• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• 터널과지하공간
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• 제19권5호
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• pp.407-420
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• 2009

이 연구에서는 시추 조사와 물리탐사 자료와 같은 다양한 지반 정보를 통합하여 최적의 암반 분류 기법을 도출하는데 지시크리깅을 적용하였다. 최적의 지시크리깅 결과를 얻기 위해서는 효과적으로 hard data(시추조사)와 soft data(물리탐사 자료)를 통합하기 위한 알맞은 방법을 모색할 필요가 있다. 이론적인 베리오그램 모델변수를 결정하기 위해 반복적 비선형 역산 방법을 적용하였고 이 알고리즘의 타당성 검증을 위해 목적함수의 분포양상을 검토한 결과 상관거리에 따른 구배는 대단히 작은 특성을 보였다. 현장 적용지역은 지표에서 터널 계획고까지의 심도가 최대 500 m인 대규모 산악터널 예정지이다. 지시크리깅을 이용하여 soft data인 AMT (Audio frequency Magneto-Telluric) 탐사 자료와 hard data인 RMR자료를 하나로 통합하고자 하였다. 결론적으로 터널계획고와 터널 상부 1D 구간에 대한 암반등급도를 작성하여 도시하였다.

• Journal of Periodontal and Implant Science
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• 제51권2호
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• pp.100-113
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• 2021

Purpose: Previous studies have solely focused on fresh extraction sockets, whereas in clinical settings, alveolar sockets are commonly associated with chronic inflammation. Because the extent of tissue destruction varies depending on the origin and the severity of inflammation, infected alveolar sockets may display various configurations of their remaining soft and hard tissues following tooth extraction. The aim of this study was to classify infected alveolar sockets and to provide the appropriate treatment approaches. Methods: A proposed classification of extraction sockets with chronic inflammation was developed based upon the morphology of the bone defect and soft tissue at the time of tooth extraction. The prevalence of each type of the suggested classification was determined retrospectively in a cohort of patients who underwent, between 2011 and 2015, immediate bone grafting procedures (ridge preservation/augmentation) after tooth extractions at Seoul National University Dental Hospital. Results: The extraction sockets were classified into 5 types: type I, type II, type III, type IV (A & B), and type V. In this system, the severity of bone and soft tissue breakdown increases from type I to type V, while the reconstruction potential and treatment predictability decrease according to the same sequence of socket types. The retrospective screening of the included extraction sites revealed that most of the sockets assigned to ridge preservation displayed features of type IV (86.87%). Conclusions: The present article classified different types of commonly observed infected sockets based on diverse levels of ridge destruction. Type IV sockets, featuring an advanced breakdown of alveolar bone, appear to be more frequent than the other socket types.

• 대한치과의사협회지
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• 제48권9호
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• pp.670-679
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• 2010

Esthetic demands for dental treatment are increasing every day. The interdisciplinary relationship of the restorative treatment, periodontal therapy and other treatments such as endodontics, orthodontics and so on is more emphasized nowadays to reconstruct the hard and soft tissue foundation for the esthetic restorative treatment. This article will focus on the periodontal plastic surgery for esthetic restorative treatment. These followings will be discussed. 1. Understand the relationship between teeth and gingival scaffold for esthetics 2. Discuss the classification and treatment of gummy smile 3. Recognize the gingival margin irregularities by gingival recession and how to achieve the harmonic soft tissue margins 4. describe the hard and soft tissue augmentation for ridge augmentation.

• 대한치과의사협회지
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• 제22권12호통권187호
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• pp.1043-1046
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• 1984

The clinical and statistical evaluation on the patients of maxillofacial deformities who were operated by author were analyzed after several month or years or se. Pre and post operative cephalometric radiographs of 45 orthognathic surgery patients were compared. The post operative radiographs had been taken at least 9 month to several years. Measurements were made between constructed hard tissue and soft tissue points located on each before and after film tracing. The items studied and evaluated are as follows: 1) Classification and divid of patients 2) Operation technic adopted by operator. 3) Motives of patients for operation and their untowards. 4) Self satisfication of patients after operation. 5) Post operative changes of soft and hard tissue. 6) Side action during and after operation.

