Time Warping Research Articles

Air Handwriting by Using CNN Model

Gesture recognition has been a popular research field under the trend of IoT and intelligent devices. Air-writing is the most challenging and crucial topic in the gesture recognition field. In this paper, we propose a wearable airwriting system that makes users can write the English alphabet in the three-dimensional space without any write rules. The proposed system is based on the Inertial Measurement Unit (IMU), and it uses dynamic time warping (DTW) as the main recognition algorithm. In addition, to improve the recognition accuracy and take a better advantage of the DTW algorithm, we present an adjustment system that gives some new optimization methods to the application of IMU and DTW. In the experiment, the accuracy of recognition is 84.6% for the uppercase alphabet (from „A‟ to „Z‟) in user-dependent case. And we also confirmed that the recognition method only based on the DTW algorithm is one kind of user-dependent methods, which means this method is heavily dependent on personalization.

Classification-Based Parameter Optimization Approach of the Turning Process

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

Velocity-independent NMO correction based on multi-scale dynamic time warping

Normal moveout (NMO) correction is one of the common steps of seismic data processing. Conventional NMO correction is generally based on a velocity function, but velocity analysis is considered time-consuming and labor-intensive. Moreover, the field seismic data observed in complex areas can have low signal-to-noise ratio (SNR), resulting in inaccurate picked up velocity parameters and poor NMO correction. The conventional NMO correction has stretching issues, most significantly at shallow and large offset areas. To address these problems, a new multi-scale dynamic time warping (MSD) algorithm for velocity-independent stretch-free NMO correction is investigated. This algorithm has higher accuracy and stronger noise resistance, as our comparisons with other applications of DTW show. In our algorithm, the original seismic data is decomposed into multiple morphological scales (using mathematical morphology theory), and alignment errors are calculated separately at each scale, followed by further adaptive weighted summation. Accumulation and backtracking operations based on alignment errors produce time shift sequences for automatic flattening of the gathers. The effectiveness of the presented approach is tested on both synthetic and field data. The results demonstrate that compared with traditional velocity-based NMO correction methods, the proposed method can accurately flatten seismic gathers without the NMO stretch and is robust to strong noise.

Bearing life prediction method based on novel health indicators and improved similarity curve matching

Abstract To tackle the issues of low accuracy in similarity curve matching and the challenges in measuring the similarity of degraded curve segments in rolling bearing life prediction methods, we propose a novel bearing remaining useful life (RUL) prediction method utilizing new health indicators and improved similarity curve matching techniques. Initially, time and frequency domain features are extracted from the bearing vibration signals. Subsequently, the multi-dimensional features are meticulously screened using monotonicity, Spearman's correlation, and uncertainty metrics to construct the health indicator (HI) and the health indicator library for the bearing. Subsequently, the support vector data description (SVDD) combined with the PID search algorithm is employed to ascertain the initial prediction time of the bearing and to construct the corresponding health indicator library. Following this, both global and local feature capture are conducted using dual dynamic time warping (DDTW) in conjunction with a sliding window to predict the remaining service life of rolling bearings.
The proposed method is validated on the PHM2012 and XJTU-SY datasets and benchmarked against the latest research. The results demonstrate that our method significantly enhances the prediction accuracy of the remaining useful life of bearings, underscoring the effectiveness and superiority of the proposed approach.and superiority of the proposed approach.

Research on Joint Forecasting Technology of Cold, Heat, and Electricity Loads Based on Multi-Task Learning

The cooperative optimization and dispatch operation of the integrated energy system (IES) depends on accurate load forecasts. A multivariate load, joint prediction model, based on the combination of multi-task learning (MTL) and dynamic time warping (DTW), is proposed to address the issue of the prediction model’s limited accuracy caused by the fragmentation of the multivariate load coupling relationship and the absence of future time series information. Firstly, the MTL model, based on the bidirectional long short-term memory (BiLSTM) neural network, extracts the coupling information among the multivariate loads and performs the preliminary prediction; secondly, the DTW algorithm clusters and splices the load data that are similar to the target value as the input features of the model; finally, the BiLSTM-attention model is used for secondary prediction, and the improved Bayesian optimization algorithm is applied for adaptive selection of optimal hyperparameters. Based on the game-theoretic view of Shapley’s additive interpretation (SHAP), a model interpretation technique is introduced to determine the validity of the liquidity indicator and the asynchronous relationship between the significance of the indicator and its actual contribution. The prediction results show that the joint prediction model proposed in this paper has higher training speed and prediction accuracy than the traditional single-load prediction model.

