• 시스템엔지니어링워크숍
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• s.1
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• pp.127-133
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• 2003

시나리오 형태의 요구사항 분류는 ATAM, SAAM, Software Quality Metric 과 같은 품질 요구사항 분석 및 평가 방법 등 많은 분야에 응용된다. 이들 기법들은 소프트웨어 시스템의 품질 요구사항을 분석, 평가하기에 앞서 초기 수집된 요구사항들을 분류하게 된다. 그러나 요구사항을 분류하는 일은 수작업을 통해 이루어지게 되고, 따라서 미 분류, 중복분류, 등의 결함을 가질 수 있다. 결함의 가능성을 요구사항의 수가 많은 대형 프로젝트 일수록 높아지게 된다. 따라서 본 논문에서는 요구사항 분류언어를 통한 품질 요구사항 자동 분류 기법을 제안한다. 제안된 기법은 분류언어와 유사도를 이용한 2 단계 분류기법을 이용하였다. 분류언어는 각 도메인별로 개발되어 비슷한 도메인일 경우 재사용될 수 있다. 이를 검증하기 위해, 본 논문에서는 15 여개의 프로젝트로부터 수집된 요구사항을 이용해 실험을 수행하고 그 결과를 분석, 평가 하였다.

• ETRI Journal
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• v.39 no.3
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• Electronics and Telecommunications Trends
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• v.17 no.2 s.74
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• pp.49-60
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• 2002

소프트웨어 관리 모델은 크게 소프트웨어 프로젝트 견적 모델과 소프트웨어 설계평가 모델, 소프트웨어 복잡성 모델, 소프트웨어 신뢰도 성장 모델, 소프트웨어 프로세스 개선 모델 등으로 나누어진다. 그 중에서도 개발된 소프트웨어를 정량적으로 분석하여 평가하는 모델이 소프트웨어 복잡도 모델이다. 본 논문은 이런 관점에서 대표적인 소프트웨어 복잡성 모델에 대한 적용법에 대해 기술하고 개발중인 교환시스템의 소프트웨어에 대해 volume metrics와 process complexity metrics 방법에 대한 분석 결과와 기타 시스템 개발을 수행하는 과정에서 발생되고 있는 문제점들에 대해 다각도로 분석을 하여 이를 연구개발 및 프로젝트 관리에 활용하고자 한다.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1345-1357
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• 2017

A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.269-274
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• 2016

In this paper We focuses on suggestion to quality model for IoT infrastructure services for Internet of Things. Quality model is suggested on security set out in ISO25000 quality factors and assessment of the existing traditional software application of ISO 9126 quality model. We validated that the proposed model can be realized it was applied to evaluate the 4 elements and related security in Metrics.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.71-75
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• 2018

Image enhancement is a necessary end essential step after taking a picture with a digital camera. Many different photo software packages attempt to automate this process with various auto enhancement techniques. This paper provides and implements a system that can learn a user's preferences and apply the preferences into the process of image enhancement. Five major components are applied to the implemented system, which are computing a distance metric, finding a training set, finding an optimal parameter set, training and finally enhancing the input image. To estimate the validity of the method, we carried out user studies, and the fact that the implemented system was preferred over the method without learning user preferences.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.17-23
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• 2023

Recently, several high-performance instance segmentation models have used the Mask R-CNN model as a baseline, which reached a historical peak in instance segmentation in 2017. There are numerous derived models using the Mask R-CNN model, and if the performance of Mask R-CNN is improved, the performance of the derived models is also anticipated to improve. The Mask R-CNN uses interpolation to adjust the image size, and the input differs depending on the interpolation method. Therefore, in this study, the performance change of Mask R-CNN was compared when various interpolation methods were applied to the transform layer to improve the performance of Mask R-CNN. To train and evaluate the models, this study utilized the PennFudan and Balloon datasets and the AP metric was used to evaluate model performance. As a result of the experiment, the derived Mask R-CNN model showed the best performance when bicubic interpolation was used in the transform layer.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.980-990
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• 2004

