• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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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.

• Honam Mathematical Journal
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• v.38 no.1
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.6
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• pp.802-814
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• 1993

This paper propose an algorithm for preprocessing of character recognition, which classify characters into Hangeul and Hanja. In this study, we use the 9 structural chacteristics of Hanja which isn't affected by deformation of size and style of characters and rates based on character size to classify characters. Firstly, we process the blocking to segment each characters. Secondly, on this segmented characters, we apply algorithm proposed in this paper to classify Hangeul and Hanja. Finally, we classify characters into Hangeul and Hanja, respectively. An experiment with 2350 Hangeul and 4888 Hanja printed Gothic and Mincho style of KS-C 5601 are carried out. We experiment on typeface sample book, newspapers, academic society's papers, magazines, textbooks and documents written out word processor to obtain the classifying rates of 98.8%, 92%, 96%, 98% and 98%, respectively.

• Journal of The Korean Association For Science Education
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• v.33 no.3
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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.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.3
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• pp.259-279
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• 2008

The purpose of this study is to classify theological terms using facets and form foundation to build a theological thesaurus The terms was chosen in a English theological dictionary and the number of them are 1,031 but eventually, 984 terms were classified. The facets which classify the terms are 7 fundamentals and 14 sub-facets. Analysing the result of classification, abstract terms are much more than physical terms. They are 70% of the whole. Decimal classification to classify documents comes to dead end. therefore I think the research to classify terms of a specific subject as theology must be active. Furthermore, It is necessary to have knowledge about a specific subject to organize information in a specific subject.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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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.

• Journal of Sensor Science and Technology
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• v.25 no.6
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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.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1177-1183
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• 2018

Recent developments in machine learning have attracted a lot of attention for techniques such as machine learning and deep learning that implement artificial intelligence. In this paper, binary malicious code using deep learning based R-CNN is imaged and the feature is extracted from the image to classify the family. In this paper, two steps are used in deep learning to image malicious code using CNN. And classify the characteristics of the family of malicious codes using R-CNN. Generate malicious code as an image, extract features, classify the family, and automatically classify the evolution of malicious code. The detection rate of the proposed method is 93.4% and the accuracy is 98.6%. In addition, the CNN processing speed for image processing of malicious code is 23.3 ms, and the R-CNN processing speed is 4ms to classify one sample.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.1
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• pp.77-85
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• 2014

Medicine-bottle classification algorithm to avoid medicine accidents must be robust to a geometric change such as rotation, size variation, location movement of the medicine bottles. In this paper, we propose an algorithm to classify the medicine bottles exactly in real-time by using SIFT(Scale Invariant Feature Transform) which is robust to the geometric change. In first, we classify medicine bottles by size using minimum boundary rectangle(MBR) of medicine bottles as a striking feature in order to classify the medicine bottles. We extract label region in the MBR and the region of interest(ROI) considering rotation. Then, we classify medicine bottles using SIFT for the extracted ROI. We also simplify the number of octave of SIFT in order to improve a process speed of SIFT. We confirm to classify all the medicine bottles exactly as a result of performance evaluation of the proposed algorithm about images of 250 medicine bottles. We also confirm to improve the process time more than twice the processing time by simplifying the number of octave of SIFT.