• Journal of the Korean Society for information Management
• /
• v.24 no.1 s.63
• /
• pp.103-120
• /
• 2007

Node is the semantic unit and a part of structured document. Information retrieval from structured documents offers an opportunity to go subdivided below the document level in search of relevant information, making any element in an structured document a retrievable unit. The node-based document retrieval constitutes several similarity calculating methods and the extended node retrieval method using structure information. Retrieval performance is hardly influenced by the methods for determining document similarity The extended node method outperformed the others as a whole.

• Proceedings of the KIEE Conference
• /
• 2005.07d
• /
• pp.2894-2896
• /
• 2005

본 논문에서는 퍼지모델을 기반으로 한 형판 벡터를 이용하여 인간의 감성을 인식할 수 있는 방법을 제안한다. 먼저 형판을 이용하여 얼굴 영상으로부터 얼굴의 특징(눈썹, 눈, 입)들을 추출한다. 추출한 형판으로부터 형판 벡터를 추출하고 이를 퍼지모델에 적용한다. 그 다음 감정에 따라 변하는 각각의 상태정보 이용하여 인간의 감성(놀람, 화남, 행복함, 슬픔)을 인식하는 방법을 제안한다. 마지막으로, 제안된 방법은 실험을 통해 그 응용 가능성을 확인한다.

• Proceedings of the KIEE Conference
• /
• 2006.07b
• /
• pp.1022-1023
• /
• 2006

본 논문에서는 MATLAB/SIMULINK와 dSPACE DS1104보드를 이용하여 유도전동기의 간접백터제어 알고리즘을 구현하였다. 유도전동기의 간접벡터제어를 위한 전체 시스템 모델은 SIMULINK 그래픽 모델과 dSPACE DS1104 R&D Control 보드의 Real Time Interface(RTI)를 이용하여 구현한 후에 실험을 수행하였다. 벡터제어를 위해서 전동기 전류의 측정은 16kHz로 측정하였고, 측정된 전류를 이용하여 8Hz로 전류제어기, 4kHz로 속도제어기를 구현하였다. MATLAB과 dSPACE 보드의 컴파일러가 설계된 그래픽 모델의 C source 파일과 object 파일을 자동으로 생성시켜주기 때문에 추가적인 프로그램의 coding 없이 실제 시스템의 시뮬레이션과 실험을 동시에 수행할 수 있으며 원하는 제어성능을 얻을 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.363-365
• /
• 2023

본 논문에서는 어텐션(Attention) 메커니즘을 이미지 처리에 적용한 연구가 진행되면서 등장한 비전 트랜스포머 (Vision Transformer, ViT)의 한계를 극복하기 위해 ViT 기반의 딥 클러스터링(Deep Clustering) 기법을 제안한다. ViT는 완전히 트랜스포머(Transformer)만을 사용하여 입력 이미지의 패치(patch)들을 벡터로 변환하여 학습하는 모델로, 합성곱 신경망(Convolutional Neural Network, CNN)을 사용하지 않으므로 입력 이미지의 크기에 대한 제한이 없으며 높은 성능을 보인다. 그러나 작은 데이터셋에서는 학습이 어렵다는 단점이 있다. 제안하는 딥 클러스터링 기법은 처음에는 입력 이미지를 임베딩 모델에 통과시켜 임베딩 벡터를 추출하여 클러스터링을 수행한 뒤, 클러스터링 결과를 임베딩 벡터에 반영하도록 업데이트하여 클러스터링을 개선하고, 이를 반복하는 방식이다. 이를 통해 ViT 모델의 일반적인 패턴 파악 능력을 개선하고 더욱 정확한 클러스터링 결과를 얻을 수 있다는 것을 실험을 통해 확인하였다.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.167-170
• /
• 2003

