Warping Algorithm Research Articles

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.

A spatial transfer-based hybrid model for wind speed forecasting

Accurate wind speed forecasting is essential for optimizing energy dispatch and enhancing grid stability. This study presents a novel hybrid wind speed forecasting model (WSDTW-CLA), emphasizing the spatial transfer characteristics of wind speed while mitigating the inherent errors in existing models. The proposed method employs the Wind Speed Dynamic Time Warping (WSDTW) algorithm to align wind speed data from neighboring stations, effectively facilitating the capture of spatial transfer patterns during the preprocessing phase. This alignment generates a wind speed spatial matrix that incorporates future-relevant information, providing precise input for forecasting module. The model employs a hybrid neural network combining a convolutional neural network (CNN), a long short-term memory (LSTM) network, and an autoencoder (AE) to predict wind speeds by establishing feature connections from the preprocessed data. The performance of the WSDTW-CLA model is evaluated using seasonal datasets from March, June, September, and December in Yunnan Province, China. A multi-step comparative analysis involving seven established models and seven sub-models within the proposed framework demonstrates that the WSDTW-CLA model significantly outperforms other similar models, with all evaluation metrics showing improvements of over 30 %. This proposed method enhances the utilization of wind energy resources, thereby promoting the advancement of the wind power industry.

A method for evaluating ecological quality levels and trends using natural evolution time series.

Ecological quality assessment serves as a valuable tool for impartially measuring the effects of development and utilization activities on ecosystems, forming the bedrock of ecological governance. The ecological quality evaluation method, relying on a reference frame, takes into consideration the comparability of ecological quality, thus guaranteeing the presence of external benchmarks for the assessment results. However, present research on absolute assessment of ecological quality is limited, as the majority of existing studies focus on large-scale regions, and overlooking regional differences in smaller-scale regions to a certain extent. Therefore, to achieve a comprehensive, convenient, objective, and accurate ecological quality evaluation, this study established a model for regional ecological quality assessment through remote sensing. This model was based on the analysis of natural evolutionary features and was applied to assess the ecological quality in the largest ecosystem within the arid area of the Ningxia Hui Autonomous Region (NHAR) - the grassland ecosystem. For this assessment, the Modified soil-adjusted vegetation index (MSAVI), the wetness components of tasseled cap transformations (WET), and net primary productivity (NPP) were selected as indicators of soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity, respectively. The evaluation units were established according to the geomorphic types, and the improved derivative dynamic time warping (DDTW) algorithm was used to compare the conformity between the time series of evaluation units and natural evolution time series, pixel by pixel. The ecological quality was classified into five levels (levels 1, 2, 3, 4, and 5) using the natural breaks (Jenks) classification method. The Mann-Kendall trend test and the Sen's slope are used to analyze the trend of the conformity measurement results of the ecological quality each year at a significance level of 95%. The main results are as follows: (1) The indicators selected in the study are mostly independent of each other within each evaluation unit, with a correlation (0.552) observed only between MSAVI and WET in plain areas. (2) Spatial heterogeneity exists in the natural evolution characteristics, with significant variations in the similarity between the reference frames of different evaluation units. There is no strict rule linking the similarity results to the elevation of the terrain. (3) From the perspective of the evolutionary process and intensity, the soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity in the study area primarily fell within level 2. The advantageous level of each evaluation unit was also primarily at level 2. The overall trend of these three capacities demonstrated stability and remained constant. In summary, our assessment indicated that the overall quality of grassland ecosystems in the arid areas of the NHAR was relatively stable and superior. This method we employed has broad applicability and can serve as a general framework for ecological quality assessments. It can be adapted for ecological restoration assessments in other regions by fine-tuning regional parameters, offering valuable insights for regional ecological management.

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.

Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.

A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts.

The application of improved DTW algorithm in sports posture recognition

Sports posture recognition plays a crucial role in modern sports science and training. Posture recognition and analysis plays a positive role in improving sports quality and ensuring sports safety. However, existing recognition technologies still have poor recognition and accuracy in large amounts of posture data. Therefore, to further improve the performance of the existing posture recognition techniques, this study assumes that postures during movement can be effectively represented through the time series of skeletal key points, and the local similarity of these postures can be captured through the Dynamic Time Warping (DTW) algorithm. Based on this assumption, the existing DTW algorithm is improved by introducing the K-Nearest Neighbor (KNN) algorithm and combining it with Principal Component Analysis (PCA) for feature dimensionality reduction. A novel algorithmic model for postures recognition is proposed. The experimental results showed that the improved algorithm performed well in postures recognition rate and accuracy. Especially, when the k value was 5, the recognition rate reached up to 89%, and the accuracy reached 87%. Compared with the existing algorithm, the improved KNN-DTW algorithm has significant improvement in accuracy and computational efficiency. In summary, the new algorithm shows significant advantages in terms of accuracy and stability, providing a powerful tool for the analysis of athletic postures in the field of sports. Meanwhile, this research result has important application prospects in fields such as sports training, sports medicine, and virtual reality.