Dynamic signatures: a mathematical approach to analysis

Abstract This study evaluates mathematical tools (principal component analysis, dynamic time warping, and the Kolmogorov–Smirnov hypothesis test) to analyse global and local data from dynamic signatures to reduce subjectivity and increase the reproducibility of handwriting examination using a two-step approach. A data set composed of 1800 genuine signature samples, 870 simulated signatures, and 60 disguises (30 formally similar or ‘autosimulated’ and 30 random but different from usual) provided by 30 volunteers was collected. The first step involved global data analysis using principal component analysis and a hypothesis test performed for 62 global characteristics, and associations of these characteristics were analysed through calculations of multivariate distance followed by a hypothesis test. The second step involved the analysis of local characteristics including vertical and horizontal positions, speed, pressure gradient, acceleration, and jerk point-to-point, by using dynamic time warping followed by a hypothesis test. Optimization of sensitivity and specificity metrics of the hypothesis test was explored by varying its stringency and observing accuracy rates for the simulated and genuine groups. A P-value threshold of 1 × 10−10 was found to be optimal, making the test more restrictive and yielding accuracy rates of 96.7% for genuine global data and 88.9% for simulated data. The same cutoff value for local characteristics provided an average accuracy rate of 95.4% for genuine samples and 94.7% for simulated samples, demonstrating high accuracy for both simulated and genuine samples. However, the method did not offer reasonable accuracy rates for disguises, consistent with observations in traditional handwriting examination. Our approach provided satisfactory results for forensic examination use. The visualization of graphs and signatures and analysis of all identifying elements of handwriting by the examining expert are still essential. In future studies, we plan to perform blind tests to validate our approach and propose a rigorous methodology.

IMITASD: Imitation Assessment Model for Children with Autism Based on Human Pose Estimation

Autism is a challenging brain disorder affecting children at global and national scales. Applied behavior analysis is commonly conducted as an efficient medical therapy for children. This paper focused on one paradigm of applied behavior analysis, imitation, where children mimic certain lessons to enhance children’s social behavior and play skills. This paper introduces IMITASD, a practical monitoring assessment model designed to evaluate autistic children’s behaviors efficiently. The proposed model provides an efficient solution for clinics and homes equipped with mid-specification computers attached to webcams. IMITASD automates the scoring of autistic children’s videos while they imitate a series of lessons. The model integrates two core modules: attention estimation and imitation assessment. The attention module monitors the child’s position by tracking the child’s face and determining the head pose. The imitation module extracts a set of crucial key points from both the child’s head and arms to measure the similarity with a reference imitation lesson using dynamic time warping. The model was validated using a refined dataset of 268 videos collected from 11 Egyptian autistic children during conducting six imitation lessons. The analysis demonstrated that IMITASD provides fast scoring, takes less than three seconds, and shows a robust measure as it has a high correlation with scores given by medical therapists, about 0.9, highlighting its effectiveness for children’s training applications.

Writing Life, Writing Time, Writing the Mother

This article focuses on Ernaux’s later life memories of a twentieth century daughter-mother relationship, rendered in the light of this era’s historical transformations and its tensions between individual freedom and collective "fate," and between pre- and post 1968 conceptions of a fulfilled life. It refers to Ernaux’s most monumental work of self-writing, Les Années (2008) and this book’s links to what she has previously written in remembrance of her mother. The purpose is to investigate how these works seek to come to terms with, not only the loss of a mother, but also the narrator’s own aging. Ernaux exemplifies the adult daughter-senescent mother relationship in ways that are characteristic for her generation of women and suggestive of general historical changes in human relationships in Western societies since 1968. Can Ernaux’s writing provide, in the terms of Kathleen Woodward, “cultural models of older women as a way of generating alternative futures for ourselves”? Or does it fail to reconciliate the stereotypical binary of female aging as either graceful or awful, conveying tabooed aspects of old age that need to be brought to light, and even more urgently so in a socio-cultural context privileging youthfulness, individualism and uncertain human bonds? Keywords: Annie Ernaux, The Years; aging daughter-mother relationship; filiation narratives; auto-socio-biography; memory and time; autonomy and dependency; social exclusion; abjection and purification.

