• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.715-717
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• 2022

객체 탐지는 디지털 이미지나 비디오에서 유의미한 객체를 탐지하는 작업을 말한다. 이 작업은 객체가 있는 곳에 경계상자를 그리는 Localization과 객체의 Class를 구분하는 Classification 이 두 단계로 나눌 수 있는데, 각각의 단계를 순차적으로 행하는 2-stage detection 방식과 동시에 행하는 1-stage detection 방식을 실시간으로 마스크 착용여부를 탐지하는 프로그램에 적용하면서 속도와 성능을 비교하고 어떤 방식이 적합한지 연구한다

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1024-1027
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• 2009

In this letter, we evaluate the classification performance of mixed numeric and categorical data for comparing the efficiency of feature filtering and feature wrapping. Because the mixed data is composed of numeric and categorical features, the feature selection method was applied to data set after discretizing the numeric features in the given data set. In this study, we choose the feature subset for improving the classification performance of the data set after preprocessing. The experimental result of comparing the classification performance show that the feature wrapping method is more reliable than feature filtering method in the aspect of classification accuracy.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.41-50
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• 2006

In order to improve software development productivity and quality, reuse approaches and supporting library systems have been proposed. Library systems have applied various methods to classify, store, retrieve, and comprehend reusable components effectively. As the number of library systems grows, it is difficult to categorize, compare and analyze existing reuse libraries. In this paper, we present classification criteria for reuse library systems. A set of criteria is defined by integrating facet-based and attribute-based classification methods which encode the properties of a reusable component. In order to show the usefulness of the proposed classification criteria, representative library systems based on application domains, as well as component classification methods ore selected and reviewed. We then classify these library systems according to the proposed criteria.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.277-286
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• 2021

The global market size of AI based SaMD for medical image in 2023 will be anticipated to reach around 620 billion won (518 million dollars). In order for Korean manufacturers to efficiently obtain CE marking for marketing in the EU countries, the paper is to introduce the recommendation and suggestion of how to reclassify SaMD based on classification rules of MDR because, after introducing the Regulation EU 2017/745, classification rules are quite modified and newly added compared to the Directive 93/42/EEC. In addition, the paper is to provide several rules of MDR that may be applicable to decide the classification of SaMD. Lastly, the paper is to examine and demonstrate various secondary data supported by qualitative data because the paper focuses on the suggestion and recommendation with a public trust on the basis of various secondary data conducted by the analysis of field data. In conclusion, the paper found that the previous classification of SaMD followed by the rule of MDD should be reclassified based on the Regulation EU 2017/745. Therefore, the suggestion and recommendation are useful for Korean manufacturers to comprehend the classification of SaMD for marketing in the EU countries.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.65-78
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• 2023

Investigations on information security factors re- main elusive at small and medium enterprises (SMEs), es- specially for custom-made software solutions. This article aims to investigate, classify, adopt factors from recent literature addressing information security resources. SMEs al- ready have information security in place, but they are not easy to adopt through the negotiation processes between the in-house software development companies and custom-made software clients at SMEs. This article proposes a strategic framework for implementing the process of adoption of the information security factors at SMEs after conducting a systematic snapshot approach for investigating and classifying the resources. The systematic snapshot was conducted using a search strategy with inclusion and exclusion criteria to retain 128 final reviewed papers from a large number of papers within the period of 2001-2022. These papers were analyzed based on a classification schema including management, organizational, development, and environmental categories in software development lifecycle (SDLC) phases in order to define new security factors. The reviewed articles addressed research gaps, trends, and common covered evidence-based decisions based on the findings of the systematic mapping. Hence, this paper boosts the broader cooperation between in-house software development companies and their clients to elicit, customize, and adopt the factors based on clients' demands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1561-1583
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• 2022

Context: Testing is one of the techniques that can assure the quality of software including the domain of Software Product Line (SPL). Various techniques have been deliberated to enhance the quality of SPL including Model-based Testing (MBT). Objective: The objective of this study is to analyze and classify trends of MBT in SPL covering the solutions, issues and evaluation aspects by using taxonomy form. Method: A Systematic Literature Review (SLR) was conducted involving 63 primary studies from different sources. The selected studies were categorized based on their common characteristics. Results: Several findings can guide future research on MBT for SPL. The important finding is that the multiple measurements are still open to improving current metrics to evaluate test cases in MBT for SPL. The multiple types of measurement required a trade-off between maximization and minimization results to ensure the testing method which could satisfy multiple test criteria for example cost and effectiveness at the same time.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.113-141
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• 2016

This study identifies and validates the factors that affect sales performance of salespersons in the business software industry. In the study, in order to measure the dependent variable (performance of the salesperson) more comprehensively, multiple items are utilized and both outcome and behavior indicators are used. Independent variables are identified based on the classification of Verbeke et al. [(2011] including sales related knowledge, degree of adaptiveness, role ambiguity, and work engagement. Results of the hypotheses testing show that 'sales related knowledge' and 'work engagement' are statistically significant factors, but 'degree of adaptiveness' and 'role ambiguity' are not. This study has a few limitations and future research direction to overcome the limitation is suggested : use of both perceptions of the salesperson and objective measures in measuring the related variables; study including cognitive ability; analyses of the factors across various types of software companies; and analyses of the factors on the team level.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.288-291
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• 2011

기존 음악 장르 분류의 경우 음악의 특징 추출 또는 기계학습을 중점적으로 연구되어왔다. 하지만 자동 분류에 필요한 장르 데이터는 음악을 제공하는 웹 사이트마다 다르고, 각 웹 사이트의 장르 분류는 해당 음악이 아닌 앨범의 장르를 표시한다. 보다 나은 자동 분류를 위해서는 일관된 장르 데이터의 제공이 필요한데, 본 논문에서는 이러한 연구의 일환으로 여러 웹사이트에서 수집한 장르 데이터에 따른 판별 성능을 분석하였다. 분석 결과 장르 분류 방법에 따라 신경망 학습 및 판별성능이 큰 차이가 발생하였다.