• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.678-689
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• 2000

This paper describes an automatic summarization approach that constructs a summary by extracting sentences that are likely to represent the main theme of a document. As a way of selecting summary sentences, the system uses a model that takes into account lexical and statistical information obtained from a document corpus. As such, the system consists of two parts: the training part and the summarization part. The former processes sentences that have been manually tagged for summary sentences and extracts necessary statistical information of various kinds, and the latter uses the information to calculate the likelihood that a given sentence is to be included in the summary. There are at least three unique aspects of this research. First of all, the system uses a text component identification model to categorize sentences into one of the text components. This allows us to eliminate parts of text that are not likely to contain summary sentences. Second, although our statistically-based model stems from an existing one developed for English texts, it applies the framework to individual features separately and computes the final score for each sentence by combining the pieces of evidence using the Dempster-Shafer combination rule. Third, not only were new features introduced but also all the features were tested for their effectiveness in the summarization framework.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.25-35
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• 2013

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. The experiments show that the feature reduction methods suggested in this paper have better performance than that of the conventional dimension reduction methods, R-Square and PCA. As performance improvement by feature reduction only is subject to limit, modular neural network is used as another method to improve the performance. The experiments show that the method also improves performance effectively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.73-82
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• 2015

In evaluating lateral behavior on the seismic and wind load, the purpose of sensitivity analysis is to find critical variables and to identify characteristic response with variability of variables. The sensitivity analysis is very important in structural diagnosis, repair and reinforcement field. This study investigates the sensitivity by linear static analysis applying the TDA method in changing angles of diagrid braces on the same height structures. In case of mid rise model, under the seismic load, the brace member is determined as a major variable at $58^{\circ}$ but a high rise model, under the wind load, has the brace member as a major variable at $67.4^{\circ}$. In addition, location of critical sensitivity on the mid rise model is distributed over middle section, while it is distributed lower section on the high rise model.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.248-248
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• 2012

산사태나 토석류와 같은 산지재해가 빈발하고 인명과 재산의 피해가 증가함에 따라 적절한 대책이 시급하게 요구되고 있다. 이런 대책 중에서 신뢰도 높은 산지재해 예, 경보시스템을 구축하는 것은 매우 중요하다. 산림청에서는 산사태 예, 경보 발령을 위한 기준을 마련하고 있으나, 좀더 신뢰도 높은 기준을 필요로 한다고 생각된다. 본 연구에서는 강우분석을 통해서 우리나라의 자연사면에서 토석류, 산사태를 일으키는 강우의 특성을 파악하고, 나아가 산지토사재해 예, 경보시스템에 적절하게 활용될 수 있는 기준을 마련하고자 하였다. 이를 위해서 회귀분석, 판별분석을 적용하여 평가하였고, 보다 개선된 기준으로서 토양우량지수를 제시하였다. 토양우량지수는 강우에 의해 지반이 어느 정도 포화되어 있는가를 계산하여, 토사재해발생의 위험성을 나타낸 것이다. 본 연구에서는 2001년에서 2009년 사이에 충북 제천시 일대의 강우자료를 조사하여 탱크모델에 적용하여 각 탱크에서의 저류량을 계산하여 토양우량지수를 결정하였다. 세 개의 탱크 중에서 두 번째 탱크에서의 저류량 (S2)과 전체 탱크에서의 저류량 (TS)을 이용하여 상위에 랭크된 이력순위를 분석한 결과, S2에서는 산사태가 발생한 2009년 이력이 3번째 높은 수준으로 기록되며, 산사태 미발생의 2007년 강우는 5번째로 기록되었다. 그리고 TS의 경우 2009년 강우가 2002년에 이어 3번째 높은 수준으로 기록되었으며, 2007년 강우는 9번째로 기록되었다. 이러한 결과를 볼 때 토양우량지수의 이력순위는 산지토사재해의 발생을 잘 반영하는 것으로 나타났다. 또한 2011년 발생한 우면산 산사태를 대상으로 토양우량지수를 적용하여 예, 경보시스템의 적용가능성을 판단하였다.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.815-826
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• 2000

