• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.585-592
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• 2004

This study detects Korea economic crisis of 1997 using various change-point detection methods and then compares their performance. In change-point detection method, there are three major categories: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. Through the application to Korea foreign exchange rate during her economic crisis, we compare the employed change-point detection methods and, furthermore, determine which of them performs better.

• 한국조경학회지
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• 제30권1호
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• pp.1-10
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• 2002

The purpose of this article is to examine land use change in the fringe area of a metropolitan city through multi-temporal data analysis. Change detection has been regarded as one of the most important applications for utilization of remotely sensed imageries. Conventionally, two images were used for change detection, and Arithmetic calculators were generally used on the process. Meanwhile, multi-temporal change detection for a large number of images has been carried out. In this paper, a digital land-use map and three Landsat TM data were utilized for the multi-temporal change detection Each urban area map was extracted as a base map on the process of multi-temporal change detection. Each urban area map was converted to bit image by using boolean logic. Various urban change types could be obtained by stacking the urban area maps derived from the multi-temporal data using Geographic Information System(GIS). Urban change type map was created by using the process of piling up the bit images. Then the urban change type map was compared with each land cover map for the change detection. Dalseo-gu of Daegu city and Hwawon-eup of Dalsung-gun, the fringe area of Daegu Metropolitan city, were selected for the test area of this multi-temporal change detection method. The districts are adjacent to each other. Dalseo-gu has been developed for 30 yeais and so a large area of paddy land has been changed into a built-up area. Hwawon-eup, near by Dalseo-gu, has been influenced by the urbanization of Dalseo-gu. From 1972 to 1999, 3,507.9ha of agricultural area has been changed into other land uses, while 72.7ha of forest area has been altered. This agricultural area was designated as a 'Semi-agricultural area'by the National landuse Management Law. And it was easy for the preserved area to be changed into a built-up area once it would be included as urban area. Finally, the method of treatment and management of the preserved area needs to be changed to prevent the destruction of paddy land by urban sprawl on the urban fringe.

• 음성과학
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• 제8권4호
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• pp.59-72
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• 2001

In this paper, we experimented with speaker change detection that uses a statistical method for NOD (News On Demand) service. A specified speaker's change can find out content of each data in speech if analysed because it means change of data contents in news data. Speaker change detection acts as preprocessor that divide input speech by speaker. This is an important preprocessor phase for speaker tracking. We detected speaker change using GLR(generalized likelihood ratio) distance base division and BIC (Bayesian information criterion) base division among matrix method. An experiment verified speaker change point using BIC base division after divide by speaker unit using GLR distance base method first. In the experimental result, FAR (False Alarm Rate) was 63.29 in high noise environment and FAR was 54.28 in low noise environment in MDR (Missed Detection Rate) 15% neighborhood.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.64-67
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• 2004

Change detection using aerial and satellite images is one of the important research topics in photogrammetry and image interpretation. It is of particular importance especially in the fields of military, political and administrative affairs. When there is a need to detect changes in multi-temporal images, the most efficient methods for change detection and thresholds of change/no change area need to be chosen. Also, the images obtained from the various methods need to be analyzed. To do so, we need a system that can support our decision making process. Therefore, in this paper, we propose the Decision Support System for Change Detection. This system is composed of Data Base, Model Base and Graphic User Interface(GUI). Data base is a compilation of previous change detection results, and Model Base comprise of numerous operations. The data can be input and have the results of change detection analyzed by using GUI. In this paper, we will explain the entire operation of the system and demonstrate the level of its effectiveness.

• 대한공간정보학회지
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• 제3권2호
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• pp.151-158
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• 1995

Change detection기법은 영상에서 차이를 알아내기 위하여 가장 많이 사용되는 방법이며 다양한 영상환경에 사용된다. 수치지형모델과 수치영상은 같은 수치격자데이타 구조를 가지므로 Change detection기법을 수치지형모델에 적용할 측량결과의 표고데이타를 불규칙삼각형(TIN)으로부터 격자구조로 변환하고 수치지형모델화하여 구조물의 변형지점을 찾는데 사용함으로 많은 소모성 자재와 인력을 줄일 수 있었다. 그 결과를 가시화하여 건물의 변형이 발생한 지점과 변형향을 수치적으로 나타낼 수 있었다.

• 한국측량학회지
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• 제37권5호
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• pp.389-395
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• 2019

Unlike aerial images covering small region, satellite data show high potential to detect urban scale geospatial changes. The change detection using satellite images can be carried out using single image or stereo images. The single image approach is based on radiometric differences between two images of different times. It has limitations to detect building level changes when the significant occlusion and relief displacement appear in the images. In contrast, stereo satellite data can be used to generate DSM (Digital Surface Model) that contain information of relief-corrected objects. Therefore, they have high potential for the object change detection. Therefore, we carried out a study for the change detection over an urban area using stereo satellite data of two different times. First, the RPC correction was performed for two DSMs generation via stereo image matching. Then, D-DSM (Differential DSM) was generated by differentiating two DSMs. The D-DSM was used for the topographic change detection and the performance was checked by applying different height thresholds to D-DSM.

• 한국측량학회지
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• 제34권2호
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.432-442
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• 2019

Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.

• Journal of Information Processing Systems
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• 제2권1호
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• pp.44-47
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• 2006

One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.25-32
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• 2004

In this study, we use various change-point detection methods to detects Korea economic crisis of 1997, and then compares their performance. In change-point detection method, there are three major categories: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. Through the application to Korea foreign exchange rate during her economic crisis, we compare the employed change-point detection methods and, furthermore, determine which of them performs better.