• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.21-27
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• 2006

Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.323-330
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• 2019

Radiometric normalization with multi-temporal satellite images is essential for time series analysis and change detection. Generally, relative radiometric normalization, which is an image-based method, is performed, and histogram matching is a representative method for normalizing the non-linear properties. However, since it utilizes global statistical information only, local information is not considered at all. Thus, this paper proposes a histogram matching method considering local information. The proposed method divides histograms based on density, mean, and standard deviation of image intensities, and performs histogram matching locally on the sub-histogram. The matched histogram is then further partitioned and this process is performed again, iteratively, controlled with the wasserstein distance. Finally, the proposed method is compared to global histogram matching. The experimental results show that the proposed method is visually and quantitatively superior to the conventional method, which indicates the applicability of the proposed method to the radiometric normalization of multi-temporal images with non-linear properties.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.1-9
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• 2015

Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.39-42
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• 2000

Motion estimation plays an important role for video coding. In this paper, we derive optimal search patterns for fast block matching motion estimation. By analyzing the block matching algorithm as a function of block shape and size, we can find an optimal search pattern for initial motion estimation. The proposed idea, which has been verified experimentally by computer simulations, can provide an analytical basis for the current MPEG-2 proposals. In order to choose a more compact search pattern for BMA, we exploit the statistical relationship between the motion and the frame difference of each block.

• Speech Sciences
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• v.11 no.4
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• pp.17-28
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• 2004

In this paper, we propose a statistical approach to improve the performance of spectral quantization of speech coders. The proposed techniques compensate for the distortion in a decoded line spectrum pairs (LSP) vector based on a statistical mapping function between a decoded LSP vector and its corresponding original LSP vector. We first develop two codebook-based probabilistic matching (CBPM) methods based on linear mapping functions according to different assumption of distribution of LSP vectors. In addition, we propose an iterative procedure for the two CBPMs. We apply the proposed techniques to a predictive vector quantizer used for the IS-641 speech coder. The experimental results show that the proposed techniques reduce average spectral distortion by around 0.064dB.

• Journal of Korean Traditional Oncology
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• v.27 no.1
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• pp.25-36
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• 2022

The Randomized Control Trial (RCT) is the most well-established and widely used statistical methodology in clinical research; however, applying thorough RCT to cancer patients presents challenges such as ethical concerns, high costs, short clinical periods, and limitations in collecting various side effects. To address this issue, the propensity score matching method, which takes advantage of the benefits of observational research while compensating for the drawbacks of randomized control trials, is used in a variety of fields. In recent years, 28 studies on the effectiveness of Korean medicine on tumors have been conducted abroad using the Propensity Score Matching Method, but none have been conducted in Korea. The majority of studies have focused on liver cancer, colon cancer, lung cancer, and stomach cancer, with endpoints such as survival time, incidence rate, quality of life, and treatment outcomes revealing statistical differences in how Korean medicine intervention affects treatment outcomes. As a result, well-established studies using the propensity matching score methodology should be useful in evaluating the impact of Korean medicine in oncology treatments.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.35-45
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• 2005

Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.643-651
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• 2017

Steganography is one of information hiding technologies and discriminated from cryptography in that it focuses on avoiding the existence the hidden information from being detected by third parties, rather than protecting it from being decoded. In this paper, as an image steganography method which uses images as media, we propose a new block matching method that embeds information into the discrete wavelet transform (DWT) domain. The proposed method, based on a statistical analysis, reduces loss of embedding capacity due to inequable use of candidate blocks. It works in such a way that computes the variance of each candidate block, preserves candidate blocks with high frequency components while reducing candidate blocks with low frequency components by compressing them exploiting the k-means clustering algorithm. Compared with the previous block matching method, the proposed method can reconstruct secret images with similar PSNRs while embedding higher-capacity information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.89-90
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• 2011

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.675-685
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• 2011

This paper proposes a job matching function that calculates the job matching probability of a job-seeker to an employer taking the working conditions of a job-seeker and an employer into account. In addition, this study analysis the degree of centrality that means interactions of a job-seeker and an employer utilizing social network analysis. The results are follows. First, a degree of centrality is found to be severely concentrated in certain job-seekers or certain employers; in addition, there are many job-seekers and employers who have no matching results. Second, according to decision tree analysis, characteristics of a job-seeker that influences the degree of centrality are gender, age and degree of education in order of importance. The characteristics of a employer that influences the degree of centrality are proposed salary, industry classification and firm size in order of importance.