalanced fault diagnosis framework for manufacturing processes

Abstract Due to the influences of sensor faults, communication lines, and human factors, it is difficult to collect and label fault data in large quantities, resulting in the imbalance between normal and fault data, and between fault and fault data. Those kinds of data imbalances violate the assumption of relatively balanced distribution of most traditional fault diagnosis methods. Associated with those trends, some imbalanced fault diagnosis methods have been put forward. However, most of those methods only consider that the proportion of various samples remains unchanged, that is, the imbalance rate is stable. In the actual manufacturing processes, the industrial data flows are fast, continuous, and dynamically changing. The imbalance rates of all kinds of samples often change continuously, showing the dynamic imbalanced characteristic. To solve this problem, a novel adaptive cost-sensitive convolution neural network based dynamic imbalanced fault diagnosis framework is designed for manufacturing processes. More specifically, a new adaptive cost-sensitive convolutional neural network is firstly constructed by coordinating the cross entropy loss function with a specific cost sensitive index, of which the dynamic imbalance rates and the diagnosis performance indicators are comprehensively considered. Subsequently, a dynamic time factor is reasonably designed and introduced to make the diagnosis model pay more attention to identification of new fault data in the industrial data flow, aiming at improving the fault diagnosis performance. Finally, sufficient simulation experiments are conducted by a typical manufacturing process, the hot rolling process, to demonstrate the superiority of the proposed framework compared with some classical algorithms.

VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns

With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning (ML), obtaining power consumption data is becoming more and more important. Collecting real-world energy data using sensors is time consuming, expensive, error-prone and in some situations not possible. Therefore, we present the VA-Creator, a framework to create Virtual Appliances (VAs). These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. To be able to generate all appliance types available in a typical household we use various kinds of NN, including Multilayer Perceptrons (MLPs), Long Short-Term Memorys (LSTMs) and a specific Generative Adversarial Network (GAN) as well as different ML techniques such as XGBoost, selecting the appropriate technique depending on each appliance’s characteristics. We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping (DTW) as well as the classification performance of an MLP descriminator as metrics. Additionally, to ensure that the VAs allow to meaningfully train ML models, we use them to generate synthetic data and then train Non intrusive Load Monitoring (NILM) models in an extensive evaluation. The presented evaluation provides evidence that the VA models produce realistic and meaningful results.

A New Method for Analyzing Storm Event Discharge‐Concentration Hysteresis: The Upper Yangtze River as an Example

AbstractThe analysis of hydrological and sediment dynamics during flood events offers a new perspective on material transport within basins and the management of rivers. The literature has highlighted the hysteretic nature of material transport at the event scale, which arises from temporal differences in the transport of materials (flow and sediment). Current hysteresis research methods fall short in effectively assessing the complex events. This study leverages suspended‐sediment and discharge data from the upper Yangtze River to show proof‐of‐concept for hysteresis studies using Dynamic Time Warping (DTW) algorithm, a time series analysis tool. To evaluate the efficacy of the method, extensive assessments were undertaken using a combination of synthetic data and basin data with over 700 flood events. The proposed index effectively captures and quantifies hysteresis relationships associated with flood events, making it a versatile tool for hysteresis studies. We also similarly tested the robustness of the method and the application to complex events, and the results were satisfactory. Furthermore, we explored the hysteretic features of flood events in the studied basin. The results revealed that a proportion ranging from 50% to 60% flood events exhibit sediment peak leading pattern, indicating that in the upper Yangtze River, sediment peaks tend to occur in a leading pattern. The operation of reservoirs profoundly impacts the dynamics of sediment transport, resulting in an increased proportion (from 27% to 54%) of sediment peaks lagging behind discharge peaks. These study findings can be used to improve hysteresis study methods and contribute to the understanding of hydrological and sediment transport characteristics.

