• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.38-39
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• 2007

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we take advantage of the characteristics of optical burst switching (OBS) to support service-differentiation in optical networks. With the offset time between control packet and burst data, the proposed scheme uses different offset time of each service class. As contrasted with the Previous method, in which the high Priority service use only long offset time, it derives the burst loss rate as a QoS parameter in consideration of conservation law and given service-differential ratios and decides a reasonable offset time for this QoS finally Firstly proposed method classifies services into one of high or low class and is an algorithm deciding the offset time for supporting the required QoS of high class. In order to consider the multi-classes environment, we expand the analysis method of first algorithm and propose the second algorithm. It divides services into one of high or low group according to their burst loss rate and decides the offset time for high group, and lastly cumulates the offset time of each class. The proposed algorithms are evaluated through simulation. The result of simulation is compared with that of analysis to verify the proposed scheme.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.220-222
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• 1998

유선 통신과 다르게 무선 통신은 자원의 한정성이란 매체의 특성을 가지고 있어 다중의 사용자가 다른 QoS를 요구하는 멀티미디어 서비스를 지원하기 위해서는 각각의 서비스에 따른 트래픽에 차별화를 두어 매체 접속을 제어하는 것이 효과적인 방법이 될 것이다. 현재, 음성과 데이터를 통합하여 매체 접속 제어를 하는 패킷 예약 다중 접속 방식은 많은 연구가 이루어진 상태이고, 특히, 멀티미디어 지원을 위해 적응형 패킷 예약 다중 접속(APRMA: Adaptive Packet Peservation Multiple Access)방식의 연구가 진행되었다. 본 논문에서는 멀티미디어 지원을 위한 다른 한가지 방법으로 패킷이 경쟁에 참여할 수 있는 파라미터를 서비스의 종류와 활성중인 슬롯의 수에 따라 조정하여 채널의 효율을 보다 향상시키고 패킷 충돌이 일어날 확률도 감소 시킬 수 있는 우선 순위 기반 적응형 패킷 예약 매체 접속 방법 (APRMA_P : APRMA with Prioroty)을 제시한다. 제안된 APRMA_P의 성능을 분석하기 위해 시뮬레이션을 통해서 체널 효율을 기존의 APRMA와 비교 분석한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.883-885
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• 2005

지지 벡터 기계(Support Vector Machine: SVM)를 이용한 다중부류 분류기법이 최근 활발히 연구되고 있다. SVM은 이진분류기이기 때문에 다중부류 분류를 위해서 다수의 분류기를 구성하고 이들을 효과적으로 결합하는 방법이 필요하다. 본 논문에서는 기존의 정적인 다중분류기 결합 방법과는 달리 포섭구조의 분류모델을 확률에 따라 동적으로 구성하는 방법을 제안한다. 확률적 분류기인 나이브 베이즈 분류기(NB)를 이용하여 입력된 샘플의 각 클래스에 대한 확률을 계산하고, OVA (One-Vs-All) 전략으로 구축된 다중의 SVM을 획득된 확률에 따라 포섭구조로 구성한다. 제안하는 방법은 OVA SVM에서 발생하는 중의적인 상황을 효과적으로 처리하여 고성능의 분류를 수행한다. 본 논문에서는 지문분류 문제에서 대표적인 NIST-4 지문 데이터베이스를 대상으로 제안하는 방법을 적용하여 $1.8\%$의 거부율에서 $90.8\%$의 분류율을 획득하였으며, 기존의 결합 방법인 다수결 투표(Majority vote), 승자독식(Winner-takes-all), 행동지식공간 (Behavior knowledge space), 결정템플릿(Decision template) 등보다 높은 성능을 확인하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.171-177
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• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.563-568
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• 2016

The word sense disambiguation problem is to find the correct sense of an ambiguous word having multiple senses in a dictionary in a sentence. We regard this problem as a multi-class classification problem and classify the ambiguous word by using Support Vector Machines. Context words of the ambiguous word, which are extracted from Sejong sense tagged corpus, are represented to two kinds of vector space. One vector space is composed of context words vectors having binary weights. The other vector space has vectors where the context words are mapped by word embedding model. After experiments, we acquired accuracy of 87.0% with context word vectors and 86.0% with word embedding model.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.383-392
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• 2006

In this paper, we propose a new relevance feedback algorithm using Probabilistic Neural Networks(PNN) while supporting multi-class learning. Then, to validate the effectiveness of our feedback approach, we incorporate the proposed algorithm into our region-based image retrieval tool, FRIP(Finding Regions In the Pictures). In our feedback approach, there is no need to assume that feature vectors are independent, and as well as it allows the system to insert additional classes for detail classification. In addition, it does not have a long computation time for training because it only has four layers. In the PNN classification process, we store the user's entire past feedback actions as a history in order to improve performance for future iterations. By using a history, our approach can capture the user's subjective intension more precisely and prevent retrieval performance errors which originate from fluctuating or degrading in the next iteration. The efficacy of our method is validated using a set of 3000 images derived from a Corel-photo CD.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.