Seeing the Futures: Evaluating the Application of Structured Analytic Technique Alternative Futures Analysis

The Structured Analytic Technique (SAT) Alternative Futures Analysis (AFA) is an imaginative thinking technique used by intelligence analysts in circumstances of uncertainty to generate and explore a range of potential future states. Recent literature on SATs has been critical of their use because of an absence of robust evidence of their value. This research observed focus groups which applied AFA to a specific case study and evaluated their outputs. The results suggest AFA can be used less prescriptively than some of the literature sets out in terms of group size and timeframe, while also supporting the recommended use of a facilitator. An understanding of the purpose of AFA as an imaginative thinking technique, intended to explore future possibilities, is vital in applying the technique, evaluating it, and communicating the results. The potential for mismatch between the criteria suggested to analyse SATs in general and the intent of AFA as a technique, undervalues the technique. A key consideration in evaluating AFA, is the need to ensure the technique is applied as intended. Further, this research identifies that the learning context may influence the application of AFA.

Revised cross-correlation and time-lag between cosmic ray intensity and solar activity using Chatterjee’s correlation coefficient

This study revisits the cross-correlation between cosmic ray intensity (CRI) and solar activity (SA) by comparing traditional Pearson correlation with Chatterjee’s correlation coefficient. Traditional analyses using Pearson correlation are useful for identifying linear relationships and time lags. However, they may not fully capture more complex interactions in the data. Chatterjee’s correlation coefficient, while sensitive to different types of relationships, including nonlinear ones, provides a complementary perspective on the temporal relationships between CRI and SA. This approach broadens our understanding of potential dependencies, offering additional insights that may not be captured through Pearson correlation alone.The findings reveal that Chatterjee’s correlation complements Pearson’s insights by providing an alternative view of the relationship between cosmic ray intensity (CRI) and solar activity (SA). The results show that Chatterjee’s correlation coefficients are, on average, approximately 45–50% smaller than Pearson’s, which could reflect different sensitivities to the underlying data structure rather than solely indicating a nonlinear component. Additionally, the time lags identified using Chatterjee’s correlation are generally shorter and more consistent across different solar cycles compared to those obtained with Pearson’s correlation, suggesting that CCC may capture temporal patterns in a distinct manner.Further analysis using Dynamic Time Warping (DTW) and Mean Absolute Percentage Error (MAPE) metrics demonstrated that, in more than half of the scenarios considered, alignment based on Chatterjee’s time lags resulted in lower errors and better alignment of the series compared to Pearson’s lags. This indicates that Chatterjee’s method is particularly effective for capturing the immediate and nuanced responses of CRI to SA changes, especially in recent solar cycles.This comprehensive approach provides broader insights into the dynamic interactions between cosmic ray intensity (CRI) and solar activity (SA), highlighting the importance of considering multiple correlation measures, including both linear and nonlinear approaches, in space weather research. The results suggest that Chatterjee’s correlation offers a complementary perspective on these interactions, providing additional details about how SA influences CRI over time, which may not be fully captured by Pearson’s correlation alone.

Seasonality of common respiratory viruses: Analysis of nationwide time-series data.

Understanding the seasonal behaviours of respiratory viruses is crucial for preventing infections. We evaluated the seasonality of respiratory viruses using time-series analyses. This study analysed prospectively collected nationwide surveillance data on eight respiratory viruses, gathered from the Korean Influenza and Respiratory Surveillance System. The data were collected on a weekly basis by 52 nationwide primary healthcare institutions between 2015 and 2019. We performed Spearman correlation analyses, similarity analyses via dynamic time warping (DTW) and seasonality analyses using seasonal autoregressive integrated moving average (SARIMA). The prevalence of rhinovirus (RV, 23.6%-31.4%), adenovirus (AdV, 9.2%-16.6%), human coronavirus (HCoV, 3.0%-6.6%), respiratory syncytial virus (RSV, 11.7%-20.1%), influenza virus (IFV, 11.7%-21.5%), parainfluenza virus (PIV, 9.2%-12.6%), human metapneumovirus (HMPV, 5.6%-6.9%) and human bocavirus (HBoV, 5.0%-6.4%) were derived. Most of them exhibited a high positive correlation in Spearman analyses. In DTW analyses, all virus data from 2015 to 2019, except AdV, exhibited good alignments. In SARIMA, AdV and RV did not show seasonality. Other viruses showed 12-month seasonality. We describe the viruses as winter viruses (HCoV, RSV and IFV), spring/summer viruses (PIV, HBoV), a spring virus (HMPV) and all-year viruses with peak incidences during school periods (RV and AdV). This is the first study to comprehensively analyse the seasonal behaviours of the eight most common respiratory viruses using nationwide, prospectively collected, sentinel surveillance data.

