Dynamic Time Warping Algorithm Research Articles

Study on Rock Mechanics Parameter Prediction Method Based on DTW Similarity and Machine-Learning Algorithms

Rock mechanics parameters are crucial factors for predicting rock behavior in oil and gas reservoirs, optimizing extraction strategies, and ensuring drilling safety. In this study, we propose a random forest (RF)-convolutional neural network (CNN)-long-term short-term memory network (LSTM) fusion model based on the dynamic time warping (DTW) algorithm to construct intelligent prediction models for elastic modulus, Poisson’s ratio, and compressive strength using real-time drilling engineering data. An autoencoder with a sliding window is employed to automatically identify abnormal points or segments in the calculated values of elastic modulus, Poisson’s ratio, and compressive strength obtained from drilled wells. These abnormal values are then corrected using a backpropagation (BP) neural network. Compared to single CNN-LSTM or single RF models, the RF-CNN-LSTM fusion model performs better. It achieves this by effectively combining the strengths of different algorithms in predicting outcomes. The accuracy of the RF-CNN-LSTM fusion model is over 94% when compared to the actual values. Furthermore, the analysis of the relative importance of input parameters reveals that weight on bit (WOB), temperature, displacement, equivalent circulation density (ECD), and mud density are the primary input features for predicting elastic modulus. For predicting Poisson’s ratio, the main input features include WOB, mud density, ECD, temperature, pumping pressure, displacement, and rate of penetration (ROP). Similarly, for predicting compressive strength, the main input features consist of WOB, temperature, displacement, ECD, and mud density. The research findings demonstrate that the rock mechanics parameter prediction models based on the RF-CNN-LSTM algorithm using DTW exhibit high computational accuracy in the B oil field of China. These results are significant for gaining a deeper understanding of the variations in rock mechanics parameters and optimizing drilling decisions.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.

Pemanfaatan Mediapipe Body Pose Estimation dan Dynamic Time Warping untuk Pembelajaran Tari Remo

Video can be used as a learning medium for various purposes. In this research, the object of study is the movements of the traditional dance "remo." Thus, as a substitute for an absent coach or instructor, videos can take on the role of a dance instructor. However, video communication is one-way between the coach and the learners. Without movement correction, individuals trying to learn remo dance may find it challenging to determine if they are performing the movements correctly. Therefore, the author aims to create a system to assist coaches in correcting the dance movements of their learners. Using the MediaPipe module and the Dynamic Time Warping algorithm, the author developed a system to correct the learners' dance movements. This system can detect deviations from the coach's instructional video and provide notifications about which body angles do not match the coach's video instructions. The system operates by having users upload a video of their dance movements, and then it identifies which movements deviate from the correct remo dance. The accuracy is measured by comparing the angle distances between the master's movements and the test data. If the angle exceeds a predetermined threshold, the movement is considered incorrect. The system's output is validated by the coach, and it achieves 90% accuracy in identifying movement errors in videos. With this accuracy, the system can assist coaches in evaluating their learners' remo dance movements.

Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home

This study presents a markerless video-based human gait analysis system for automatic assessment of at-home rehabilitation. A marker-based MoCap system (Vicon) is used to evaluate the accuracy of the proposed approach. Additionally, a novel gait rehabilitation score based on the Dynamic Time Warping (DTW) algorithm is introduced, enabling quantification of rehabilitation progress. The accuracy of the proposed approach is assessed by comparing it to a marker-based MoCap system (Vicon), which is used to evaluate the proposed approach. This evaluation results in mean absolute errors (MAE) of 4.8° and 5.2° for the left knee, and 5.9° and 5.7° for the right knee, demonstrating an acceptable accuracy in knee angle measurements. The obtained scores effectively distinguish between normal and abnormal gait patterns. Subjects with normal gait exhibit scores around 97.5%, 98.8%, while those with abnormal gait display scores around 30%, 29%, respectively. Furthermore, a subject at an advanced stage of rehabilitation achieved a score of 65%. These scores provide valuable insights for patients, allowing them to assess their rehabilitation progress and distinguish between different levels of gait recovery. The proposed markerless approach demonstrates acceptable accuracy in measuring knee joint angles during a sagittal walk and provides a reliable rehabilitation score, making it a convenient and cost-effective alternative for automatic at-home rehabilitation monitoring.

