• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.93-96
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• 2022

DTW can eliminate gaps between sequences of different lengths and find out the similarity of patterns, but due to the time and space complexity, it requires a high computational cost on large datasets. In this paper, we propose a DDTW algorithm that not only reduces computational costs but also has no error in the results. In addition, the algorithm complexity of DTW and DDTW is compared by measuring the computational time according to the length of the sequence. Simulation results show a noticeable reduction in computational time in DDTW compared to DTW.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.52-59
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• 2011

In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.851-876
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• 2018

Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for quasi-periodic time series. In the current situation, except the recently published the shape exchange algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time series containing different number of periods each), according to the recent classification of time series alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery, classification, etc.).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.209-219
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• 2004

In order to implement a real time hardware for keyword spotting, we propose a Single-End-Point DTW(SEP-DTW) algorithm which is simple and less complex for computation. The SEP-DTW algorithm only needs a single end point which enables efficient applications, and it has a small wont of computations because the global search area is divided into successive local search areas. Also, we adopt new local constraints and a new distance measure for a better performance of the SEP-DTW algorithm. Besides, we make a normalization of feature same vectors so that they have the same variance in each frequency bin, and each frame has the same energy levels. To construct several reference patterns for each keyword, we use a clustering algorithm for all training patterns, and mean vectors in every cluster are taken as reference patterns. In order to detect a key word for input streams of speech, we measure the distances between reference patterns and input pattern, and we make a decision whether the distances are smaller than a pre-defined threshold value. With isolated speech recognition and keyword spotting experiments, we verify that the proposed algorithm has a better performance than other methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.8
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• pp.639-647
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• 1990

Dynamic time warp(DTW) needs for interative calculations and the design of PE cell suitable for the operations is very important. Accordingly, this paper aims at the real time recognition design which enables large dictionary hardware realization using DTW algorithm. The DTW PE cell is seperated into three large blocks. "MIN" is the one block for counting accumulated minimum distance, "ADD" block calculates these minimum distances, and "ABS" seeks for the absolute values to the total sum of local distances. We have accomplisehd circuit design and verification for the MIN blocks, and performed MIN layout and DRC(design rule check) using 3um CMOS N-Well rule base.ing 3um CMOS N-Well rule base.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.432-434
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• 2008

This paper deals with the DTW algorithm for the filtering contents, in order to improve the filtering performance rate. Contents filtering is the technology that confirm the identification of contents by using the feature of voice. Such technique is classified into general contents and adults contents. This proposed method extracts the information of voice contribute to improvement of filtering contents. In other words, We proposed filtering identification rate can be improved by using DTW algorithm. As a result, the proposed method is utilized improvement of filtering contents. Finally, we provide contents examples to test the accuracy of the proposed feature. Consequently, We know that the difference of characteristic between general contents and adults contents. In the future, We utilize this to improve filtering performance rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.592-600
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• 2008

In this paper, a Hybrid Scaling based DTW (HS-DTW) mechanism is proposed for detection of periodic shrew TCP attacks. A low-rate TCP attack which is a type of shrew DoS (Denial of Service) attacks, was reported recently, but it is difficult to detect the attack using previous flooding DoS detection mechanisms. A pattern matching method with DTW (Dynamic Time Warping) as a type of defense mechanisms was shown to be reasonable method of detecting and defending against a periodic low-rate TCP attack in an input traffic link. This method, however, has the problem that a legitimate link may be misidentified as an attack link, if the threshold of the DTW value is not reasonable. In order to effectively discriminate between attack traffic and legitimate traffic, the difference between their DTW values should be large as possible. To increase the difference, we analyze a critical problem with a previous algorithm and introduce a scaling method that increases the difference between DTW values. Four kinds of scaling methods are considered and the standard deviation of the sampling data is adopted. We can select an appropriate scaling scheme according to the standard deviation of an input signal. This is why the HS-DTW increases the difference between DTW values of legitimate and attack traffic. The result is that the determination of the threshold value for discrimination is easier and the probability of mistaking legitimate traffic for an attack is dramatically reduced.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.181-186
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• 2011

In this paper, we propose the Noise Cancel DTW that to use a kind of feature compensation. This method is not to use estimated noise but we use real life environment noise data for Voice Recognition. And we applied this contaminated data for recognition reference model that suitable for noise environment. NCDTW is combined with surround noise when generating reference patten. We improved voice recognition rate at mobile environment to use NCDTW.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.482-486
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• 2010

Recently, gesture recognition methods based on dynamic time warping (DTW) have been actively investigated as more mobile devices have equipped the accelerometer. DTW has no additional training step since it uses given samples as the matching templates. However, it is difficult to apply the DTW on mobile environments because of its computational complexity of matching step where the input pattern has to be compared with every templates. In order to address the problem, this paper proposes a gesture recognition method based on DTW that uses localized subset of templates. Here, the k-means clustering algorithm is used to divide each class into subclasses in which the most centered sample in each subclass is employed as the localized template. It increases the recognition speed by reducing the number of matches while it minimizes the errors by preserving the diversities of the training patterns. Experimental results showed that the proposed method was about five times faster than the DTW with all training samples, and more stable than the randomly selected templates.