• 한국정보통신학회논문지
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• 제17권8호
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• pp.1899-1907
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• 2013

본 논문에서는 초미세 패턴으로 구성된 칩-온-필름(Chip-on-Film, COF) 패키징 작업에서 발생하는 결함들을 참조영상에 기초하여 효율적으로 검출하고 분류하는 시스템을 제안한다. COF패키징 제작 과정에서 발생하는 치명적인 결함은 개방(open), 일부개방(mouse bite, near open), 단락(hard short) 및 돌기(protrusion, near short, soft short) 등을 포함한다. 이러한 결함을 검출하기 위해서는 기존에 직접 육안으로 식별하거나 또는 전기회로 설계를 이용하는 방법을 사용하였다. 그러나 이러한 방법은 매우 많은 시간과 고비용이 요구되는 단점이 있다. 본 논문에서는 참조영상을 사용하여 효과적으로 결함유무를 판단하고 결함이 발생되는 경우에 결함의 종류를 4 가지 형태로 분류하는 시스템을 제안한다. 제안방식은 검사영상의 전처리, 관심영역 추출, 지역이진분석에 의한 이물 특징 분석과 분류 등을 포함한다. 수많은 실험을 통해, 제안된 시스템은 초미세 패턴을 가진 COF의 결함 검사 및 분류에 대해 기존의 방식에 비해 시간과 경비를 줄이는데 효과적임을 보인다.

• 한국경영과학회지
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• 제22권3호
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• 한국작물학회지
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• 제47권
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• pp.63-94
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• 2002

On the purpose to suggest an advanced scheme in assessing the domestic wheat quality, this paper reviewed the inspection systems of wheat in major wheat producing countries as well as the quality criteria which are being used in wheat grading and classification. Most wheat producing countries are adopting both classifications of class and grade to provide an objective evaluation and an official certification to their wheat. There are two main purposes in the wheat classification. The first objectives of classification is to match the wheat with market requirements to maximize market opportunities and returns to growers. The second is to ensure that payments to glowers aye made on the basis of the quality and condition of the grain delivered. Wheat classes has been assigned based on the combination of cultivation area, seed-coat color, kernel and varietal characteristics that are distinctive. Most reputable wheat marketers also employ a similar approach, whereby varieties of a particular type are grouped together, designed by seed coat colour, grain hardness, physical dough properties, and sometimes more precise specification such as starch quality, all of which are genetically inherited characteristics. This classification in simplistic terms is the categorization of a wheat variety into a commercial type or style of wheat that is recognizable for its end use capabilities. All varieties registered in a class are required to have a similar end-use performance that the shipment be consistent in processing quality, cargo to cargo and year to year, Grain inspectors have historically determined wheat classes according to visual kernel characteristics associated with traditional wheat varieties. As well, any new wheat variety must not conflict with the visual distinguishability rule that is used to separate wheats of different classes. Some varieties may possess characteristics of two or more classes. Therefore, knowledge of distinct varietal characteristics is necessary in making class determinations. The grading system sets maximum tolerance levels for a range of characteristics that ensure functionality and freedom from deleterious factors. Tests for the grading of wheat include such factors as plumpness, soundness, cleanliness, purity of type and general condition. Plumpness is measured by test weight. Soundness is indicated by the absence or presence of musty, sour or commercially objectionable foreign odors and by the percentage of damaged kernels that ave present in the wheat. Cleanliness is measured by determining the presence of foreign material after dockage has been removed. Purity of class is measured by classification of wheats in the test sample and by limitation for admixtures of different classes of wheat. Moisture does not influence the numerical grade. However, it is determined on all shipments and reported on the official certificate. U.S. wheat is divided into eight classes based on color, kernel Hardness and varietal characteristics. The classes are Durum, Hard Red Spring, Hard Red Winter, Soft Red Winter, Hard White, soft White, Unclassed and Mixed. Among them, Hard Red Spring wheat, Durum wheat, and Soft White wheat are further divided into three subclasses, respectively. Each class or subclass is divided into five U.S. numerical grades and U.S. Sample grade. Special grades are provided to emphasize special qualities or conditions affecting the value of wheat and are added to and made a part of the grade designation. Canadian wheat is also divided into fourteen classes based on cultivation area, color, kernel hardness and varietal characteristics. The classes have 2-5 numerical grades, a feed grade and sample grades depending on class and grading tolerance. The Canadian grading system is based mainly on visual evaluation, and it works based on the kernel visual distinguishability concept. The Australian wheat is classified based on geographical and quality differentiation. The wheat grown in Australia is predominantly white grained. There are commonly up to 20 different segregations of wheat in a given season. Each variety grown is assigned a category and a growing areas. The state governments in Australia, in cooperation with the Australian Wheat Board(AWB), issue receival standards and dockage schedules annually that list grade specifications and tolerances for Australian wheat. AWB is managing "Golden Rewards" which is designed to provide pricing accuracy and market signals for Australia's grain growers. Continuous payment scales for protein content from 6 to 16% and screenings levels from 0 to 10% based on varietal classification are presented by the Golden Rewards, and the active payment scales and prices can change with market movements.movements.

