• Journal of Internet Computing and Services
• /
• v.9 no.1
• /
• pp.129-135
• /
• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.5
• /
• pp.1766-1784
• /
• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06c
• /
• pp.106-108
• /
• 2006

본 논문은 HWP, DOC와 같은 전자 문서에서 사용자가 제공한 구조적인 규칙과 XML 기반 전자 문서 변환 기법을 이용함으로써, 사용자의 관심 영역에 해당하는 다양한 형태(표, 리스트 등)의 정보를 효과적으로 추출(변환)하여 저장하기 위한 방법에 관한 것이다. 본 논문에서 제시한 시스템은 3가지의 중요한 요소들로 구성되어 있는데, 1)전자문서의 원시 XML 문서로의 변환 방법 2)XML 기반 구조적인 규칙과 작성된 규칙을 이용하여 원시 XML 문서에서 정보를 추출(변환)하는 방법, 3)추출 된 정보에서 최종 XML을 생성하거나 DB에 저장하는 방법이 그것이다. 전자문서의 변환을 위해서 독립적으로 동작하는OCX 기반의 전자문서 변환 데몬(Daemon)을 개발하였고, 사용자의 정보 추출(변환)과정을 돕기 위해서 XSLT를 확장한 형태의 스크립트 언어를 개발하였다. 스크립트 언어는 비교적 간단한 문법 구조를 가지고 있고, 데이터 처리를 위한 자체 정의 함수와 변수를 사용한다. 추출된 정보는 원하는 형태의 데이터 포멧으로 생성하거나 DB에 저장할 수 있다. 본 시스템은 전자 문서 원문 정보에 대한 데이터베이스 구축 및 서비스의 제공, 혹은 구축된 데이터베이스를 이용하여 다양한 현황 통계를 제공하는 분야에서 유용하게 사용할 수 있다. 실제로 연구과제관리시스템과 성과정보시스템에 적용하여 그 성과를 입증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.200-205
• /
• 2005

The objectives of this study were to introduce the easiest and most proper applications of datamining in industrial processes. Applying datamining in manufacturing is very different from applying it in marketing. Misapplication of datamining in manufacturing system results in significant problems. Therefore, it is very important to determine the best procedure and technique in advance. In previous studies, related literature has been introduced, but there has not been much description of datamining applications. Research has not often referred to descriptions of particular examples dealing with application problems in manufacturing. In this study, a datamining roadmap was proposed to support datamining applications for industrial processes. The roadmap was classified into three stages, and each stage was categorized into reasonable classes according to the datamining purposed. Each category includes representative techniques for datamining that have been broadly applied over decades. Those techniques differ according to developers and application purposes; however, in this paper, exemplary methods are described. Based on the datamining roadmap, nonexperts can determine procedures and techniques for datamining in their applications.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.2
• /
• pp.174-182
• /
• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

• Journal of Electrical Engineering and information Science
• /
• v.2 no.5
• /
• pp.17-26
• /
• 1997

Interconnect characterization on a wafer level was performed. Test patterns for single, two-coupled, and triple-coupled lines ere designed by using 0.5$\mu\textrm{m}$ CMOS process. Then interconnect capacitances and resistances were experimentally extracted by using tow port network measurements, Particularly to eliminate parasitic effects, the Y-parameter de-embedding was performed with specially designed de-embedding patterns. Also, for the purpose of comparisons, capacitance matrices were calculated by using the existing CAD model and field-solver-based commercial simulator, METAL and MEDICI. This work experimentally verifies that existing CAD models or parameter extraction may have large deviation from real values. The signal transient simulation with the experimental data and other methodologies such as field-solver-based simulation and existing model was performed. as expected, the significantly affect on the signal delay and crosstalk. The signal delay due to interconnects dominates the sub-micron-based a gate delay (e.g., inverter). Particularly, coupling capacitance deviation is so large (about more than 45% in the worst case) that signal integrity cannot e guaranteed with the existing methodologies. The characterization methodologies of this paper can be very usefully employed for the signal integrity verification or he electrical design rule establishments of IC interconnects in the industry.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.1567-1570
• /
• 2012

