• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1095-1109
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• 2022

Along with the popularity of GPS system and smart cell phone, trajectories of pedestrians or vehicles are recorded at any time. The great amount of works had been carried out in order to discover traffic paradigms or other regular patterns buried in the huge trajectory dataset. The core of the mining algorithm is how to evaluate the similarity, that is, the "distance", between trajectories appropriately, then the mining results will be accordance to the reality. Euclidean distance is commonly used in the lots of existed algorithms to measure the similarity, however, the trend of trajectories is usually ignored during the measurement. In this paper, a novel segment transform distance (STD) algorithm is proposed, in which a rule system of line segment transformation is established. The similarity of two-line segments is quantified by the cost of line segment transformation. Further, an improvement of STD, named ST-DTW, is advanced with the use of the traditional method dynamic time warping algorithm (DTW), accelerating the speed of calculating STD. The experimental results show that the error rate of ST-DTW algorithm is 53.97%, which is lower than that of the LCSS algorithm. Besides, all the weights of factors could be adjusted dynamically, making the algorithm suitable for various kinds of applications.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.39-45
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• 2012

Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by the state of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiac arrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on the detection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processing techniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized data and variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECG and abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MITBIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for Rpeak detection is 99.63 % and abnormal detection is 99.60 %.

• MALSORI
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• no.57
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• pp.99-112
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• 2006

In this paper, we introduce two retrieval methods for photos with speech documents. We compare the pattern of speech query with those of speech documents recorded in digital cameras, and measure the similarities, and retrieve photos corresponding to the speech documents which have high similarity scores. As the first approach, a phoneme recognition scheme is used as the pre-processor for the pattern matching, and in the second one, the vector quantization (VQ) and the dynamic time warping (DTW) are applied to match the speech query with the documents in signal domain itself. Experimental results show that the performance of the first approach is highly dependent on that of phoneme recognition while the processing time is short. The second method provides a great improvement of performance. While the processing time is longer than that of the first method due to DTW, but we can reduce it by taking approximated methods.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.599-605
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• 2017

Accurate cloud discrimination in satellite images strongly affects accuracy of remotely sensed parameter produced using it. Especially, cloud contaminated pixel over ocean is one of the major error factors such as Sea Surface Temperature (SST), ocean color, and chlorophyll-a retrievals,so accurate cloud detection is essential process and it can lead to understand ocean circulation. However, static threshold method using real-time algorithm such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Himawari Imager (AHI) can't fully explained reflectance variability over ocean as a function of relative positions between the sun - sea surface - satellite. In this paper, we assembled a reflectance spectral library as a function of Solar Zenith Angle (SZA) and Viewing Zenith Angle (VZA) from ocean surface reflectance with clear sky condition of Advanced Himawari Imager (AHI) identified by NOAA's cloud products and spectral library is used for applying the Dynamic Time Warping (DTW) to detect cloud pixels. We compared qualitatively between AHI cloud property and our results and it showed that AHI cloud property had general tendency toward overestimation and wrongly detected clear as unknown at high SZA. We validated by visual inspection with coincident imagery and it is generally appropriate.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.85-95
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• 1996

The purpose of this paper Is to propose a dubbed signal time-synchroniztion technique based on the SOLA(Synchronized Over-Lap and Add) method which has been widely used to modify the time scale of speech signal. In broadcasting audio recording environments, the high degree of background noise requires dubbing process. Since the time difference between the original and the dubbed signal ranges about 200mili seconds, process is required to make the dubbed signal synchronize to the corresponding image. The proposed method finds he starting point of the dubbing signal using the short-time energy of the two signals. Thereafter, LPC cepstrum analysis and DTW(Dynamic Time Warping) process are applied to synchronize phoneme positions of the two signals. After determining the matched point by the minimum mean square error between orignal and dubbed LPC cepstrums, the SOLA method is applied to the dubbed signal, to maintain the consistency of the corresponding phase. Effectiveness of proposed method is verified by comparing the waveforms and the spectrograms of the original and the time synchronized dubbing signal.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.363-370
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• 2023

Due to the spread of COVID-19, the use of public transportation such as urban railroads has changed significantly since the beginning of 2020. Therefore, in this study, daily time series data for each urban railway station were collected for three years before COVID-19 and after the spread of COVID-19, and the similarity of time series analysis was evaluated through DTW (Dynamic Time Warping) distance method to derive regression centers for each cluster, and the effect of various external events such as COVID-19 on changes in the number of users was diagnosed as a time series impact detection function. In addition, the characteristics of use by cluster of urban railway stations were analyzed, and the change in passenger volume due to external shocks was identified. The purpose was to review measures for the maintenance and recovery of usage in the event of re-proliferation of COVID-19.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.201-206
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• 2004

This study attempted to develop a speech recognition module applied to a wheelchair for the physically handicapped. In the proposed speech recognition module, TMS320C32 was used as a main processor and Mel-Cepstrum 12 Order was applied to the pro-processor step to increase the recognition rate in a noisy environment. DTW (Dynamic Time Warping) was used and proven to be excellent output for the speaker-dependent recognition part. In order to utilize this algorithm more effectively, the reference data was compressed to 1/12 using vector quantization so as to decrease memory. In this paper, the necessary diverse technology (End-point detection, DMA processing, etc.) was managed so as to utilize the speech recognition system in real time

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.37-40
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• 2024

본 연구에서는 웨어러블 디바이스로부터 수집된 다변량 반려동물 행동 데이터를 처리하기 위해, GCN(Graph Convolutional Network)과 GRU(Gated Recurrent Unit)를 결합한 모델을 제안한다. 제안된 모델은 시계열 내부 구조를 활용하여 그래프 구조로 변환하고, DTW(Dynamic Time Warping) 유사도 분석을 통해 노드 간의 시간적 유사도를 기반으로 엣지를 생성한다. 실험결과로 DTW 기반 엣지 생성 방식이 유클리드 거리 및 선형 방식에 비해 더 높은 성능을 나타냈다. 본 연구는 반려동물의 행동을 정확히 분류하기 위한 효과적인 방법론을 제공한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.2
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• pp.196-205
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• 1991

This paper is a study on the isolated word recognition of speaker independent which proposes to newly improved MSVQ(multisection vector quantization) recognition system which improve the classical MSVQ recognition system. It is a difference that test pattern has on more section than reference pattern in recognition system 146 DDD area names are selected as recognition vocabulary. 12th LPC cepstral coefficients is used as feature parameter. and when codebook is generated, MINSUM and MINMAX are used in finding the centroid. According to the experiment result. it is proved that this method is better than VQ(vector quantization) recognition methods, DTW(dynamic time warping) pattern matching methods and classical MSVQ methods for recognition rate and recognition time.