• 한국농공학회지
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• 제7권2호
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• pp.935-943
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• 1965

1. Nation wide soil surver is going of from Oct't by Unkup. According to the sever years program of national high hueld of food production campaign. 2. About eighry of new soil surveryors wee assigned to provincial office of Unkup. 3. Land capability classifieation comes from U.S.D.A method. Bur we fells most adequate land classification should be studied and set uo of the real Korean Natural situation. 4. This theory has been studied by the Unkup soil survey staffs.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(B)
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• pp.522-524
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• 2012

For exact classification of the defect, good feature selection and classifier is necessary. In this paper, various features such as brightness features, shape features and statistical features are stated and Bayes classifier using Gaussian mixture model is used as classifier. Also feature extraction method based on spectral graph theory is presented. Experimental result shows that feature extraction method using graph Laplacian result in better performance than the result using PCA.

• 한국통신학회논문지
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• 제5권1호
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• pp.49-53
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• 1980

Since Zadeh had published the fuzzy set theory at 1965, it has been applied to many fields such as realizability of communication nets, automatic control, learning systems, switching circuits. In this paper, the method of applying a fuzzy logic to a pattern classification is studied and the difference of fuzzy logic from Boolean algebra is discussed. Classfication experiment is carried out 16 persons' photos of three families by fourty male and female observers and recognition rate 94% is obtained.

• 대한수학회보
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• 제59권3호
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• pp.567-608
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• 2022

We show that certain extensions of classifiable C^*-algebras are strongly classified by the associated six-term exact sequence in K-theory together with the positive cone of K₀-groups of the ideal and quotient. We use our results to completely classify all unital graph C^*-algebras with exactly one non-trivial ideal.

• 한국도서관정보학회지
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• 제42권2호
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• pp.151-170
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• 2011

이 연구에서는 분석합성식 문헌분류이론이 실생활에 적용되어 활용되고 있는 사례들을 찾아 이를 새로운 시각에서 분석하고자 시도하였다. 이를 위해 우리가 일상적으로 사용하는 한글의 초성과 중성, 종성의 합성 사례와 함께, 대구광역시 및 서울특별시의 시내버스번호, 주요 결혼관련업체의 회원정보, 부동산중개 및 경매관련정보, 우편번호와 광역전화통화권역(DDD) 번호 등의 사례를 문헌분류의 시각에서 분석해보고 그것이 주는 시사점을 제시하였다. 분석합성식 분류이론과 그 기법을 적용할 경우 특히 각 시스템의 기호법의 개선에 도움이 될 수 있음을 밝히고 있다.

• 대한한의학원전학회지
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• 제23권1호
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• pp.163-186
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• 2010

Objective and Background : Attempt to express knowledge by IT is the current of the times, knowledge of the oriental medicine have to meet the needs of the times. It takes 'classification system of the oriental medicine terms' and 'system of the predicate' for explaining the relation between concepts to express knowledge by IT technique. Researches for 'classification system of the oriental medicine terms' are in progress already, researches for 'system of the predicate' are insufficient. Subject of study : We proceeded to study of the predicate in Idongwon(李東垣)'s "Biwiron(脾胃論)" has clear theory system and considerable influence upon knowledge of the oriental medicine for studying 'system of the predicate' which expresses knowledge of the oriental medicine in early stage. Method : Acquire Chinese play a predicate part in "Biwiron(脾胃論)", translate the Chinese to answer the context, group the similar predicate, decide representative predicate of group. And attempt to make classification system of the representative predicate with Term management system based on SQL Server 2005. Results and Considerations : I classify the predicate which predicate diagnosis, treatment, symptoms and knowledge of the oriental medicine into existence, condition, cognition and will. This classification seems to be useful to explain factors which have an effect on demonstration and treatment.

• 대한원격탐사학회지
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• 제28권6호
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• 대한공간정보학회지
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• 제15권3호
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• pp.33-39
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• 2007

하이퍼스펙트럴 영상자료는 객체에 대한 많은 정보를 함유하고 있어 객체의 보다 정확한 분류가 가능하다. 본 논문에서는 하이퍼스펙트럴 영상분류를 위하여 SMI(Spectral Mutual Information)이라는 새로운 스펙트럼 유사도 측정기법을 제안하였다. 본 방법은 정보이론 분야에서 대두된 상호정보량의 개념을 차용하여 고안되었으며 스펙트럼간의 통계적 의존성을 측정할 수 있다. SMI는 영상의 각 화소스펙트럼을 확률변수로 간주하고 두 스펙트럼간의 유사 상호정보량을 통하여 유사도를 측정함으로써 영상을 분류한다. 제안된 기법의 효율성을 평가하기 위해 기존에 개발된 SAM, SSV 분류기법을 이용하여 동일지역에 대해 분류를 수행하고 분류 정확도를 비교 평가하였다. 실험결과 제안한 SMI 기법은 하이퍼스펙트럴 영상분류에 유용하게 적용될 수 있으리라 판단된다.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.663-676
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• 2020

Plant leaf classification is a significant application of image processing techniques in modern agriculture. In this paper, a multi-granular angle description method is proposed for plant leaf classification and retrieval. The proposed method can describe leaf information from coarse to fine using multi-granular angle features. In the proposed method, each leaf contour is partitioned first with equal arc length under different granularities. And then three kinds of angle features are derived under each granular partition of leaf contour: angle value, angle histogram, and angular ternary pattern. These multi-granular angle features can capture both local and globe information of the leaf contour, and make a comprehensive description. In leaf matching stage, the simple city block metric is used to compute the dissimilarity of each pair of leaf under different granularities. And the matching scores at different granularities are fused based on quotient space theory to obtain the final leaf similarity measurement. Plant leaf classification and retrieval experiments are conducted on two challenging leaf image databases: Swedish leaf database and Flavia leaf database. The experimental results and the comparison with state-of-the-art methods indicate that proposed method has promising classification and retrieval performance.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.