Dynamic radar cross section similarity study based on dynamic time warping

Abstract Dynamic radar cross-section (RCS) represents the radar reflectance cross-section of an aircraft at different moments during flight and is a crucial criterion for evaluating the ability of a radar to detect an aircraft. In practice, it is challenging to construct an accurate 6-DOF dynamics model for the purpose of solving its dynamic RCS. Consequently, a 3-DOF dynamics model is employed for the analysis of dynamic RCS. This paper presents a methodology for the joint simulation of the dynamic RCS under two DOF models and subsequent comparison and analysis of the resulting sequence data via the dynamic time warping algorithm. It is also concluded that the dynamic RCS values calculated using the 3-DOF dynamics model can identify the target vehicle.

Break a peg! A study of stablecoin co-instability

Following the increasing adoption of stablecoins, their robustness has become an ongoing concern for investors and regulators alike. This article studies the co-instability of a large panel of stablecoins by identifying their structural breaks and co-occurrences. Moreover, we use Dynamic Time Warping (DTW) and Dynamic Conditional Correlation DCC-GARCH models to assess spillover effects in the wake of four major black swan events in the crypto world. Similar to prior literature, we conclude that algorithmic stablecoins were the major shock receivers of the IRON TITAN and TERRA LUNA crashes, highlighting the importance of allowing for sufficient margins of safety in their design. We also observe that the FTX bankruptcy and the Silicon Valley Bank collapse had repercussions of a different nature and affected more categories of stablecoins, from algorithmic to fiat money-backed. We also highlight how, irrespective of the idiosyncratic or systemic nature of the shocks, well-known custodial stablecoins, as well as the most successful crypto-collateralized tokens, showed resilience during or in the immediate aftermath of these events.

Critical learning development and the crisis of the university: a collective consideration

This participatory session concerned consideration of the ‘critical turn in LD’ (Syska and Buckley, 2023: 107), manifest through an emerging critical LD movement – as centring an academic literacies approach (Hilsdon, 2018; Asher, 2022, 2023a; Dhillon, 2023; Rooney, 2023) – as a response to ‘the crisis of the university’ (Bacevic, 2017). The focus is on the positionality of LD/LDers within the increasingly neoliberal university, and to this critical learning development movement. Time and space are provided for sharing experiences, worries and hopes for LD/HE and to collectively explore the contemporary realities/trajectory of LD/HE – and alternative futures. The central cause of the crisis of UK HE, exacerbated by government and senior management responses to COVID-19, is intensifying and accelerating processes of the ‘deep neoliberalisation’ of the public university – reflected in industrial action concerning ‘pay devaluation; pay inequality, based on gender and race; excessive workloads; and casualisation’ (Hall, 2020a). The session explores where LD/LDers are positioned within the HE crisis, before considering how critically oriented LD/LDers who believe HE should be a social and public good can best respond to the crisis, while connecting such to wider contemporary societal and global crises. Specifically: How might LD/LDers respond to different contexts of crisis in ways that encompass LD’s developmental/pedagogical ethos, emancipatory values and empowering academic literacies approach (Syska and Buckley, 2023; Webster, 2019)? How do we link with wider critical educational theories, practices, communities and struggles? What might be the role of critical Learning Development and academic literacies? The objective is critical reflection on contemporary LD/HE contexts – with take-aways, as thus grounded in actual lived experiences of the crisis, including: Developing understandings as to self, team and LD contemporary positionality. Considerations of possible actions and next steps, including the potential for an ALDinHE CoP focused on critical LD and academic literacies.

Imagining alternative futures with augmentative and alternative communication: a manifesto.

This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are mainly driven by technological developments-technologies usually being developed for different markets-and are often based on ableist assumptions. In online conversations and a discussion panel at the 2023 International Society for Augmentative and Alternative Communication conference, we explored alternative futures by adopting different starting positions. Our case is presented under five headings: questioning the dominance of predictions that artificial intelligence and brain-computer interfaces will define the future of AAC; resisting disability being framed medically, as a problem to be solved, yet acknowledging both the pleasures and pains of being disabled; declaring that people who use AAC-as cyborgs of necessity rather than choice-should have choice and ownership of our technologies; challenging notions of independence as the necessary end goal for disabled bodies and considering interdependence as a human right; imagining alternative futures in which all people who use AAC are accepted and embraced for our communication and self-expression. This manifesto is an invitation for further discussion, and we welcome responses. While our focus is AAC, and three of the authors use AAC, we believe that our stance could be relevant to other disability communities in turn. This paper is about who gets to imagine disability futures and whose voices are left out. It is about how uncritical these futures can be, often presuming values that disabled people, in all their diversity, may not share.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters.

