• Journal of KIISE
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• v.43 no.11
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• pp.1270-1274
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• 2016

Logistic regression is widely used for predicting and estimating the relationship among variables. We propose a new logistic regression model, the value weighted logistic regression, which comprises of a fine-grained weighting method, and assigns adapted weights to each feature value. This gradient approach obtains the optimal weights of feature values. Experiments were conducted on several data sets from the UCI machine learning repository, and the results revealed that the proposed method achieves meaningful improvement in the prediction accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.154-156
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• 2002

정보화시대에 들어서면서 나날이 급증하는 데이터에 대한 재가용성을 위한 많은 연구가 이루어지고 있다 이러한 연구들은 의사결정지원, 예측, 추정 등의 분야에서 적용되고 있으나, 실생활에 활발히 적용되기까지 앞으로 많은 연구 및 개발이 요구된다. 이 논문에서는 수집된 데이터로부터 패턴을 추출하여 예측결과를 제공할 수 있는 시스템 모델과 모델에 적합한 점진적 규칙갱신 알고리즘을 제안하였다. 제안하는 예측 모델의 특징은 새로 입력되는 정보에 대한 반복 학습시 수치데이터에 대한 평균근사치 할당방법을 적용하여 규칙갱신을 용이하게 하였으며 각 클래스의 수치데이터에 대한 분류를 용이하도록 하였다.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.373-379
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• 2015

In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.498-509
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• 2002

As WWW grows at an increasing speed, a classifier targeted at hypertext has become in high demand. While document categorization il quite mature, the issue of utilizing hypertext structure and hyperlinks has been relatively unexplored. In this paper, we propose a practical method for enhancing both the speed and the quality of hypertext categorization using hyerlinks. In comparison against a recently proposed technique that appears to be the only one of the kind, we obtained up to 18.5% of improvement in effectiveness while reducing the processing time dramatically. We attempt to explain through experiments what factors contribute to tile improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.441-442
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• 2021

본 논문에서는 고해상도의 시계열 위성영상을 딥러닝 알고리즘으로 학습하여 도시 변화탐지를 수행한다. 고해상도 위성영상을 활용한 서비스는 4 차 산업혁명 융합 신사업 중 하나인 스마트시티에 적용하여 도시 노후화, 교통 혼잡, 범죄 등 다양한 도시 문제 해결 및 효율적인 도시를 구축하는데 활용이 가능하다. 이에 본 연구에서는 도시 변화탐지를 위한 딥러닝 알고리즘으로 DeepLabV3+를 사용한다. 이는 인코더-디코더 구조로, 공간 정보를 점진적으로 회복함으로써 더욱 정확한 물체의 경계면을 찾을 수 있다. 제안하는 방법은 DeepLabV3+의 레이어와 loss function 을 수정하여 기존보다 좋은 결과를 얻었다. 객관적인 성능평가를 위해, 공개된 데이터셋 LEVIR-CD 으로 학습한 결과로 평균 IoU 는 0.87, 평균 Dice 는 0.93 을 얻었다.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.108-116
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• 2019

In this paper, we propose a method to apply artificial intelligence technology efficiently to integrated security control technology. In other words, by applying machine learning learning to artificial intelligence based on big data collected in integrated security control system, cyber attacks are detected and appropriately responded. As technology develops, many large capacity Is limited to analyzing individual logs. The analysis method should also be applied to the integrated security control more quickly because it needs to correlate the logs of various heterogeneous security devices rather than one log. We have newly proposed an integrated security service model based on artificial intelligence, which analyzes and responds to these behaviors gradually evolves and matures through effective learning methods. We sought a solution to the key problems expected in the proposed model. And we developed a learning method based on normal behavior based learning model to strengthen the response ability against unidentified abnormal behavior threat. In addition, future research directions for security management that can efficiently support analysis and correspondence of security personnel through proposed security service model are suggested.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.361-368
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• 2004

The purpose of this study is to propose a new on-line nonlinear PCA(OL-NPCA) method for a nonlinear feature extraction from the incremental data. Kernel PCA(KPCA) is widely used for nonlinear feature extraction, however, it has been pointed out that KPCA has the following problems. First, applying KPCA to N patterns requires storing and finding the eigenvectors of a N${\times}$N kernel matrix, which is infeasible for a large number of data N. Second problem is that in order to update the eigenvectors with an another data, the whole eigenspace should be recomputed. OL-NPCA overcomes these problems by incremental eigenspace update method with a feature mapping function. According to the experimental results, which comes from applying OL-NPCA to a toy and a large data problem, OL-NPCA shows following advantages. First, OL-NPCA is more efficient in memory requirement than KPCA. Second advantage is that OL-NPCA is comparable in performance to KPCA. Furthermore, performance of OL-NPCA can be easily improved by re-learning the data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.786-795
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• 2002

In this paper, we present a histogram and moment-based vidoe scencd change detection technique using hierarchical Hidden Markov Models(HMMs). The proposed method extracts histograms from a low-frequency subband and moments of edge components from high-frequency subbands of wavelet transformed images. Then each HMM is trained by using histogram difference and directional moment difference, respectively, extracted from manually labeled video. The video segmentation process consists of two steps. A histogram-based HMM is first used to segment the input video sequence into three categories: shot, cut, gradual scene changes. In the second stage, a moment-based HMM is used to further segment the gradual changes into a fade and a dissolve. The experimental results show that the proposed technique is more effective in partitioning video frames than the previous threshold-based methods.