The component-based development methodology aims at the high state of abstraction and the reusability with components larger than classes. It is indispensible to measure the component so as to improve the quality of the component-based system and the individual component. And, the quality of the component should be improved through putting the results into the process of the development. So, it is necessary to study the component metric which can be applied in the stage of the component analysis and design. Hence, in this paper, we propose component cohesion, coupling, independence metrics reflecting the information extracted in the step of component analysis and design. The proposed component metric bases on the similarity information about behavior patterns of operations to offer the component's service. Also, we propose the redesigning process for the improvement of component design. That process uses the techniques of clustering and is for the thing that makes the component as the independent functional unit having the low complexity and easy maintenance. And, we examine that the component design model can be improved by the component metrics and the component redesigning process.

• Journal of KIISE:Software and Applications
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• v.26 no.9
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• pp.1095-1104
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• 1999

객체지향 시스템의 개발은 클래스를 통해서 이루어진다. 즉, 문제 영역에 존재하는 중요한 대상 또는 개념을 클래스로 모델링하고, 이로부터 생성된 객체들 사이의 메시지 교환을 통해서 시스템은 구축된다. 또한, 클래스는 정보 은닉을 제공함으로써, 객체지향 시스템의 재사용성과 유지보수성에 상당한 기여를 한다. 그러나, 설계 단계에서 실세계의 대상을 부적절하게 모델링하거나, 또는 유지보수 단계에서 클래스에 무분별한 변경을 가하는 경우 클래스의 품질은 악화될 수 있고, 이는 결국 시스템을 유지보수 하거나 확장하는데 상당한 장애를 초래한다.응집도는 모듈의 구성 요소들 사이의 연관성 정도를 나타내는 척도로서 전통적으로 모듈의 품질을 평가하기 위한 기준으로 사용되어 왔다. 이 논문에서는 클래스의 품질을 평가하는 방법으로서의 클래스 응집도를 제안한다. 즉, 클래스가 실세계의 대상을 적절하게 모델링한다면, 그 구성요소들 사이에 밀접한 관련이 있고 결국 높은 응집도를 가지게 될 것이다. 반대로 실세계의 대상에 대한 적절한 모델이 아니라면, 그 클래스의 구성 요소들 사이에는 밀접한 관련성이 없을 것이고 따라서 낮은 응집도를 보일 것이다.Abstract Object-oriented systems are developed by means of classes; that is, classes captures the essential entities or concepts in the problem domain, and the system is embodied by the interactions of objects instantiated from the classes. In addition to the basic units of object-oriented systems, classes serves as the units of encapsulation, which considerably promote the modifiability and the extensibility of them. However, improper modeling in the design phase or uncontrolled changes during the maintenance phase can degrade the quality of classes, which leads to systems cumbersome to maintain and extend.Cohesion refers to the degree of connectivity among the elements of a single module, and is being used as a factor which characterizes the quality of a module. In this paper, we propose a new cohesion metric for assessing the quality of classes. If a class captures properly the essential features of objects, the members of the class surely have strong relationship among them. On the contrary, the poor relationship among class members can indicate that the class is not a proper model of objects.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.407-416
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• 2001

In this paper we propose a suite of metrics $\pi_{java}$ Java/, for evaluating the complexity of Java Programs based on a suite of metrics $\pi_{java}$ c++/, which we previously presented for C++ programs. So far, a lot of metrics for C++ are proposed for C++ programs. But since the specific properties of Java programs are not explicitly considered in those metrics, it is hard to apply them to Java programs. Thus we aim to develop a metric suite that is applicable to Java Programs. At first, we decide if any properties are commonly possessed by both C++ programs and Java programs, or not. For example, the multiple inheritance of the class in C++ is not implemented in Java. On the other hand, the features such as package and interface are newly implemented in Java, and therefore we cannot discuss the complexity of Java programs without considering these new features. Then we define a new suite of metrics $\pi_{java}$ Java/ for Java programs by deleting 3 metrics $\pi_{java}$/c++/, and then incorporating 3 metrics which are newly defined or modified for Java programs to $\pi_{java}$ c++/. Finally, we analytically evaluate the new metric with regard to Weyuker's measurement principles and also compare it with conventional metrics for Java.