Derivative dynamic time warping algorithm with introduced correction for varying load fault diagnosis of nuclear power system steam turbine units

The Derivative Dynamic Time Warping (DDTW) algorithm is improved to address the multi-parameters time series classification problem faced by nuclear power system steam turbine units varying load fault diagnosis. Firstly, the entire load changing process is treated as a single sample rather than multiple time-step-samples. This ensures the complete information on the load changing processes, while avoiding interference from normal data fluctuations during faults. Secondly, Time Series Position Coefficient and Time Series Length Coefficient are proposed to correct the DDTW algorithm from two perspectives: the sequences lengths and the data positions in the sequences. This solves the singularities and timeline scaling problems, thereby preventing interference introduced by data sequences' lengths differences and ''similar data appearing at different times'' problem. The nuclear power system steam turbine unit simulation model was built to obtain load changing processes data under normal and faults statuses. In the varying load fault diagnosis test based on these data, the improved DDTW algorithm achieved an accuracy of 1.38%–12.06% higher than other methods, reaching 87.50%. Finally, The Deep Convolutional Generative Adversarial Networks (DCGAN) model was used to generate data to supplement the limited samples of complete load changing processes, and the accuracy of the novel method increased to 95.51% with the increase of data used to support the comparison.

The triglyceride-glucose index dynamic trajectory reveals the association between the clinical subphenotype of insulin resistance and mortality in patients with sepsis

BackgroundThe relationship between the dynamic changes in insulin resistance (IR) and the prognosis of septic patients remains unclear. This study aims to investigate the correlation between the clinical subphenotype of IR represented by the triglyceride-glucose (TyG) index trajectory and the mortality rate among patients with sepsis.MethodsIn this retrospective cohort study, we utilized data from septic patients within the Medical Information Mart for Intensive Care (MIMIC)-IV database version 2.0 to construct trajectories of the TyG index over 72 h. Subsequently, we computed the similarity among various TyG index trajectories with the dynamic time warping (DTW) algorithm and utilized the hierarchical clustering (HC) algorithm to demarcate distinct cluster and identified subphenotypes according to the trajectory trend. Subsequently, we assessed the mortality risk between different subphenotypes using analyses such as survival analysis and validated the robustness of the results through propensity score matching (PSM) and various models.ResultsA total of 2350 patients were included in the study. Two trajectory trends: TyG index decreasing (n = 926) and TyG index increasing (n = 1424) were identified, which indicated corresponding to the clinical subphenotype of increased and alleviative IR respectively. The 28-day and in-hospital mortality for the increased IR group was 28.51% and 25.49% respectively. In comparison, patients in the alleviative IR group with a 28-day mortality of 23.54% and an in-hospital mortality of 21.60%. These subphenotypes exhibited distinct prognosis, time dependent Cox model showed the increased IR group with a higher 28-day mortality [hazard ratio (HR): 1.07, 95% confidence interval (CI): 1.02–1.12, P = 0.01] and in-hospital mortality [HR: 1.05, 95% CI: 1.00–1.11, P = 0.045] compared to the alleviative IR group. Sensitivity analyses with various models further validated the robustness of our findings.ConclusionDynamic increase in the TyG index trajectory is associated with elevated mortality risk among patients with sepsis, which suggests that dynamic increased IR exacerbates the risk of poor outcomes in patients.

