• 한국농공학회논문집
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• 제65권2호
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• 인터넷정보학회논문지
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• 제6권1호
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• pp.105-114
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• 2005

본 논문에서는 다중 시계열 패턴을 분석하여 계측 값을 예측하는 방법에 관하여 기술한다. 본 논문의 목적은 표본패턴들 중에서 입력패턴과 가장 유사한 패턴을 찾은 다음 그 표본패턴이 가지는 실측값과의 오차율을 산출하는 것이다. 따라서 인식이 아니라 계측이며 하드웨어가 아닌 소프트웨어 기술을 제안하다. 본 논문에서 제안하는 방법은 초기화, 인식 및 계측 등의 단계로 구성된다. 초기화 단계에서는 중요도를 사용하여 인자들 각각의 가중치를 산출한다. 학습 단계에서는 수집된 표본패턴을 먼저 DTW와 LBG 알고리즘을 사용하여 각 인자별 독립적으로 군집화를 수행한 다음, 모든 표본패턴에 대하여 군집의 번호들로 구성된 코드열을 생성한다. 계측 단계에서는 입력패턴에 대한 코드열을 생성한 다음 해슁으로 표본패턴들 중에서 같은 코드열을 가지는 표본들을 찾고, 이 표본들 중에서 입력패턴에 가장 잘 정합되는 하나의 표본을 선택하다. 최종적으로 이 패턴이 가지고 있는 실측값과 오차율을 출력한다. 성능평가는 반도체생산장치 중에서 하나인 식각장치로부터 얻어진 자료에 적용하여 수행한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권2호
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• pp.121-125
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• 2015

In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.

• 대한수학회보
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• 제52권4호
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• pp.1113-1122
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• 2015

This paper is concerned with the pattern formation of a diffusive predator-prey model with two time delays. Based upon an analysis of Hopf bifurcation, we demonstrate that time delays can induce spatial patterns under some conditions. Moreover, by use of a series of numerical simulations, we show that the type of spatial patterns is the spiral wave. Finally, we demonstrate that the spiral wave is asymptotically stable.

• 응용통계연구
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• 제26권5호
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• pp.807-819
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• 2013

확증적 시계열 자료분석 전의 그래픽 탐색적 자료분석방법으로서 재현그림을 사용할 수 있다. 재현그림을 통하여 시계열 자료의 구조적 패턴을 확인할 수 있고 이 패턴을 통하여 탐색적으로 시계열 데이터의 구조 변화점을 한 눈에 확인할 수 있게 된다. 우리나라 주식 자료를 이용하여 재현그림이 시계열 자료를 위한 그래픽 탐색적 자료분석방법으로서 유용함을 보였다.

• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• 한국정밀공학회지
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• 제15권12호
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• pp.226-235
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• 1998

This study proposes the analysis and evaluation of method of time series of ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. The features are extracted from time series data for analysis of weld defects quantitatively. For this purpose, analysis objectives in this study are fractal dimension, Lyapunov exponent, and strange attractor on hyperspace. The Lyapunov exponent is a measure of rate in which phase space diverges nearby trajectories. Chaotic trajectories have at least one positive Lyapunov exponent, and the fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal(correlation) dimensions and Lyapunov exponents show the mean value of 4.663, and 0.093 relatively in case of learning, while the mean value of 4.926, and 0.090 in case of testing in slag inclusion(weld defects) are shown. Therefore, the proposed chaotic feature extraction can be enhancement of precision rate for ultrasonic pattern recognition in defecting signals of weld zone, such as slag inclusion.

• 대한기계학회논문집A
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• 제23권7호
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• pp.1065-1074
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• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaotic time series signal analysis quantitatively welding defects. For this purpose analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shills such as 0.5 and 1.0 skip distance. Such differences in chaoticity enables the evaluation of unique features of defects in the weld zone. In experiment fractal(correlation) dimension and Lyapunov exponent extracted from 6dB ultrasonic defect signals of weld zone showed chaoticity. In quantitative chaotic feature extraction, feature values(mean values) of 4.2690 and 0.0907 in the case of porosity and 4.2432 and 0.0888 in the case of incomplete penetration were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaotic feature extraction in this study enhances ultrasonic pattern recognition results from defect signals of weld zone such as vertical hole.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.703-710
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• 2010

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.

• 정보처리학회논문지D
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• 제9D권2호
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• pp.251-258
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• 2002

대용량의 데이터들로부터 사용자가 인하는 데이터를 찾기 위하여 많은 데이터 마이닝 기술들이 연구되어 실제 응용프로그램에서 많이 적용되고 있다. 이러한 데이터 마이닝 기술들은 시계열 데이터를 이용하는 경우보다 트랜잭션 데이터를 이용하여 유용한 정보를 찾는 경우에 초점이 맞춰져 있다. 본 논문에서는 시계열 데이터를 트랜잭션 데이터로 변환하는 접근방법을 소개한다. 가상 트랜잭션은 서로 상대적으로 근접한 시간에 발생하는 이벤트의 집합이라고 정의하며, 가상 트랜잭션 생성기는 가상 트랜잭션을 생성시 시간윈도우와 이벤트 윈도우 방법을 사용한다. 본 논문의 접근 방법을 사용하여 기존의 트랜잭션 데이터를 이용하는 많은 데이터 마이닝 알고리즘들을 수정 없이 시계열 데이터에 적용하여 유용한 정보를 찾을 수 있다.