• 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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.33-40
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• 2007

In this paper, low-rate TCP attack as one of shrew attacks is considered and the scaling based dynamic time warping (S-DTW) algorithm is introduced. The low-rate TCP attack can not be detected by the detection method for the previous flooding DoS/DDoS (Denial of Service/Distirbuted Denial of Service) attacks due to its low average traffic rate. It, however, is a periodic short burst that exploits the homogeneity of the minimum retransmission timeout (RTO) of TCP flows and then some pattern matching mechanisms have been proposed to detect it among legitimate input flows. A DTW mechanism as one of detection approaches has proposed to detect attack input stream consisting of many legitimate or attack flows, and shown a depending method as well. This approach, however, has a problem that legitimate input stream may be caught as an attack one. In addition, it is difficult to decide a threshold for separation between the legitimate and the malicious. Thus, the causes of this problem are analyzed through simulation and the scaling by maximum auto-correlation value is executed before computing the DTW. We also discuss the results on applying various scaling approaches and using standard deviation of input streams monitored.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.141-147
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• 2011

This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.419-423
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• 2005

This paper is a research on feature extraction and comparison method of dynamic (on-line) signature verification. We suggest desirable feature information and modified DTW(Dynamic Time Warping) and describe the performance results of our enhanced dynamic signature verification system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.19-26
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• 2010

In this paper, we suggest a efficient handwritten character verification method in on-line environments which automatically analyses two input character string and computes their similarity degrees. The proposed algorithm first applies the circular projection method to input handwritten strings and extracts their representative features including shape, directions, etc. It then calculates the similarity between two character strings by using an improved dynamic time warping (DTW) algorithm. We improved the conventional DTW algorithm efficiently through adopting the branch-and-bound policy to the existing DTW algorithm which is well-known to produce good results in the various optimization problems. The experimental results to verify the performance of the proposed system show that the suggested handwritten character verification method operates more efficiently than the existing DTW and DDTW algorithms in terms of the speed.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Smart Media Journal
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• v.13 no.4
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• pp.16-22
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• 2024

Pitching is a major part of baseball, so much so that it can be said to be the beginning of baseball. Analysis of accurate pitching motions is very important in terms of performance improvement and injury prevention. When analyzing the correct pitching motion, the currently used motion capture method has several critical environmental drawbacks. In this paper, we propose analysis of pitching motion using the RGB-based Human Pose Estimation (HPE) model to replace motion capture, which has these shortcomings, and use motion capture data and HPE data to verify its reliability. The similarity of the two data was verified by comparing joint coordinates using the Dynamic Time Warping (DTW) algorithm.