• Proceedings of the KSRS Conference
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• v.2
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• pp.764-767
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• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab- and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.243-244
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• 2008

기존의 고정 부하 중심점 방식의 ULTC 운전제어전략은 계절별 부하 변화에 따른 전압보상을 효율적으로 실현하기 어렵다. 따라서 가변 부하 중심점 방식의 ULTC 운전제어전략들이 제안되는데, ANN이나 Fuzzy 멤버쉽 함수를 설계하는 문제는 고도의 전문적 설계경험과 상당한 시간을 요구한다. 따라서 본 연구에서는 적용이 쉬운 최소거리 판별기법을 적용, 부하변화에 따라 ULTC의 운전 부하 중심점을 이동시켜, 전압보상 범위를 개선시킬 수 있는 디지털 ULTC 운전 제어전략을 제안한다.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.225-232
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• 2000

The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.53-57
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• 2003

Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1760-1778
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• 2018

For image segmentation with intensity inhomogeneity, many region-based level set methods have been proposed. Some of them however can't get the relatively ideal segmentation results under the severe intensity inhomogeneity and weak edges, and without use of the image gradient information. To improve that, we propose a new level set method combined with local direction gradient in this paper. Firstly, based on two assumptions on intensity inhomogeneity to images, the relationships between segmentation objects and image gradients to local minimum and maximum around a pixel are presented, from which a new pixel classification method based on weight of Euclidian distance is introduced. Secondly, to implement the model, variational level set method combined with image spatial neighborhood information is used, which enhances the anti-noise capacity of the proposed gradient information based model. Thirdly, a new diffusion process with an edge indicator function is incorporated into the level set function to classify the pixels in homogeneous regions of the same segmentation object, and also to make the proposed method more insensitive to initial contours and stable numerical implementation. To verify our proposed method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.89-96
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• 2003

This paper propose the new method witch is adopted in extracting character features and recognizing numeric characters using fractal dimension and modified Henon Attractor of the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from numeric character images And their feature hi converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of numeric character image after calculating Natural Measure and information bit which art meant fractal dimension. Finally, numeric character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An Experimental result shows 100% character classification rates for 10 digits and 90% of recognition rates in real situation and the recognition speed was 26 characters per second.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Proceedings of the KSRS Conference
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• v.2
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• pp.555-558
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• 2006

In recent years, urban development has expanded rapidly in Nawabshah City of Pakistan. A major effect associated with this population trend is transformation of the landscape from natural cover types to increasingly impervious urban land. The core objective of this study are to provide time-series information to define and measure the urban land cover changes of Nawabshah, Pakistan between the years 1992 and 2002, and to examine related urbanization impacts on air quality of the study area. Two multi-temporal Landsat images acquired in 1992 and 2002 together with standard topographical maps to measure land cover changes were used in this study. The image processing and data manipulation were conducted using algorithms supplied with the ERDAS Imagine software. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 84 percent to 92 percent. Land cover statistics demonstrate that during the study period (1992-2002) extensive transformation of barren and vegetated lands into urban land have taken place in Nawabshah City. Results revealed that land cover changes due to urbanization has not only contaminated the air quality of the study area but also raised the health concerns for the local residents.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.207-214
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• 2020

Currently, the operation of unmanned aerial vehicle (UAV) is regulated to be able to fly only within the visible range, but in recent years, the needs for operation in the invisible area, in the urban area and at night have increased. In order to operate UAVs in the invisible area, at night, and in the urban area, a flight path for UAVs must be prepared like those operated by manned aircraft, and for this, it is necessary to establish an unmanned aircraft system traffic management (UTM). In order to establish the UTM, information on the minimum separation distance to prevent collisions with UAVs and buildings is required, and accordingly, information on the navigation performance of UAVs is required. In order to analyze the navigation performance of an UAV, total system error (TSE), which is the difference between the planned flight path and the actual location of the UAV, is required. If the collected data are insufficient and classification according to integrity, independence, and direction is not performed, accurate navigation performance is not derived. In this paper, propose a navigation performance analysis method of UAV that is derived TSE using flight data and modeled with normal distribution, analyze performance.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.421-425
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• 2006

It can be important framework data to monitor the change of land-use pattern of river boundary in design and management of river. This study analyzed the change of land-use pattern of Gab and Yudeung River using time-series aerial images. To do this, we carried out radiation and geometric correction of image, and estimated land-use changes in inland and floodplain. As the analysis of inland, the ratio of residential, commercial, industrial, educational and public area, that is urbanized element, increases, but that of agricultural area shows a decline on the basis of 1990. Also, Minimum Distance Method, which is a kind of supervised classification method, is applied to extract water-body and sand bar layer in floodplain. As the analysis of land-use, the ratio of level-upped riverside land and water-body increases, but that of sand bar decreases. These time-series land use information can be important decision making data to evaluate the urbanization of river boundary, and especially it gives us goodness in river development project such as the composition of ecological habitat.