• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.225-228
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• 2005

본 논문에서는 생물의 2차원영상에서 4가지의 특징을 추출한 다음 약품에 대한 생물의 행동 패턴 반응에 대하여 의사결정나무를 적용하여 패턴의 인식 및 분류를 하였다. 생물의 행동패턴을 대변하는 물리적인 특징인 속도, 방향전환 각도, 이동거리에 대하여 각각 중간이상속도비율, FFT(Fast Fourier Transformation), 2차원 히스토그램 면적, 프렉탈, 무게중심을 사용하여 특징을 추출하였다. 이렇게 추출된 4가지의 특징변수들을 사용하여 의사결정나무 모델을 구성한 다음 생물의 약품 첨가에 대한 반응을 분석하였다. 또한 결과에서는 기존의 생물의 행동패턴 구분에 쓰였던 전형적인 기법(conventional methods)보다 본 연구에서 적용한 의사결정나무가 생물의 행동패턴이 가지는 물리적 요소에 대한 독해력을 가짐을 보임으로써 특정환경에서 이동행동에 대한 분석을 용이하게 하고자 하였다.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.148-153
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• 1989

본 논문에서는 광 상관기 (optical correlator) 에 의한 한글문자 분리 인식의 한 방법을 제안하였다. 효율적인 분리 인식을 위해서 병렬 나무 (tree) 구조로부터 입력 신호를 두 방향으로 병렬 처리하여 각 방향으로 자음과 모음을 따로 분리시켜 2단계 만에 인식한 뒤 이들을 위치에 따라 조합하여 문자 분리 인식하도록 하며, 아울러 이러한 병렬 나무구조의 각 단계에서 필터 합성시 MACE (minimum average correlation energy) 개념을 이용하여 광 상 관평면상에서 부엽의 문제를 줄이고, 실제 광 시스템에서 생길 수 있는 왜곡을 학습표본에 포함하여 광 상관기 시스템에 의한 실질적인 한글 문자의 왜곡에 무관한 분리인식을 하도록 하였다.

• Korean Journal of Plant Taxonomy
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• v.46 no.1
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• pp.13-32
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• 2016

Neillieae was traditionally recognized as a small tribe in Rosaceae, which consists of Neillia, Stephanandra, and Physocarpus. Recently, Stephanandra was merged into Neillia based on molecular phylogenetic analyses, meaning that Neillieae now contains Neillia and Physocarpus. The genus Neillia is distinguished from Physocarpus by ovate to lanceolate leaves with acuminate to caudate apices, racemose or paniculate inflorescences, and a unicarpellate (rarely bicarpellate) gynoecium. Plants of Neillia are distributed from the Himalayas across China and Korea to Japan in the east, and south to Indonesia. This study provides a taxonomic treatment of Neillia based on a morphological examination of herbarium specimens, including types, and field observations, as the second part of the taxonomic revision of the tribe Neillieae. A summary of the phylogeny of Neillia, keys to the species, nomenclatural reviews, descriptions, distribution maps, and lists of specimens examined are provided. Twelve species with ten varieties are recognized in Neillia. A lectotype was designated here for the following species: N. sinensis f. glanduligera and N. thyrsiflora.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.9-16
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• 2019

The goal of this study is to propose an efficient model for recognizing and classifying tree images to measure the accuracy that can be applied to smart devices during class. From the 2009 revised textbook to the 2015 revised textbook, the learning objective to the fourth-grade science textbook of elementary schools was added to the plant recognition utilizing smart devices. In this study, we compared the recognition rates of trees before and after retraining using a pre-trained inception V3 model, which is the support of the Google Inception V3. In terms of tree recognition, it can distinguish several features, including shapes, bark, leaves, flowers, and fruits that may lead to the recognition rate. Furthermore, if all the leaves of trees may fall during winter, it may challenge to identify the type of tree, as only the bark of the tree will remain some leaves. Therefore, the effective tree classification model is presented through the combination of the images by tree type and the method of combining the model for the accuracy of each tree type. I hope that this model will apply to smart devices used in educational settings.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.682-687
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• 2005

Behavioral sequences of the medaka(Oryzias latipes) were investigated through an image system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon(0.1 mg/1). After much observation, behavioral patterns could be divided into 4 patterns: active smooth, active shaking, inactive smooth, and inactive shaking. These patterns were analyzed by 5 features: speed ratio, x and y axes projection, FFT to angle transition, fractal dimension, and center of mass. Each pattern was classified using decision tree. It provide a natural way to incorporate prior knowledge from human experts in fish behavior, The main focus of this study was to determine whether the decision tree could be useful in interpreting and classifying behavior patterns of the animal.