A GPS multipath mitigation method in coordinate-domain considering the effects of gross errors and missing data

Multipath is a significant obstacle to achieving high-precision deformation monitoring using the Global Positioning System (GPS) technique. Sidereal filtering (SF) can mitigate GPS multipath effects based on the multipath models derived from historical measurements, but its practical application in complex scenarios is still challenging due to unavoidable gross errors and missing data problems. In this paper, an improved SF algorithm is proposed that accurately extracts multipath immune to the effects of gross errors and missing values in historical measurements, aiming to obtain accurate displacements in a harsh environment. Specifically, a gross error detection method is first developed based on the dynamic time warping (DTW) algorithm to filter gross errors. Then, we adopt the multichannel singular spectrum analysis (MSSA) algorithm to extract initial multipath errors. Finally, we propose a weighted fusion (WF) algorithm to eliminate the impact of missing values and obtain the final multipath sequences. Extensive experiments with datasets collected from a real-world landslide, including both stable and sliding stations, demonstrate the effectiveness of our proposed method. Results show that compared with the traditional SF method, our proposed method can effectively mitigate the effects of gross errors and missing values on multipath modeling, with the multipath filtering accuracy improving by approximately 12% and 21% for stable and sliding scenarios, respectively.

Anomaly Detection of Axial Piston Pump Based on the DTW-RCK-IF Composite Method Using Pressure Signals

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use.

Effectiveness of Green Bonds in Selected CEE Countries: Analysis of Similarities

Green bonds are an increasingly important area not only in the financing of investments important to the environment, but recently also as an object of investment. From the investors point of view, the key aspect still remains the efficiency of the investment and its profitability. The subject of this research is to evaluate changes in the efficiency of green bonds issued in the selected CEE countries (Poland, Slovakia, Czech Republic, and Hungary), in the short and long term. Poland is the largest issuer of green bonds in this group, followed by the Czech Republic, Hungary, and Slovakia. Individual green bonds in these group of countries are characterized by varying levels of green bond yields, duration of the investment, issue size and counterparty risk. These factors greatly hinder their comparability, especially in terms of investment efficiency. This manuscript fits into this area, as the main purpose of the manuscript is to show similarities in the yields of green bonds issued in Poland and green bonds issued in CEE countries. The hypothesis that will be tested is that changes in the effectiveness of green bonds issued in Poland are strongly correlated with changes in the effectiveness of green bonds issued in CEE countries. The results of the research positively verified the hypothesis, and the objectives of the research were achieved. It was shown that green bonds issued in the Czech Republic and Slovakia demonstrate a high similarity in terms of effectiveness to green bonds issued in Poland. At the same time, the results confirmed that of all the bonds analysed, the one bond issued by the Hungarian government is the least related to green bonds issued in Poland in terms of effectiveness for investors. The study used multiresolution analysis and Dynamic Time Warping. The Dynamic Time Warping algorithm measures the similarity between two sequences that can change over time. The analysis was carried out over a wide temporal cross-section, analysing the similarity between the effectiveness in both the short and long term.

Signal enhancement and efficient DTW-based comparison for wearable gait recognition

The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals.

Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series.

This article aims to design a trend-oriented-granulation-based fuzzy C -means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, l1 trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.

Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting

Wind energy has attracted more and more attention due to its sustainability and pollution-free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of great importance for fully harvesting wind energy and ensuring the stability and reliability of wind energy systems. By accurately predicting the future wind speed, we can make efficient power schedules and consequently improve the utilization of wind power. Although existing forecasting methods have achieved satisfactory wind forecasting performance, they may still fail to discover the intricate spatio-temporal dependencies obscured in entangled patterns. To resolve this issue and further improve the prediction accuracy, we propose Mixformer, a novel mixture Transformer with hierarchical context for spatio-temporal wind speed forecasting. Mixformer first applies seasonal-trend decomposition, and then employs an MLP to predict the trend part and an attention model to predict the seasonal part. Besides, Mixformer innovatively proposes a spatio-temporal Gaussian mixture attention (ST-GMA) layer, which fuses the periodic temporal and long-term spatial context. To capture intricate long-term spatial characteristics, Mixformer extracts global information by the Dynamic Time Warping algorithm. Moreover, with the ST-GMA layer, Mixformer builds a hierarchical encoder–decoder architecture to fully utilize the context at different scales for seasonal forecasting and further enhance the spatio-temporal modeling. Empirical experiments on four real-world benchmark datasets show that Mixformer yields the lowest prediction errors with the MAE scores of 1.66, 1.68, 3.14 and 0.70 and the RMSE scores of 2.07, 2.01, 3.99 and 0.98, and improves the prediction accuracy by 8.43%, 5.36%, 4.14%, 6.57% in term of MAE scores and 9.18%, 6.92%, 4.01%, 5.75% in term of RMSE scores compared to state-of-the-art methods, which demonstrates the superiority of Mixformer. In a nutshell, Mixformer presents a promising method for spatio-temporal wind speed forecasting and may achieve desirable performance in real-world applications of wind power systems.

Severity Classification Using Dynamic Time Warping-Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study.

In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores. This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level. We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy. Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/. The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

Detecting early-stage Parkinson's disease from gait data.

Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.

Application of dynamic time warping optimization algorithm in speech recognition of machine translation

Speech recognition is the foundation of human-computer interaction technology and an important aspect of speech signal processing, with broad application prospects. Therefore, it is very necessary to recognize speech. At present, speech recognition has problems such as low recognition rate, slow recognition speed, and severe interference from other factors. This paper studied speech recognition based on dynamic time warping (DTW) algorithm. By introducing speech recognition, the specific steps of speech recognition were understood. Before performing speech recognition, the speech that needs to be recognized needs to be converted into a speech sequence using an acoustic model. Then, the DTW algorithm was used to preprocess speech recognition, mainly by sampling and windowing the speech. After preprocessing, speech feature extraction was carried out. After feature extraction was completed, speech recognition was carried out. Through experiments, it can be found that the recognition rate of speech recognition on the basis of DTW algorithm was very high. In a quiet environment, the recognition rate was above 93.85 %, and the average recognition rate of the 10 selected testers was 95.8 %. In a noisy environment, the recognition rate was above 91.4 %, and the average recognition rate of the 10 selected testers was 93 %. In addition to high recognition rate, DTW based speech recognition also had a very fast speed for vocabulary recognition. Based on the DTW algorithm, speech recognition not only has a high recognition rate, but also has a faster recognition speed.

Data-similarity-based IoT node selection for UAV trajectory optimization

Data collected from densely deployed IoT sensor nodes via UAVs may incur remarkable redundancy, unnecessarily wasting the UAV power. This paper introduces a framework of UAV trajectory planning for optimizing energy efficiency via data-similarity-based node selection while maintaining sufficient information integrity out of the refined data. The proposed framework consists of three phases, namely data similarity determination, redundant nodes removal, and UAV trajectory planning. In particular, we propose a sliding-window dynamic time warping (SDTW) algorithm to quantify the data similarity between nodes. Then a hybrid genetic ant colony algorithm (HGACA) is introduced for the redundant node removal, where data similarity and UAV energy consumption are jointly considered. Finally, we formulate the trajectory planning problem as a three-stage integer linear programming (ILP) model, which clusters the nodes with minimal overlap and finds the shortest UAV cruising path that traverses each cluster head once and only once. The simulation result demonstrates that the proposed framework outperforms all the considered counterparts under various threshold values of data similarity in terms of execution time and power consumption while maintaining information integrity.