• 대한치과교정학회지
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• 제31권5호
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• pp.535-548
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• 2001

본 연구는 소구치 발치와 전치부 후방 이동을 통한 교정 치료시 이부 연조직의 형태 변화와 주위 경조직과의 상관관계를 알아보기위해 시행되었다. 초진시 구순 돌출감을 보인 Angle분류 I급 또는 II급 1류 부정교합의 환자로서 상하악의 제 1 또는 제 2 소구치를 발치하고 상하악 전치의 후방 이동을 시행한 성인 여성 환자 35명을 대상으로 하였다. 치료전후의 측모두부방사선사진을 계측하였으며, 특히 이부 연조직을 재현성있는 여섯 부분으로 세분화하여 살펴보았고 다음과 같은 결론을 얻었다. 1. 치료전 이부 연조직과 경조직간의 상관 관계를 살펴본 결과 B-B'와 Pm-Pm'는 수직 골격 계측 항목인 $MP{\perp}HP,\;MP{\perp}PP$, ALFH와 유의 한 순상관관계 (P<0.001)를 보이고 a-a', b-b', Me-Me'는 이부 형태 계측 항목인 SL, SW, PL와 유의한 역상관관계(P<0.01)를 보였다. 2. 이부 연조직은 치료전후에 B-B'과 Pm-Pm'에서는 유의한 감소를 보였고(P<0.001), a-a', b-b'에서는 유의한 증가(P<0.01)를 보였다. 3.치료후 이부 연조직 변화는 B-B'의 변화를 제외한 모든 부위에서 경조직의 변화와 상관관계를 보였으며 상관 계수는 0.3-0.4정도였다. 4. 치료후 Pog-Pog'이 증가한 집단과 감소한 집단간에 ${\Delta}UI-VP,\;LI{\perp}의 계측 항목이 유의한 차이를 보였다. 5. 치료후 Me-Me'이 증가한 집단과 감소한 집단간에 ${\Delta}overbite,\;NPog{\perp}HP,\;Me-Me'$의 계측 항목이 유의한 차이를 보였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권3호
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• pp.149-154
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• 2017

MCT-Adaboost 학습 알고리즘은 각 학습 단계에서 배경과 객체를 구분하는 가장 좋은 특징을 찾는 학습 알고리즘이다. 각 학습 단계에서는 최적의 특징을 검출하기 위해 학습 데이터에서 각 특징의 각 커널에서 모든 오차율을 산정하고, 각 특징에서 모든 커널들의 합을 하였을 경우 최소 오차율을 가지는 특징을 선택하도록 되어 있다. 이를 선택하고 다음 학습때 영향을 주는 약분류기에서 기존의 MCT-Adaboost 방법은 경판정 방법으로 사용하였다. 이 방법은 특정 커널에서 객체 데이터와 배경 데이터의 오류율이 유사할 경우, 한쪽으로 판정하기 때문에, 제대로 된 결과값을 산정할 수 없는 문제가 있다. 이를 유연하게 하기 위해 본 연구에서는 연판정을 이용한 약분류기 방법을 제안한다. 기존의 MCT-Adaboost는 초기 가중치를 동일하게 산정한다. 하지만, 이는 데이터의 특성을 모른다는 가정하에 설계된 초기 가중치 설정이다. 본 논문에서는 데이터 분포를 이용하여 가중치를 확률적으로 다르게 할당함으로서, 적은 학습에도 좋은 결과를 보이는 방법을 제안한다. 실험 결과에는 기존의 MCT-Adaboost가 제안하는 성능평가를 통해, 본 연구가 제안하는 방법이 기존 방법에 비해 좋은 결과를 보였다.