• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.595-602
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• 2009

Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.117-125
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• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.130-145
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• 2021

As part of the Smart Seoul policy, the importance of using big urban data is being highlighted. Furthermore interest in the impact of transportation-related urban environmental factors such as PM10 and noise on citizen's quality of life is steadily increasing. This study established the integrated DB by matching IoT big data with transportation data, including traffic volume and speed in the microscopic Spatio-temporal scope. This data analyzed the impact of a spatial unit in the road-effect zone on environmental risk level. In addition, spatial units with similar characteristics of road traffic and environmental factors were clustered. The results of this study can provide the basis for systematically establishing environmental risk management of urban spatial units such as PM10 or PM2.5 hot-spot and noise hot-spot.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.367-371
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• 2010

가뭄 발생원인은 기후학적인 인자(온도, 바람, 상대습도 등)들과 밀접한 관계를 갖고 있으나, 가장 큰 원인은 강수부족이라고 말할 수 있다. 따라서 가뭄은 정상수준 이하의 강수 상황이 연속적으로 발생하여 나타나며, 설정된 절단수준에 대해 가뭄의 지속기간, 심도, 발생간격 등을 정의한 후 이에 대한 시계열 분석을 수행하여 가뭄의 특성을 분석한다. 본 연구에서는 가뭄 절단수준의 변화에 따른 한반도 내 가뭄의 특성분석을 위하여 하나의 절단수준으로 고정된 경우의 가뭄특성과 각 년도 월별 특성을 고려하여 절단수준이 지속적으로 변화하는 경우로 구분하여, 가뭄특성의 변화를 분석하였다. 또한 위 두 가지 경우에 대해 각각 가뭄해소 여부를 판단하여 총 4가지 경우에 따른 가뭄 특성을 분석하였다. 가뭄 절단수준의 변화 및 가뭄 해소여부에 따른 한반도 내 가뭄 특성을 분석하기 위해, 가뭄의 지속기간, 심도의 기초통계량 등을 산정하여 비교 분석하였다. 본 연구는 한반도 내의 가뭄특성을 보다 정확하게 해석하기 위해서는 다양한 가뭄정의에 따라 가뭄 해석결과가 나타내는 상대적 차이를 비교할 필요성이 있음을 증명하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.415-416
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• 2004

이어도 해양과학기지에서 해수중으로 청음기를 내려 2004년 7월에 3일간 연속적으로 수중 배경소음을 녹음 하였다. 측정된 주파수스펙트럼을 통계처리하고 또한 시계열 신호의 특성을 분석하였다. 해상 풍속도 관측하였다. 전체적으로 한반도 주변 해양의 배경소음에 비해 상대적으로 소음레벨이 작은 경향을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.179-180
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• 2015

운항하는 선박의 특성상 미래 상태를 반영한 예방정비는 선박의 안전한 운항과 운영비용 절감에 중요한 요인이다. 이에 본 논문에서는 선박 엔진의 세 가지 주요 베어링의 마모 상태를 모니터링하고 앞으로의 마모 정도를 예측하는 시스템을 개발하였다. 이 시스템은 현재의 실린더 하사점 레벨 데이터를 기반으로 앞으로의 실린더 하사점 레벨을 예측한다. 실험에 적용한 실린더 하사점 레벨 데이터는 테스트 지그를 제작하여 발생시켰고, 이 장치를 통해서 취득한 데이터를 이용하여 선박 엔진의 미래 상태를 예측하였다.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.117-120
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• 2015

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1139-1149
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• 2014

Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.517-525
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• 2013

Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.