External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters. In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method. Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions. This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

Transfer learning and source domain restructuring-based BiLSTM approach for building energy consumption prediction

ABSTRACT Currently, building energy consumption prediction typically relies on vast amounts of historical data. However, for newly constructed buildings, the scarcity of data leads to reduced prediction accuracy. To address this challenge, this paper proposes a novel approach that integrates transfer learning with a source domain reconstruction-based BiLSTM model for building energy consumption prediction. In the first stage, both source and target domains are clustered into profile types using k-means. For each profile type in the target domain, the most similar profiles in the source domain are identified using Maximum Mean Discrepancy and Dynamic Time Warping. The source domain is then reconstructed by combining these identified profiles based on their proportions in the target domain. Subsequently, a feature extraction method based on EMD-CWT-Conv is introduced. Empirical Mode Decomposition is first applied to decompose and filter the source domain data. Continuous Wavelet Transform is then employed to extract distinctive frequency-domain and time-domain features from both the reconstructed source domain and the target domain. Final predictions are made using transfer learning and fine-tuning. Experiments on a grocery shop and a school show that the proposed approach reduces Mean Absolute Percentage Error by at least 13.19% and 17.67%, respectively.

Comparison of SEMG-Based Hand Gesture Classifiers

Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption of non-invasive technologies, such as surface electromyography (sEMG), becomes paramount. This study systematically assesses the isolated influence of classification algorithms within hand gesture recognition (HGR) systems using sEMG data and dynamic time warping (DTW) based features. This approach effectively handles temporal variations in sEMG signals by leveraging DTW, ensuring input features are invariant to gesture speed. Six supervised learning classifiers were evaluated: the multilayer perceptron, support vector machine, logistic regression, linear discriminant analysis, k-nearest neighbors, and decision tree. Cross-validation was employed to fine-tune the segmentation hyperparameters, significantly improving results. To ensure reproducibility, the source code has been made available, the proposed system design has been detailed, and the evaluation protocols have been described. Our findings indicate that logistic regression outperformed other classifiers in this setup, achieving 95.2% accuracy in classifying six hand movements from ten healthy individuals, representing a 1.6% improvement over the best previously reported performance using the same publicly available dataset. Future research will assess the proposed HGR system’s generalization capability on larger datasets suitable for training more complex classifiers, including deep learning models.

Imagining alternative climate futures in higher education

AbstractAcross the globe, interdisciplinary and creative approaches to climate change education are crucial at all levels, particularly in higher education. In this article, I draw from insights working with a class at New Mexico State University in the United States. The aim was to examine approaches to understanding, communicating, and representing climate change. Each student was asked to compose a narrative in which they imagined the year 2100 as a time when we have adequately mitigated and/or adapted to the climate crisis. The assignment set the tone for collective action and foregrounded the importance of story and imagination in building just and sustainable futures. The class complemented a public climate change speaker series and, as a second assignment, students suggested which speakers to invite to shape the series in the future. The two assignments opened new spaces to empower, learn with and from, and build connections between university students and academic staff to shape climate discourse and action in communities. Reflecting on what was learned, sharing an example of a climate futures assignment, and presenting views on a collaborative approach to climate change education all add, I hope, to the literature on imagining futures, empowerment, and authentic learning in climate change education.

Non-elastic time series fuzzy clustering for efficient analysis of industrial data sets

In line with the green transition and digitalization trends, there is an increasing need for effective performance and health monitoring of production machines. Machine operation footprints resonate in quantities such as supply current and pneumatic line pressure, providing valuable insights when analyzed. Examining these low-level signals allows for monitoring and identification of changes in machine operation over time. A critical step in analyzing machine data is clustering, requiring an effective method for calculating time series cluster centroids. This paper introduces Error in Aligned series (ERAL), a novel method that generates a time series centroid that distills the fundamental shape of the underlying datasets. ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods. Our analysis shows that existing methods can create artificial spikes and plateaus, which ERAL mitigates; it remains faithful to the original data shape. Additionally, ERAL offers improvements in computational efficiency and prototype quality. We evaluate the method against Dynamic Time Warping (DTW)-based methods across various datasets, and apply it to a time series clustering task using a real-world industrial dataset. In combination with a fuzzy clustering algorithm, ERAL generates visually convincing clusters. By leveraging fuzzy membership concepts, it achieves robust and adaptable clustering outcomes that reflect real-world data complexity and ambiguity.