본 논문에서는 패턴 인식의 전형적인 경우인 보이기 기반 물체 인식(Appearance based object recognition)을 수행하기 위하여, 일반적인 퍼지 분류 모델과, 서포트 벡터 머신을 하이브리드(hybrid) 하게 연결한 서포트 벡터 기반 퍼지 분류 시스템이라는 새로운 방법을 제안하고 이에 대하여 연구한다. 일반적인 분류(classification)문제의 경우 두 클래스로 구분하는데 최적의 성능을 가지고 있는 서포트 벡터 머신이 다중클래스(Multiclass)의 경우 발생 하는 계산량의 증가 문제를 해 결하기 위하여 다중 클래스 분류(Multiclass classification)에 장점을 가진 퍼지 분류 시스템을 도입, 서포트 벡터 머신에 연결함으로써 단점을 보완하는 시스템을 제안한다. 즉 서포트 벡터 머신을 통해 퍼지 시스템의 구조를 러닝(learning)하는데 사용하여 최종 적으로는 퍼지 분류 시스템(Fuzzy Classifier)이 나오도록 하는 것이다. 이 시스템의 성능을 확인하고자 여러 가지 물체들에 대한 이미지를 가지고 있는 COIL(Columbia Object Image Library) 데이터 베이스를 사용하여 보이기 기반 물체 인식(Appearance based Object Recognition)을 수행 하였으며 이를 순수한 서포트 벡터 머신만을 이용하여 물체 인식을 수행한 경우와 정확도 및 인식 시간에 대하여 비교하였다.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.2
• /
• pp.44-58
• /
• 2014

In this study, a standard vector data model was developed for interoperability of river-geospatial information and for verification purpose the applicability of the standard vector model was evaluated using a model to RIMGIS vector data at Changnyeong-Hapcheon & Gangjung-Goryeong irrigation watershed. The standards from ISO and OGC were analyzed and the river geospatial data model standard was established by applying the standards. The ERD was designed based on the analysis information on data characteristics and relationship. The verification of RIMGIS vector data included points, lines and polygon to develope GDM was carried out by comparing with the data by layer. This conducting comparison of basic spatial data and attribute data to each record and spatial information vertex. The error in the process of conversion was 0 %, indicating no problem with model. Our Geospatial Data Model presented in this study provides a new and consistent format for the storage and retrieval of river geospatial data from connected database. It is designed to facilitators integrated analysis of large data sets collected by multiple institutes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.2
• /
• pp.247-254
• /
• 2010

In this paper, a new approach has been studied correcting the geometric distortion of SPOT 4 imagery. Two new equations were induced by the relationship between satellite and the Earth in the space. line-of-sight (LOS) vector adjustment model for SPOT 4 imagery was implemented in this study. This model is to adjust LOS vector under the assumption that the orbital information of satellite provided by receiving station is uncertain and this uncertainty makes a constant error over the image. This model is verified using SPOT 4 satellite image with high look angle and thirty five ground points, which include 10 GCPs(Ground Control Points) and 25 check points, measured by the GPS. In total thirty five points, the geometry of satellite image calculated by given satellite information(such as satellite position, velocity, attitude and look angles, etc) from SPOT 4 satellite image was distorted with a constant error. Through out the study, it was confirmed that the LOS vector adjustment model was able to be applied to SPOT4 satellite image. Using this model, RMSEs (Root Mean Square Errors) of twenty five check points taken by increasing the number of GCPs from two to ten were less than one pixel. As a result, LOS vector adjustment model could efficiently correct the geometry of SPOT4 images with only two GCPs. This method also is expected to get good results for the different satellite images that are similar to the geometry of SPOT images.

• The Journal of the Korea Contents Association
• /
• v.7 no.2
• /
• pp.11-20
• /
• 2007

AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In this paper, we propose a face alignment method using progressive AAM. The proposed method consists of two stages; modelling and relation derivation stage and fitting stage. Modelling and relation derivation stage first builds two AAM models; the inner face AAM model and the whole face AAM model and then derive the relation matrix between the inner face AAM model parameter vector and the whole face AAM model parameter vector. The fitting stage is processed progressively in two phases. In the first phase, the proposed method finds the feature parameters for the inner facial feature points of a new face, and then in the second phase it localizes the whole facial feature points of the new face using the initial values estimated utilizing the inner feature parameters obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment method is more robust with respect to pose, and face background than the conventional basic AAM-based face alignment.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.9
• /
• pp.125-139
• /
• 2014

In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.5
• /
• pp.187-197
• /
• 2020

Recently, various methods of text embedding using deep learning algorithms have been proposed. Especially, the way of using pre-trained language model which uses tremendous amount of text data in training is mainly applied for embedding new text data. However, traditional pre-trained language model has some limitations that it is hard to understand unique context of new text data when the text has too many tokens. In this paper, we propose self-supervised learning-based fine tuning method for pre-trained language model to infer vectors of long-text. Also, we applied our method to news articles and classified them into categories and compared classification accuracy with traditional models. As a result, it was confirmed that the vector generated by the proposed model more accurately expresses the inherent characteristics of the document than the vectors generated by the traditional models.