Explore the vast range of titles available.

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Monitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making. This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted for broad landcover types in one of the areas of interest (AOIs). We implemented a pixel-wise -nearest neighbor classification model with DTW distance to identify paddy rice pixels. Standard rice phenological profiles of the SAR VH band were defined by ground monitoring of a sample rice field. Based on rice planting maps and the standard phenological profiles, rice phenological phases were estimated by pattern matching strategy with the sub-DTW algorithm. Experiments on six counties in Northeast China presented promising results. The overall producer and user accuracy reached 92.9 and 91.9% for rice mapping, respectively. The mean root mean square error (RMSE) for phenology estimation was 3.5 days. Rice planting and rice phenology maps were generated for the six AOIs. The phenological variances of the AOIs implied the effects of climate and rice cultivars on phenological development.

We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing HGR algorithms do not consider this characteristic, which results in the degradation of recognition performance. Because the dynamic time warping (DTW) technique considers the time-dependent characteristic of IMU sensor data, the recognition performance of DTW-based algorithms is better than that of others. However, the DTW technique requires a very complex learning algorithm, which makes it difficult to support real-time learning. To solve this issue, the proposed HGR algorithm is based on a restricted column energy (RCE) neural network, which has a very simple learning scheme in which neurons are activated when necessary. By replacing the metric calculation of the RCE neural network with DTW distance, the proposed algorithm exhibits superior recognition performance for time-dependent sensor data while supporting real-time learning. Our verification results on a field-programmable gate array (FPGA)-based test platform show that the proposed HGR algorithm can achieve a recognition accuracy of 98.6% and supports real-time learning and recognition at an operating frequency of 150 MHz.

Sound is one of the most common communication medias used by humans. Every human has different sound characteristics. To recognize the compatibility of a sound, a special algorithm is needed, which is Dynamic Time Warping (DTW). DTW is a method to measure the similarity of a pattern with different time zones. The smaller the distance produced, the more similar between the two sound patterns. Both sound patterns are similar, thus the two voices are said to be the same. The initial data on the speech recognition process is transformed into frequency waves. Pronounce volume, pronunciation time, and noise from the sound around the recording takes place affecting the distance generated. The smaller the effect, the smaller the distance that will be generated.

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

Active sonar signal matching is a critical technique for measuring inter-signal similarity and enhancing target detection and classification performance. However, in complex underwater environments, noise, reverberation, and prolonged signal durations often degrade matching accuracy and computational efficiency. To address these challenges, this paper proposes an adaptive extremum-aligned boundary-constrained dynamic time warping (AEB-DTW) algorithm, based on the classical dynamic time warping (DTW) framework. The algorithm extracts significant extrema from signal envelopes to suppress noise and reverberation while capturing salient features. By integrating the position and amplitude of extrema, an adaptive weighted matching strategy is introduced to enhance feature discrimination. In addition, spline fitting is applied to the residuals of the extremum matching path to dynamically generate upper and lower boundary constraints, thus restricting DTW computation to a meaningful region and achieving a balance between accuracy and efficiency. Experiments using lake-trial active sonar data under signal-to-reverberation ratios (SRRs) from 0 dB to 30 dB show that AEB-DTW outperforms Euclidean distance (ED), DTW, and its variants in matching accuracy, robustness, and angular resolution, while significantly improving computational efficiency, particularly for long-duration signals.

This study presents a comprehensive evaluation of the quick invariant signature (QIS), dynamic time warping (DTW), and a novel hybrid QIS + DTW approach for time series analysis. QIS, a translation and rotation invariant shape descriptor, and DTW, a widely used alignment technique, were tested individually and in combination across various datasets, including ECG5000, seismic data, and synthetic signals. Our hybrid method was designed to embed the structural representation of the QIS with the temporal alignment capabilities of DTW. This hybrid method achieved a performance of up to 93% classification accuracy on ECG5000, outperforming DTW alone (86%) and a standard MLP classifier in noisy or low-data conditions. These findings confirm that integrating structural invariance (QIS) with temporal alignment (DTW) yields superior robustness to noise and time compression artifacts. We recommend adopting hybrid QIS + DTW, particularly for applications in biomedical signal monitoring and earthquake detection, where real-time analysis and minimal labeled data are critical. The proposed hybrid approach does not require extensive training, making it suitable for resource-constrained scenarios.

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

This paper presents a summary of a range of characteristic parameters that define the tone features. This is achieved by studying the time-frequency and frequency characteristics of music signals with different instrumental timbres, and it represents the characteristics of the music in various frequency bands and time domains. The optimized DTW pattern recognition algorithm achieves the classification of music styles. The conducted experiments clearly recognized several basic violin bowing styles. Jazz’s classification and recognition effect is 80% accurate. The accuracy rate of the music brief spectrum recognition exceeded 95%. The teaching method based on music pattern recognition has a significant teaching effect in the knowledge and skill dimensions, with a Sig. value of 0.001.

At a time of rapid climate change and an uncertain geopolitical situation caused by the war in Ukraine, the problem of access to energy is a serious issue. The use of renewable energy sources and ensuring the highest possible energy independence are becoming important. They are in line with the seventh Sustainable Development Goal (SDG7). The aim of our research is to compare European countries in terms of the degree of SDG7 implementation and its dynamics from 2005 to 2020. We assess the SDG7 implementation using the COPRAS method and compare its dynamics using the Dynamic Time Warping (DTW) and hierarchical clustering. In years 2005, 2009 and 2020, we present rankings of countries in terms of the SDG7 implementation. Norway, Denmark, Sweden, Croatia, and Estonia were ranked the best, and Luxembourg, Belgium, Bulgaria, Lithuania, Iceland, and Cyprus—the worst. We obtained eight clusters with respect to dynamics of the degree of SDG7 implementation. In Poland, Romania, Belgium, Luxembourg, Latvia, and Ireland, the relative dynamics was increasing, while in the Nordic and South European countries, it was decreasing. The novelty of our research is combining the COPRAS (assessment of SDG7 implementation) and DTW methods (selection of similar countries with respect to its dynamics).