Reservoir oriented migrated seismic image improvement and poststack seismic inversion using well-seismic mistie

In seismic exploration of subsurface hydrocarbon reservoirs, migrated seismic image is the foundation for structural interpretation and subsequent seismic inversion for reservoir properties. Seismic events in migrated images can be mispositioned due to several factors including seismic velocity inaccuracy. The mispositioned events lead to poor well-seismic tie and degrades the accuracy of subsequent inversion and reservoir prediction. Particularly, the mistie can be quite obvious for the mid- and mature- phases of the reservoir development after many wells drilled. We propose to utilize the information from mistie between seismic images and synthetic traces to improve the accuracy of migrated seismic images and thus inversion results. Our method includes three major steps: firstly, calculate time shifts between the synthetic traces and the co-located seismic trace using the path-limited dynamic time warping (PDTW) algorithm; then compute the time shift volume for the entire migrated image using the seismic-driven modeling approach based on the time shift traces at the well locations; apply the time shift volume to update the migrated image. The improved seismic volume can be used for further processing such as seismic inversion, which makes the inversion results more consistent with seismic and well data. We apply our method to synthetic and field datasets. The results demonstrate that the updated seismic image has an improved seismic well tie and an improved inversion result. Our method indicates a venue for a better honor of seismic and well data in reservoir characterization.

ECG arrhythmia classification based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability

Electrocardiograph (ECG) is one of the most critical physiological signals used for arrhythmia diagnosis. In recent years, ECG arrhythmia classification devices consisting of multi-module sensors, clustering algorithms and neural networks play an important role in monitoring and diagnosing cardiovascular diseases. However, the commonly used ECG arrhythmia classification methods are still facing some problems such as the complex model structure and long running time. To address the above problems, this paper proposes an ECG arrhythmia classification method based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability (SFP-FACC), which uses LSTM to fit the cluster centers and avoids the time consumption of updating the cluster centers during the classification process. The spatiotemporal feature perception ability of this model with the dynamic time warping (DTW) algorithm is improved. The classification is achieved by applying the combination of Euclidean distance and DTW. The convergence speed of the model is improved by using dynamic pheromone volatility coefficient; and finally the optimal solution of the model is determined by using radix sort. Based on the MIT-BIH arrhythmia dataset, the overall accuracy of the proposed classification method in this paper achieves 99.04 %, and even the accuracy of certain types of classification achieves 100 %, and the running time is about 3.5 times faster than that of the basic models. The experiments show that the method proposed in this paper has certain advantages.

PredATW: Predicting the Asynchronous Time Warp Latency For VR Systems

With the advent of low-power ultra-fast hardware and GPUs, virtual reality (VR) has gained a lot of prominence in the past few years and is being used in various areas, such as education, entertainment, scientific visualization, and computer-aided design. VR-based applications are highly interactive, and one of the most important performance metrics for these applications is the motion-to-photon-delay (MPD). MPD is the delay from the user’s head movement to the time at which the image gets updated on the VR screen. Since the human visual system can even detect an error of a few pixels (very spatially sensitive), the MPD should be as small as possible. Popular VR vendors use the GPU-accelerated Asynchronous Time Warp (ATW) algorithm to reduce the MPD. ATW reduces the MPD if and only if the warping operation finishes just before the display refreshes. However, due to the competition between the different constituent applications for the single, shared GPU, the GPU-accelerated ATW algorithm suffers from an unpredictable ATW latency, making it challenging to find the ideal time instance for starting the time warp and ensuring that it completes with the least amount of lag relative to the screen refresh. Hence, the state-of-the-art is to use a separate hardware unit for the time-warping operation. Our approach, PredATW , uses an ML-based hardware predictor to predict the ATW latency for a VR application, and then schedule it as late as possible while running the time-warping operation on the GPU itself. As far as we know, this is the first work to do so. Our predictor achieves an error of only 0.22 ms across several popular VR applications for predicting the ATW latency. As compared to the baseline architecture, we reduce deadline misses by 80.6%.

An Extremely Sparse Tomography Reconstruction of a Multispectral Temperature Field without Any a Priori Knowledge.

When undertaking optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. In order to reconstruct the radiation field of unknown materials or in situations where a priori knowledge cannot be obtained, this paper proposes an extremely sparse tomography multispectral temperature field reconstruction algorithm that analyzes the similarity (the similarity here compares and calculates the Euclidean distance of the spectral emissivity values at various wavelengths between different spectral curves) of radiation characteristics of materials under the same pressure and concentration but different temperature, describes the similarity between the radiation information of the tested field using the dynamic time warping (DTW) algorithm, and uses the similarity sum of the radiation information among the subregions of the temperature field as the optimization objective. This is combined with the equation-constrained optimization algorithm and multispectral thermometry to establish the statistical law between the missing information and finally realize the reconstruction of the temperature field. Simulation experiments show that, without any a priori knowledge, the method in this paper can realize reconstruction of the temperature field with an accuracy of 1.53-12.05% under two projection angles and has fewer projection angles and stronger robustness than other methods.