기존 연구에서는 순차적, 상태, 엑티브 다이어그램 기반의 테스트케이스 추출을 초점을 두고 있다. 하지만 현재 최신의 모델링 언어인 UML2.4.1(Unified Modeling Language) 기반으로 한 테스트케이스 추출 메커니즘은 없다. 그래서 본 논문은 UML2.4.1 기반에 기존의 원인-결과 다이어그램의 접목을 통해 테스트케이스 추출 메커니즘을 제안 한다. 이를 위해 UML2.4.1 의 메시지-순차적 다이어그램에 ECA Rule(Event Condition Action)기법을 적용하고, 제안한 접목 알고리즘을 통해 확장된 메시지-순차적 다이어그램을 원인-결과 다이어그램과 접목한 후, 결정 테이블화로 테스트케이스를 발생한다. 이러한 절차를 통해 모델링 기반에서 테스트케이스 추출 가이드가 제공된다. 본 논문에서는 복잡한 메시지-순차적 다이어그램을 통해 테스트케이스 발생 사례연구로서 자동차 와이퍼 시스템을 적용한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1284-1297
• /
• 2019

The network environment is presently becoming very increased. Accordingly, the study of traffic classification for network management is becoming difficult. Automatic signature extraction system is a hot topic in the field of traffic classification research. However, existing automatic payload signature generation systems suffer problems such as semi-automatic system, generating of disposable signatures, generating of false-positive signatures and signatures are not kept up to date. Therefore, we provide a fully automatic signature update system that automatically performs all the processes, such as traffic collection, signature generation, signature management and signature verification. The step of traffic collection automatically collects ground-truth traffic through the traffic measurement agent (TMA) and traffic management server (TMS). The step of signature management removes unnecessary signatures. The step of signature generation generates new signatures. Finally, the step of signature verification removes the false-positive signatures. The proposed system can solve the problems of existing systems. The result of this system to a campus network showed that, in the case of four applications, high recall values and low false-positive rates can be maintained.

• Journal of the Korean Wood Science and Technology
• /
• v.13 no.3
• /
• pp.49-53
• /
• 1985

The effects of pre-treatments, the hot water extraction of wood meal and the addition of chemical ($CaCl_2$) to wood-cement water system on the properties of wood-cement composite such as modulus of rupture (MOR), modulus of elasticity (MOE), water sorption ratio and swelling ratio of resulting boards were studied in this experiment. The wood meals through 0.83mm(20 mesh) and retained on 0.42mm(35 mesh) screen were prepared from Pinus densiflora S. at Z. and Larix leptolepsis G. For hot water extraction, 500 grams of wood meal for each species were heated to boiling with 1,500ml of distilled water in 2-liter beaker for 6 hours. Every 2 hours, the wood meals were washed with boiling distil1ed water and reheated to boiling again. After 6 hours boiling, the boiled wood particles were collected by pouring this particles on 200 mesh screen. The collected particles then washed twice with hot distilled water and dried for 24 hours in an oven at $109{\pm}20^{\circ}C$. A mixture of 663.4 grams of cement with 331.7 grams of wood meal based on oven-dry weight were dry-mixed in a plastic vessel. The mixture was kneaded with 497.6ml of distilled water in the ratio of 1.5ml of water to a gram of wood meal. To add calcium chloride to the mixture as an accelerator, $CaCl_2$ 4% solution by weight per volume, was added to pine-or larch-cement board in the ratio of 3% to cement weight. To set wood-cement board, this mixture was clamped at 30cm ${\times}$ 30cm, in thickness of 1.5cm for 3 days at room temperature, declamped and then placed at open condition for 17 days. The target density was 1.0. The four specimens sized to 5cm in width and 28cm in length were used for MOR and MOE test for each treatment. After MOR test, the tested specimens were cut to the size of 5cm ${\times}$ 5cm for water sorption and swelling test. The twenty specimens used to measure the water sorption ratio (soaking 24 hours) and ten of these were used for swelling ratio measurement The results obtained were as follows: 1) Larch was not suitable for wood-cement boards because larch-cement board developed no strength, but pine showed 97.9kg/$cm^2$ by hot water extraction. 2) To increase MOR, hot water extraction was more effective than the addition of $CaCl_2$ in pine and larch because the $CaCl_2$ addition was seemed to speed up the ratio of cement hydration without reacting with the wood substances. 3) The water sorption ratio was lowered by the addition of $CaCl_2$ to wood-cement system because the chemical additive accelerated the rate of cement hydration. 4) In pine-cement board, the swelling ratio from 0.37 to 0.42 percent was observed in length and the swelling ratio from 0.88 to 2.0 percent in thickness. As a rule, the swelling ratio of wood-cement board was very low and the swelling ratio in thickness was higher than in length.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.