• 전기학회논문지
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• 제68권1호
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• pp.153-158
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• 2019

In this paper, we analyze the infrared feature for the small coast targets according to the surrounding environment for autonomous flight device equipped with an infrared imaging sensor and we propose Cross Duality of Neural Network (CR-DuNN) method which can classify the target and clutter in coastal environment. In coastal environment, there are various property according to diverse change of air temperature, sea temperature, deferent seasons. And small coast target have various infrared feature according to diverse change of environment. In this various environment, it is very important thing that we analyze and classify targets from the clutters to improve target detection accuracy. Thus, we propose infrared feature learning algorithm through LSTM neural network and also propose CR-DuNN algorithm that integrate LSTM prediction network with Du-CNN classification network to classify targets from the clutters.

• 호남수학학술지
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• 제38권1호
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• pp.59-69
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• 2016

We classify conformally flat Kenmotsu 3-manifolds and classify conformally flat cosympletic 3-manifolds.

• 한국통신학회논문지
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• 제18권6호
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• pp.802-814
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• 1993

본 논문에서는 문서인식시스템의 문자인식부에서 각 문자를 인식하기 위한 전처리 단계인 한글과 한자를 구별하는 알고리즘을 제안한다. 본 연구에서는 문자의 구별에 큰 영향을 미치고, 쓰기형태와 글자체에 따라서 변동을 흡수할 수 있는 9가지의 한자 특성을 제안하고, 문자의 크기에 영향을 받지 않고 문자를 구별할 수 있도록 문자 크기에 따른 비율을 제안된 각 특성에 반영하여 문자의 구별을 행하였다. 입력된 문서 제안한 9가지의 한자 구조적 특성을 조사하여, 한글과 한자로 구별한다. KS-C5601의 한글 2350자와 한자 4888자의 고딕, 명조체에 대하여, 실험결과는 인쇄 표본, 신문, 학회지, 잡지 교재에서 각각 98.8%, 92%, 96%, 98%, 98%을 얻었다.

• 한국과학교육학회지
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• 제33권3호
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• pp.679-693
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• 2013

The purpose of this study is to develop an inventory to classify task commitment types of science learning and to classify highschool students' task commitment types. Firstly, inventory questions were designed following the literature analysis on the task commitment components which involve self confidence, high goal setting, and focused attention. Prototype inventory underwent the content validity test, pilot test, and reliability test. Through these steps, final inventory was input to 462 high school students and underwent the factor analysis and cluster analysis. Factor analysis confirmed three components of task commitment as the three factors of inventory questions. In order to find how many clusters exist, factors of developed inventory became new variables. Each factor's factor mean was calculated and served as the new variable of the cluster analysis. Cluster analysis extracted five clusters as task commitment types. The 5 clusters were suggested by the agglomarative schedule and dendrogram gained from a hierarchical cluster analysis with the setting of the Ward algorithm and Squared Euclidean distance. Based on the factor mean score, traits of each cluster could be drawn out. Inventory developed by this study is expected to be used to identify student commitment types and assess the effectiveness of task commitment enhancement programs.

• 한국문헌정보학회지
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• 제42권3호
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• pp.259-279
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• 2008

신학 분야의 시소러스를 구축하는 전 단계로서, 패싯을 이용하여 신학 용어를 분류하였다. 영어로 된 신학 사전에서 1.031개의 용어를 선정하였고, 이 중에서 실제로 분류한 용어의 수는 984개이다. 용어를 분류하기 위해서 전개한 패싯은 기본 패싯 7개와 하위 패싯 14개이었다. 분류한 용어들을 분석해 본 결과, 신학 분야의 특성에 맞게 물리적 형태를 갖는 구체적인 용어의 수보다 추상적인 용어의 수가 많았으며. 전체 용어 수의 70% 정도를 차지하였다. 문헌 분류를 위한 십진 분류 체계에 대한 연구가 한계에 이른 상황에서. 이러한 특정 주제 분야의 용어를 분류하고 시소러스를 구축하는 연구가 더 활발해져야 한다고 생각한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.1007-1014
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• 2003

It is important to integrate an enterprise application for automating of the business profess, which is responded by a flow of market environment. There are two categories of method that integrate enterprise applications. One is Synchronous Integration, and the other is Asynchronous Integration. EAI(Enterprise Application Integration) and Web service which of the asynchronous integration is focused in the automating method of the business process. After we construct the application integration for automating of the business process, we have to concern about managing of the business transaction. Many Organizations have proposed the process method of business transaction based on 2-phase commit protocol. But this method can′t supply the phase that classify the transaction by transaction weight. In this paper, we propose an efficient method of transaction process for business transactions, which is composed by ′Classify Phase′ that classify transactions. We called this model "3-Phase Commit Method Applied by Classify Phase", we design this model to manage an resource of enterprise efficiently. The proposed method is compared by the method based on 2-Phase commit that could be a problem of management the resource of enterprise, and the advantage of this method is certified to propose the solution of that problem.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.852-862
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• 1996