Deep Learning Techniques for Radar-Based Continuous Human Activity Recognition

Human capability to perform routine tasks declines with age and age-related problems. Remote human activity recognition (HAR) is beneficial for regular monitoring of the elderly population. This paper addresses the problem of the continuous detection of daily human activities using a mm-wave Doppler radar. In this study, two strategies have been employed: the first method uses un-equalized series of activities, whereas the second method utilizes a gradient-based strategy for equalization of the series of activities. The dynamic time warping (DTW) algorithm and Long Short-term Memory (LSTM) techniques have been implemented for the classification of un-equalized and equalized series of activities, respectively. The input for DTW was provided using three strategies. The first approach uses the pixel-level data of frames (UnSup-PLevel). In the other two strategies, a convolutional variational autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. The second approach for equalized data series involves the application of four distinct feature extraction methods: i.e., convolutional neural networks (CNN), supervised and unsupervised CVAE, and principal component Analysis (PCA). The extracted features were considered as an input to the LSTM. This paper presents a comparative analysis of a novel supervised feature extraction pipeline, employing Sup-ENLevel-DTW and Sup-EnLevel-LSTM, against several state-of-the-art unsupervised methods, including UnSUp-EnLevel-DTW, UnSup-EnLevel-LSTM, CNN-LSTM, and PCA-LSTM. The results demonstrate the superiority of the Sup-EnLevel-LSTM strategy. However, the UnSup-PLevel strategy worked surprisingly well without using annotations and frame equalization.

Mag-ODO: Motion speed estimation for indoor robots based on dual magnetometers

In indoor environments, accurate speed estimation is crucial for providing continuous and reliable position of mobile robots. However, conventional odometry methods may suffer from performance degradation, particularly in scenarios involving wheel odometry slipping or visual odometry blurring. In this study, we propose Mag-ODO, a novel motion speed estimation method based on dual magnetometers aimed at enhancing the robustness of indoor robot positioning systems. The dual magnetometers are mounted at the front and back of the robot, and the speed is estimated by matching the magnetic-filed waveforms sampled from the magnetometers. We employ the dynamic time warping (DTW) algorithm to implement waveform matching and use both the magnetic field strengths and the changing trend of the magnetic field strengths as the matching cost function, effectively reducing the matching error. Mag-ODO has two key advantages: immunity to magnetometer bias and transient disappearance of magnetic field gradients within a matching time window does not affect speed estimation accuracy. Test results show that Mag-ODO performs similarly to Wheel-ODO in magnetic-rich environments (RMSE < 0.06 m/s) and comparable dead reckoning (DR) performance with inertial navigation systems (INS) in both straight and curved environments.

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method.

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.

Taijiquan Auxiliary Training and Scoring Based on Motion Capture Technology and DTW Algorithm

Learning Tai Chi requires long-term practice and guidance, which is difficult for beginners. This article proposes a Tai Chi-assisted training and scoring method based on motion capture technology and a dynamic time warping (DTW) algorithm. Firstly, by using motion capture technology, the key point data of Tai Chi movements can be accurately captured. Then, using the DTW algorithm, the learner's action sequence is compared and matched with the standard Tai Chi action sequence in order to evaluate the learner's action accuracy and fluency. Learners can promptly correct incorrect actions and improve the accuracy and fluency of their actions. This method has significant advantages in accuracy and reliability. In summary, the Tai Chi-assisted training and scoring method based on motion capture technology and DTW algorithm provides an effective auxiliary tool for Tai Chi learners, which can help them better master the techniques and essence of Tai Chi. This study is of great significance for promoting the popularization of Tai Chi and improving the learning effectiveness of Tai Chi.

Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification.

Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test

A tailing storage facility (TSF) is a complex hydrotechnical structure that requires continuous monitoring to prevent catastrophic dam damage. One critical issue to control is the soil’s characteristics, which is why many field and laboratory tests are carried out on the dam to determine the relevant soil parameters. Among these tests, down-hole seismic tests, such as SCPT, are performed to determine, e.g., the shear wave velocity. However, accurately calculating the difference in the times of the arrival of the wave at the two geophones is crucial to determining its value. This article proposes a novel method for estimating this variable using the DTW (Dynamic Time Warping) algorithm, which calculates the shift between two signals by determining their optimal match. The article also addresses signal interference and proposes methods for clearing it to obtain more accurate results. Furthermore, the article introduces a method for measuring the signals’ quality based on their similarity, which helps assess whether determining the shear wave velocity is possible for a given sample.