• 산업공학
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• 제18권1호
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• Journal of applied mathematics & informatics
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• 제1권1호
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• pp.1-12
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• 1994

We consider a single channel CSMA/CD system with D homogeneous stations and impeded buffer of infinite size. We find a sufficient condition for the model to be stable under the threshold control policy and derive the limiting distri-bution of the number of messages in the system at the moment of service completion. We also derive the limiting distributing of the number of messages in the system size at arbitrary time by using Markov regenerative processes. Some numerical examples and special cases are also treated.

• 대한원격탐사학회지
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• 제34권4호
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• pp.661-670
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• 2018

This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

• 한국측량학회지
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• 제38권5호
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• pp.407-414
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• 2020

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

• 한국IT서비스학회지
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• 제21권5호
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• pp.65-79
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• 2022

The smart factory has spread to small and mid-size enterprises (SMEs) under the leadership of the government. Smart factory consists of a work area, an operation management area, and an industrial control system (ICS) area. However, each site is combined with the IT system for reasons such as the convenience of work. As a result, various breaches could occur due to the weakness of the IT system. This study seeks to discover the items and vulnerabilities that SMEs who have difficulties in information security due to technology limitations, human resources, and budget should first diagnose and check. First, to compare the existing domestic and foreign smart factory vulnerability classification systems and improve the current classification system, the latest smart factory vulnerability information is collected from NVD, CISA, and OWASP. Then, significant keywords are extracted from pre-processing, co-occurrence network analysis is performed, and the relationship between each keyword and vulnerability is discovered. Finally, the improvement points of the classification system are derived by mapping it to the existing classification system. Therefore, configuration and maintenance, communication and network, and software development were the items to be diagnosed and checked first, and vulnerabilities were denial of service (DoS), lack of integrity checking for communications, inadequate authentication, privileges, and access control in software in descending order of importance.

• 한국지형학회지
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• 제26권4호
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• pp.47-65
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• 2019

A case study was conducted in Taean region to seek a more detailed macrotidal beach classification than existing beach classification models (Masselink and Short, 1993). Seepage and ridge & runnel were used for classification. On 20 beaches, 68 transects were surveyed 5 times using VRS-GPS. Cross-section area from the transect profiles, mean grain size from sediment analysis, significant wave height from Swan-wave modeling and beach embaymentization from aerial photograph analysis were used to identify the characteristics of the individual types. The transects were classified into 5 types in Taean region; Type 1: low tidal terrace, Type 2: low tidal terrace & ridge, Type 3: dissipative, Type 4: seasonal ridge, and Type 5: ridge & runnel. Generally, seepage was related to coarse sediment size and ridge & runnel was related to high significant wave height. Each type has different characteristics and there was a tendency between the types. The low tidal terrace type had coarse sediments, because this type is excluded from the littoral cell. In this study, the ridge and runnel type could be applied to the classification because the study area is limited only to the macrotidal environment in Taean region.

• 한국의류학회지
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• 제45권1호
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• pp.1-16
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• 2021

This study classified short adult male body types and identified characteristics by body type according to Size Korea's 7th human system measurement data for men in their 20s to 60s. There were four body types for short adult males. Type 1 was a 'short bird legs-normal body shape' with an average body size, low body height, short torso length, thin legs, and no sagging shoulders. Type 2 was a 'short torso thin body' with a small body size, a slim body, a high body height, a short torso length and no sagging shoulders. Type 3 was a 'thick leg-overweight body shape' with a large body size, thick legs, low body height, small shoulder length and obesity. Type 4 was a 'long bird legs-normal body' with a normal body size, high body height, thin legs, long torso and sagging shoulders. The development of clothing design and pattern reflecting the body shape characteristics of short adult males should be improved to fit clothing and suitability. It is necessary to increase the satisfaction of ready-to-wear for consumers with various body types by adding the size for shorter men through a subdivision of the ready-made size system.

• 정보처리학회논문지B
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• 제9B권3호
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• pp.311-318
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• 2002

문서영상 구조분석은 문서영상을 세부 영역으로 분할하는 과정과 분할된 영역을 문자, 그림, 표 등으로 분류하는 과정을 포함한다. 이 중 영역분류 과정에서 영역의 크기, 흑화소 밀도, 화소 분포의 복잡도는 영역을 분류하는 기준이 된다. 그러나 그림의 경우 이러한 기준들의 범위가 넓어 경계를 정하기 어려우므로 다른 형태에 비해 상대적으로 오분류의 비율이 높다. 본 논문에서는 그림과 문자를 분류하는 과정에서 영역의 크기, 흑화소 밀도, 화소 분포의 복잡도에 의한 영향을 줄이기 위하여 메디안 필터를 이용하고, 영역확장 필터(region expanding filter)와 제한 조건들을 이용하여 영역분류에서의 오분류를 수정함으로써 상용제품을 포함한 기존 방법에 비해 그림과 문자의 분류가 우수한 문서영상 구조 분석 방법을 제안한다.

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• 한국군사과학기술학회지
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• 제13권1호
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• pp.147-155
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• 2010

Landcover information of access-denied area was extracted from low-medium and high resolution satellite image. Training for supervised classification was performed to refer visually by landcover map which is made and distributed from The Ministry of Environment. The classification result was compared by relating data of FACC land classification system. As we rasterize digital military map with same pixel size of satellite classification, the accuracy test was performed by image to image method. In vegetation case, ancillary data such as NDVI and image for seasons are going to improve accuracy. FACC code of FDB need to recognize the properties which can be automated.