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.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

A demonstration trajectory segmentation approach for wheelchair‐mounted assistive robots

AbstractSegmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment. With the aim to conveniently and accurately segment demonstration trajectories, a novel demonstration trajectory segmentation approach is proposed based on the beta process autoregressive hidden Markov model (BP‐AR‐HMM) algorithm and generalised time warping (GTW) algorithm aiming to enhance the segmentation accuracy utilising acquired demonstration data. This approach first adopts the GTW algorithm to align the multiple demonstration trajectories for the same task. Then, it adopts the BP‐AR‐HMM algorithm to segment the demonstration trajectories, acquire the contained motion primitives, and establish the related task library. This segmentation approach is validated on the 6‐degree‐of‐freedom JACO robotic arm by assisting users to accomplish a holding water glass task and an eating task. The experimental results show that the motion primitives within the trajectories can be correctly segmented with a high segmentation accuracy.

Shape Pattern Recognition of Building Footprints Using t-SNE Dimensionality Reduction Visualization

The shape pattern recognition of building footprints stands as a pivotal concern within GIS spatial cognition. In this study, we introduce a novel approach for the shape recognition of building footprints, leveraging t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction visualization. First, the Canonical Time Warping (CTW) algorithm is employed to gauge the shape similarity distance of building footprints. Subsequently, the t-SNE model is utilized to map the building footprints, featuring varying numbers of coordinate vertices, onto points within the Cartesian coordinate system. The shape similarity distance serves as the input to the t-SNE model for parameter optimization. Lastly, building footprint shapes are identified through the inherent clustering patterns of points using a Gaussian Mixture Model (GMM). Experimental results demonstrate the method’s robustness to the translation, rotation, scaling, and mirroring of geometric objects, while effectively measuring shape similarity between building footprints. Furthermore, diverse types of building footprints are discernible through natural clustering in low-dimensional spaces, aligning closely with human visual perception.

Time series clustering based on polynomial fitting and multi-order trend features

Time series clustering serves as a potent data mining method, facilitating the analysis of an extensive array of time series data without the prerequisite of any prior knowledge. It finds wide-ranging use across various sectors, including but not limited to, financial and medical data analysis, and sensor data processing. Given the high dimensionality, non-linearity, and redundancy characteristics associated with time series, conventional clustering algorithms frequently fall short in yielding satisfactory results when directly applied to this kind of data. As such, there is a critical need to judiciously select suitable feature extraction methods and dimension reduction techniques. This paper introduces a time series clustering algorithm, drawing primarily from polynomial fitting derivative features as a wellspring for feature extraction to achieve effective clustering results. Initially, Hodrick Prescott (HP) filtering comes into play for the processing of raw time series data, thereby eliminating noise and redundancy. Subsequently, polynomial curve fitting (PCF) is applied to the data to derive a globally continuous function fitting this time series. Next, by securing multi-order derivative values via this function, the time series is transformed into a multi-order derivative feature sequence. Lastly, we designed a polynomial function derivative features-based dynamic time warping (PFD_DTW) algorithm for determining the distance between two equal or unequal granular length time series, and subsequently a hierarchical clustering method anchored on the PFD_DTW distances for time series clustering after computing interspecies distances. The effectiveness of this method is corroborated by experimental results obtained from several practical datasets.

Remaining Useful Life Prediction Method Enhanced by Data Augmentation and Similarity Fusion

Precise prediction of the remaining useful life (RUL) of rolling bearings is crucial for ensuring the smooth functioning of machinery and minimizing maintenance costs. The time-domain features can reflect the degenerative state of the bearings and reduce the impact of random noise present in the original signal, which is often used for life prediction. However, obtaining ideal training data for RUL prediction is challenging. Thus, this paper presents a bearing RUL prediction method based on unsupervised learning sample augmentation, establishes a VAE-GAN model, and expands the time-domain features that are calculated based on the original vibration signals. By combining the advantages of VAE and GAN in data generation, the generated data can better represent the degradation state of the bearings. The original data and generated data are mixed to realize data augmentation. At the same time, the dynamic time warping (DTW) algorithm is introduced to measure the similarity of the dataset, establishing the mapping relationship between the training set and target sequence, thereby enhancing the prediction accuracy of supervised learning. Experiments employing the XJTU-SY rolling element bearing accelerated life test dataset, IMS dataset, and pantograph data indicate that the proposed method yields high accuracy in bearing RUL prediction.