• Proceedings of the Korean Information Science Society Conference
• /
• 2005.11b
• /
• pp.835-837
• /
• 2005

최근 얼굴 인식은 사용자의 편의성을 포함한 다양한 장점으로 인하여 생체 인식 시장에서 주요 기술로 대두되고 있다. 그러나 조명 변화에 기인한 얼굴 인식 성능의 저하는 실용화에 걸림돌이 되고 있는 실정이다. 따라서 조명 변화에 따른 얼굴의 외형 변화를 분석하는 연구들이 세계적으로 활발히 진행되고 있다. 그러나 기존 방법들은 다수의 등록 영상이나 조명에 대한 사전 정보가 필요하거나 실시간으로 구현되기 어렵기 때문에 실용 시스템에 적용하기는 어려운 실정이다. 따라서, 본 논문에서는, 여러 조명 영상들로 구성된 학습 데이터를 이용하여, 조명에 대한 정보가 없는 한 장의 입력 영상을 분석하는 방법을 제안한다. 제안된 방법은 SVDD를 이용하여 학습 데이터의 여러 조면 영상들로부터 입력 영상의 조명과 같은 대표영상을 합성하고 이 대표영상들의 선형 조합을 이용하여 입력 영상을 표현한다. 제안 방법의 효율성을 검증하기 위하여 공인 얼굴 데이터베이스들을 이용하여, 기존 방법들과 비교 실험을 수행하였으며, 조명 변화가 큰 영상에서도 안정된 조명 변화의 분석이 가능하였다.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1823-1824
• /
• 2007

핸드폰, PDA, Laptop이 보편화 되면서 이를 이용한 위치 인식 기술의 중요성이 높아지고 있다. 이러한 위치기반 서비스(LBS : Location Based Service)는 GPS를 이용한 실외 서비스와 WLAN, Zigbee, UWB 등을 이용한 실내 서비스로 나눌 수 있다. 본 논문에서는 이미 많은 수의 기반시설(AP : Access Point)가 구축되어 있는 무선랜 기반의 효과적인 위치 측정 기법에 관한 연구를 모색 해보고자 한다. 각 AP에서 받은 신호세기(SS : Signal Strength)를 데이터 베이스에 저장한 후, 이동단말기(MU : Mobile Unit)의 위치가 요구되는 장소에서 다시 신호세기를 측정하여 데이터 베이스와 비교하여 가장 적합한 위치 데이터 정보를 리턴하는 핑거프린트(Fingerprint) 방식을 소개한다. 그리고 불안정한 신호 세기 데이터를 판별하기 위하여 단일 클래스 SVM 기법인 SVDD(Support Vector Data Description)을 이용하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1228-1229
• /
• 2023

현대 도시 모빌리티의 중요한 구성 요소로 자리 잡은 전동킥보드는 편리한 이동 수단으로 인기를 얻고 있으나, 이에 따른 안전사고 증가로 운전자와 보행자의 안전이 심각하게 위협받고 있다. 본 논문에서는 전동킥보드 운전 중에 발생한 사고의 원인을 객관적으로 분석하고, 사고가 운전자의 부주의로 인한 것인지를 판별하며, 이로 인한 배상 책임을 정확하게 결정하기 위한 모델을 제안한다. 운전 중 수집된 센서 데이터를 활용하여 Deep SVDD (Deep Support Vector Data Description) 모델을 구축하고, 이상치 탐지를 통해 운전 패턴을 분류하며 운전자의 부주의로 인한 사고를 파악한다. 이를 통해, 정확하고 공정한 배상 책임 판단을 지원하며, 도시 모빌리티 분야에서 안전사고 감소에 기여할 것으로 기대된다.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.21 no.11
• /
• pp.997-1004
• /
• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• IE interfaces
• /
• v.25 no.2
• /
• pp.170-177
• /
• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.5
• /
• pp.21-28
• /
• 2022

All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

• Computers and Concrete
• /
• v.23 no.6
• /
• pp.445-457
• /
• 2019

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06a
• /
• pp.124-127
• /
• 2008

DoS/DDoS로 대표되는 트래픽 폭주 공격은 대상 시스템뿐만 아니라 네트워크 대역폭, 프로세서 처리능력, 시스템 자원 등에 악영향을 줌으로써 네트워크에 심각한 장애를 유발할 수 있다. 따라서 신속한 트래픽 폭주 공격의 탐지는 안정적인 서비스 제공 및 시스템 운영에 필수요건이다. 전통적인 패킷 수집을 통한 DoS/DDoS의 탐지방법은 공격에 대한 상세한 분석은 가능하나 설치의 확장성 부족, 고가의 고성능 분석시스템의 요구, 신속한 탐지를 보장하지 못한다는 문제점을 갖고 있다. 본 논문에서는 15초 단위의 SNMP MIB 객체 정보를 바탕으로 SVDD(support vector data description)를 이용하여 보다 빠르고 정확한 침입탐지와 쉬운 확장성, 저비용탐지 및 정확한 공격유형별 분류를 가능케 하는 새로운 시스템을 설계 및 구현하였다. 실험을 통하여 만족스러운 침입 탐지율과 안전한 false negative rate, 공격유형별 분류율 수치 등을 확인함으로써 제안된 시스템의 성능을 검증하였다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.3 no.1
• /
• pp.57-72
• /
• 2008

Serve and do so that graft together specialists' view application field of computer and developmental disability diagnosis estimation data to construct developmental disability diagnosis estimation system in this Paper and constructed developmental disability diagnosis estimation system. Developmental disability diagnosis estimation must supply information of specification area that specialists are having continuously. Developmental disability diagnosis estimation specialist system need multimedia data processing that is specialized little more for developmental disability classification diagnosis and decision-making and is atomized for this. Characteristic of developmental disability diagnosis estimation system that study in this paper can supply quick feedback about result, and can reduce mistake on recording and calculation as well as can shorten examination's enforcement time, and background of training is efficient system fairly in terms of nonprofessional who is not many can use easily. But, as well as when multimedia information that is essential data of system construction for developmental disability diagnosis estimation is having various kinds attribute and a person must achieve description about all developmental disability diagnosis estimation informations, great amount of work done is accompanied, technology about equal data can become different according to management. Because of these problems, applied search technology of contents base (Content-based) that search connection information by contents of edit target data for developmental disability diagnosis estimation data processing multimedia data processing technical development. In the meantime, typical access way for conversation style data processing to support fast image search, after draw special quality of data by N-dimension vector, store to database regarding this as value of N dimension and used data structure of Tree techniques to use index structure that search relevant data based on this costs. But, these are not coincided correctly in purpose of developmental disability diagnosis estimation because is developed focusing in application field that use data of low dimension such as original space DataBase or geography information system. Therefore, studied save structure and index mechanism of new way that support fast search to search bulky good physician data.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.1
• /
• pp.105-111
• /
• 2010

This paper proposes the algorithm which addresses the problem of detecting leak of defaced confidential documents from original confidential document. Generally, a confidential document is defaced into various forms by insiders and then they are trying to leak these defaced documents to outside. Traditional algorithms detecting leak of documents have low accuracy because they are based on similarity of two documents, which do not reflect various forms of defaced documents in detection. In order to overcome this problem, this paper proposes a novel v-SVDD algorithm which is based on SVDD, the novelty detection algorithm. The result of experiment shows that there is significant improvement m the accuracy of the v-SVDD in comparison with the traditional algorithms.