Optimal intensity measures and probabilistic fragility assessment for the long-span aqueduct structure with four-column bents

The intensity measure (IM) is a key factor in determining the accuracy of seismic performance assessment, which can represent the power of ground motion. However, the optimal IMs selection for long-span aqueduct structures with four-column bents has not been investigated in the previous study. This study aims to evaluate the optimal IMs for use in probabilistic seismic demand model (PSDM) and propose the scalar- and vector-valued fragility methods of the long-span aqueduct structure. To achieve this goal, taking the long-span aqueduct structure with four-column bents in the Central Yunnan Water Diversion Project in Southwest China as a typical case, a series of nonlinear dynamic time history analysis are conducted. Then, the 21 commonly-used IMs are tested and evaluated based on the different metrics (e.g. correlation, efficiency, practicality and proficiency). The optimal vector-valued IMs for the fragility analysis of the long-span aqueduct structure with four-column bents is proposed. Meanwhile, the PSDMs of optimal vector-valued IMs are also established and compared. Finally, the scalar- and vector-valued fragility functions are developed in terms of the optimal IMs. A method for seismic fragility analysis of the long-span aqueduct structure with four-column bents based on vector-valued IMs is proposed. The numerical results reveal that peak ground acceleration (PGA), peak pseudo acceleration spectrum (PSA), root-mean-square of acceleration (Arms) and characteristic intensity (Ic) are relatively appropriate IMs for seismic performance evaluation of the long-span aqueduct structure with four-column bents. In particular, PGA is considered to be the optimal IM due to its highest correlation, better efficiency, practicality and proficiency. The scalar-valued fragility curves can only describe the impact of the single IM on the damage probability of the aqueduct structure, which may overestimate or underestimate its damage probability. Furthermore, the vector-valued IMs can significantly increase the fitting ability of the PSDMs. The fragility surfaces are superior to scalar-valued fragility curve due to the vector-valued IMs can more accurately describe the ground motion information. In summary, the findings of this study highlight the significance of proposing the optimal IMs and developing vector-valued fragility surfaces when evaluating the seismic performance of the long-span aqueduct structure with four-column bents.

Hybrid Preprocessing for Neural Network-Based Stock Price Prediction

In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.

The distance and entropy measures-based intuitionistic fuzzy C-means and similarity matrix clustering algorithms and their applications

Determining the similarity between datasets by distance/similarity measures is important in most clustering algorithms. Therefore, this paper characterizes two clustering algorithms with improved distance measures. In the similarity matrix clustering algorithm, the mixed correlation coefficient and two probability parameters are suggested into the weighted probabilistic Euclidean distance (WPED) measure, which expresses the randomness and fuzziness of intuitionistic fuzzy sets. Second, based on the characteristics of dynamic time warping (DTW), a DTW-based probabilistic Euclidean distance (PED-DTW) method is introduced. The attribute ordering rule is developed with the help of entropy, which is beneficial for PED-DTW in achieving optimal path matching. Furthermore, a DTW-based intuitionistic fuzzy C-means algorithm is constructed. Finally, we performed experiments using car datasets and 7 UCI datasets. Compared with the existing method, the similarity matrix clustering algorithm is feasible. The DTW-based intuitionistic fuzzy C-means algorithms is compared with six clustering methods on four performance metrics, and the performance of our algorithm is analyzed. Our algorithm performs well on four performance metrics. Experimental results indicate that the clustering algorithms proposed in this paper are practical and outperform their existing counterparts. Also, the proposed algorithm gives good results in image segmentation.

A study on fatigue life evaluation of 42CrMo steel under cyclic loading based on metal magnetic memory method

Accidents involving drilling tool fractures in the process of oil and gas resource exploration and development occur frequently, and there are few detection methods for detecting the residual fatigue life of drilling tool joints with irregular surfaces. Therefore, developing a quick and effective method to evaluate fatigue damage in drilling tool joints is particularly important. In this study, a fatigue life evaluation method for ferromagnetic materials was established based on metal magnetic memory (MMM) detection technology, which can be used in the petroleum industry to evaluate the remaining life of drilling tool joints. First, the relationship between the impact load and specimen fatigue life was determined through a simulation, followed by a fatigue test, and the magnetic signals on the surface of the specimen under different fatigue loads were collected. The effects of the three evaluation methods were compared, and it was found that dynamic time warping (DTW) was more accurate for the evaluation of metal fatigue. Finally, a fatigue damage evaluation model for ferromagnetic metallic materials was established, and the feasibility of the method was verified using test data. This method can effectively predict the remaining fatigue life of threaded specimens, and can be applied in the petroleum industry to evaluate the remaining fatigue life of drilling tool joints. This study provides guidance for the petroleum industry in ensuring the safety of drilling tools.