• Journal of the Korean Wood Science and Technology
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• v.29 no.2
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• pp.91-99
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• 2001

In this study the possibility of classifying wood surface defects by image processing technique was investigated. An algorithm for the classification of wood surface defects, such as knot, check, and bark, on three Korean domestic species, Pinus densiflora, Quercus acutissima, and Carpinus laxiflora was also developed. Filtering was executed to separate dummies from the labels including real defect. Error rates in classifying knots on Pinus densiflora and Quercus acutissima were lower than 1% and error rates. In classifying check and bark in Quercus acutissima and Carpinus laxiflora could be lowered to below 13%.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.12-12
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• 2001

정보기술을 바탕으로 전자상거래의 규모는 빠르게 늘어가고 있다. 본 연구에서는 종합쇼핑몰의 성격을 띠는 사이버 쇼핑몰의 고객과 구매 고객의 특성 등을 살펴보고 의사결정나무를 이용한 이탈고객의 분류, 쇼핑몰에 등록된 상품군과 인구특성적인 변수들간의 대응분석을 실시하여 쇼핑몰에 대한 인식을 제고한다.

• Korean Journal of Environment and Ecology
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• v.29 no.6
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• pp.959-966
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• 2015

A natural monument is designated and protected as a natural or natural/cultural feature of outstanding or unique value because of its aesthetic qualities or cultural significance. However, in recent years, a natural monument plays a role in satisfying the cultural desire of people. For this reason, the main purpose of this study was to investigate public awareness of natural monuments and to evaluate the attitudes towards nature the visitors to Naejang national park displayed. This study also examined the differences in visitors' level of attitudes towards nature according to their awareness of natural monuments. Population of Macropodous Daphniphyllum (Natural Monument No. 91) and Forest of Japanese Torreyas at Baegyangsa Temple, Jangseong (Natural Monument No. 153) are present in large numbers in Naejang national park. For the research, 240 Naejang national park visitors were surveyed to collect data. Results of this study indicated that fewer than 50% of visitors displayed an appropriate awareness of natural monuments. There were also significant differences in attitudes towards nature according to visitors' awareness of natural monuments. In particular, visitors' awareness of the population of Macropodous Daphniphyllum was relatively lower when compared to that on Forest of Japanese Torreyas at Baegyangsa Temple. In addition, visitors who had a high level of awareness about natural monuments and thought that natural monuments had high cultural value displayed more positive attitudes than those who didn't have good levels of awareness. Based on these findings, this study suggests policy changes to establish development plans of the natural monuments in this area.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.56-62
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• 2008

This paper proposes the use of decision tree classification for the measuring pattern recognition from industrial Computed Radiography(CR) images used in nondestructive evaluation(NDE) of steel-tubes. It appears that NDE problems are naturally desired to have machine learning techniques identify patterns and their classification. The attributes of decision tree are taken from NDE test procedure. Geometric features, such as radiative angle, gradient and distance, are estimated from the analysis of input image data. These factors are used to make it easy and accurate to classify an input object to one of the pre-specified classes on decision tree. This algerian is to simplify the characterization of NDE results and to facilitate the determination of features. The experimental results verify the usefulness of proposed algorithm.

• IE interfaces
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• v.3 no.2
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• pp.53-62
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• 1990

본 연구는 컴퓨터에 의한 인쇄체 한글의 인식방법을 제안하고 있다. 일반적인 인식방법에서는 세선화과정 후의 이미지를 처리하고 있으나, 본 연구는 이 과정을 거치지 않고 원 이미지로부터 직업 패턴점들을 찾아내고, 이들을 이용하여 획을 결정하고 자모를 분리하였다. 문자 판별시에는 한글 의사 결정 나무(Decision-Tree)를 이용하여 자소를 분리하고 판별하였다. 본 연구는 자형에 관계없는 인식 방법을 제안 하였으므로 필기체 한글 인식에 기초를 제공하게 된다.