This study was carried out to develop tools to detect defects of apple using machine vision. For the purpose, 6 kinds of frame for color images, R, G, B, h, S, and I frame, and a frame for near infra-red images (NIR frame) were tested first to select one which is useful to segment defect areas from apple images. After then, several methods to classify kind of defect for the segmented defect areas were developed and tested. Five kinds of apple defect -bruise , decay ,fleck worm hole and scar were investigated . The results are as follows: NIR frame was selected as the best one among the 7 kinds of image frame, and R, G and I frames showed favourable result to segment areas of apple defect. Various features of the segmented defect areas were measured to classify the defect areas. Eight kids of feature of the areas-size, roundness, axes length ratio, mean and variance of pixel values, variance of real part of spectrum, mean and variance of power spectrum resulted from spacial ourier transform were observed for the segmented defect areas in the selected 4 frames. then procedures to classify defects using the features were developed for the 4 frames and tested with 75-113 defects on apples. The test resulted that NIR and I frames showed high accuracies to classify the kind of defect as 77% and 76% , respectively.

• 센서학회지
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• 제25권6호
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• pp.423-430
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• 2016

This study used a single metal oxide semiconductor (MOS) sensor to classify the major odorous gases hydrogen sulfide ($H_2S$), ammonia ($NH_3$) and toluene ($C_6H_5CH_3$). In order to classify these odorous substances, the voltage on the MOS sensor heater was gradually reduced in 0.5 V steps 5.0 V to examine the changes to the response by the cooling effect on the sensor as the voltage decreased. The hydrogen sulfide gas showed the highest sensitivity compared to odorless air under approximately 2.5 V and the ammonia and toluene gases showed the highest sensitivity under approximately 5.0 V. In other words, the hydrogen sulfide gas reacted better in the low temperature range of the MOS sensor, and the ammonia and toluene gases reacted better in the high-temperature range. In order to analyze the response characteristics of the MOS sensor by temperature in a pattern, a two-dimensional (2D) x-y pattern analysis was introduced to clearly classify the hydrogen sulfide, ammonia, and toluene gases. The hydrogen sulfide gas was identified by a straight line with a slope of 1.73, whereas the ammonia gas had a slope of 0.05 and the toluene gas had a slope of 0.52. Therefore, the 2D x-y pattern analysis is suggested as a new way to classify these odorous substances.

• 디지털콘텐츠학회 논문지
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• 제19권6호
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• pp.1177-1183
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• 2018

최근 기계학습의 발달로 인공지능을 구현하는 머신러닝과 딥러닝 같은 기술이 많은 관심을 받고 있다. 본 논문에서는 딥러닝 기반의 R-CNN을 이용한 바이너리 악성코드를 이미지화 하고 이미지에서 특징을 추출해 패밀리를 분류한다. 본 논문에서는 딥러닝에서 두 단계를 이용해 악성코드를 CNN을 이용해 이미지화하고, 악성코드의 패밀리가 갖는 특징을 R-CNN을 이용해 분류함으로 악성코드를 이미지화하여 특징을 분류하고 패밀리를 분류한 후 악성코드의 진화를 자동 분류한다. 제안 기법은 검출율이 93.4%로 우수한 탐지 성능을 보였고 정확도는 98.6%로 매우 높은 성능을 보였다. 또한 악성코드를 이미지화 하는 CNN 처리속도가 23.3ms, 하나의 샘플을 분류하기 위해서 R-CNN처리 속도는 4ms로 비교적 빠르게 악성코드를 판별하고 분류가 가능함을 실험을 통해 증명하였다.

• 한국산업정보학회논문지
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• 제19권1호
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• pp.77-85
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• 2014

약화 사고 방지를 위한 약통 분류 알고리즘은 약통의 회전, 크기변화, 위치 이동 등의 기하학적 변화에 강인하여야 한다. 본 논문에서는 기하학적 변화에 강인한 SIFT(Scale Invariant Feature Transform)을 이용하여 약통을 실시간으로 정확하게 분류하는 알고리즘을 제안한다. 먼저, 약통 분류를 위해서 두드러진 특징으로 약통의 크기 정보인 최외곽 사각형을 이용하여 약통을 크기 별로 분류한다. 다음으로 최외곽 사각형내에서 라벨 영역을 추출하고, 회전을 고려한 관심영역을 추출한다. 그리고 추출된 관심영역에 대해 SIFT를 이용하여 약통을 분류한다. 또한 SIFT의 처리 속도를 개선하기 위하여 SIFT의 옥타브 수를 간소화하였다. 250개의 약통 영상에 대해 제안한 알고리즘의 성능을 평가한 결과, 모든 약통에 대해 정확히 분류함을 확인하였다. 또한 SIFT의 피라미드 레벨 간소화에 의해 처리 시간을 2배 이상 향상됨을 확인하였다.