This study is for the segmentation and volume calculation of the white matter and gray matter from brain MRI. In general, the volume of white and gray matter is reduced by contraction of each components in the case of mental retardation which are Alzheimer's disease and Down's syndrome. As results, it is useful for diagnostic and early detection for various mental retardation through the tracing of variation for its volume from the brain MRI. But, until now, it was very difficult to calculate the partial volume of each components existing in some thickness, because MR image was represented by single gray value after scanning by MR scanner. Accordingly, new segmentation algorithm proposed in this paper is to calculate the partial volume of the white and gray matter existing in some thickness through the analysis of the blurred gray value, and is to determine the threshold for segmentation of white and gray matter, and is to calculate the volume of each segmented component. And finally, proposed algorithm was applied the models which was created manually, and then acquired results was compared with that of original model.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.525-532
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• 2006

This paper proposes a new class of image restoration on the Ising modeled binary 'Takbon' image by the flexible line-fitting ICM(Iterated conditional modes) method. Basically 'Takbon' image need be divided into two extreme regions, information and background one due to its stroke combinations. The main idea is the line process, comparing with the conventional ICM approaches which were based on partially rectangular structured point process. For calculating geometrical mechanism, we have defined line-fitting functions at each current pixel array which form the set of linear lines with gradients and lengths. By applying the Bayes' decision to this set, the region of the current pixel is decided as one of the binary levels. In this case, their statistical reiteration for distinct tracking between intra and extra region offers a criterion to decide the attachment at each step. Finally simulations using the binary 'Takbon' image are provided to demonstrate the effectiveness of our new algorithm

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.293-296
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• 2021

K-water에서는 다목적댐의 관리를 위해 실시간으로 댐수위, 하천 수위 및 강우량 등을 계측하고 있으며, 계측된 값들은 댐을 효과적으로 운영하는데 필요한 데이터로 활용되고 있다. 특히 댐수위 이상 데이터를 탐지하지 못한 채 그대로 사용할 경우 댐의 방류 시기와 방류량 등을 결정하는 중요한 의사결정을 그르칠 수 있으므로 이를 신속히 탐지하는 것이 매우 중요하다. 현재의 자동화된 이상 데이터 탐지방법 중 하나는 현재 데이터가 최댓값과 최솟값을 초과할 때, 다른 하나는 현재 데이터와 일정 시간 동안의 평균값 간의 차이가 관리자가 정한 특정 값을 벗어났을 때를 기준으로 삼고 있다. 전자는 상한과 하한의 초과 여부만 판단하므로 탐지가 쉬우나 정상범위 내에서 발생한 이상 데이터는 탐지가 불가하다. 후자는 관리자의 경험을 통해 판단 조건을 정하기 때문에 객관성이 결여되는 문제가 있다. 특히 방류와 강우가 복합적으로 댐수위에 영향을 미치는 홍수기에 관리자의 경험에 기초한 이상 데이터 판별은 신뢰성의 문제가 있을 수 있다. 따라서 본 연구에서는 기계학습을 최초로 적용하여 이상 데이터를 탐지하고자 하였다. 댐수위, 누적강우량 및 누적방류량 데이터와 댐수위데이터를 가공하여 생성한 댐수위차, 댐수위차평균, 댐수위평균 등 자질들의 다양한 조합을 만든 후 이를 Random Forest, SVM, AdaptiveBoost 및 다층퍼셉트론(MLP) 등과 같은 여러 가지 기계학습모델 등을 통해 이상 데이터를 판별하는 실험(분류)을 하였다. 실험결과 댐수위, 댐수위차, 댐수위-댐수위평균, 누적강우량, 누적방류량 및 댐수위차평균을 사용하였을 때 MLP에서 가장 우수한 성능을 보였다. 이 연구를 통해서 댐수위 이상 데이터를 기계학습의 분류기능을 통해 효과적으로 탐지할 수 있다는 것과 모델의 성능은 실험에 사용한 자질의 수뿐 아니라 자질의 종류에도 큰 영향을 받는다는 것을 알 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.41-46
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• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.