• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.5A
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• pp.752-762
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• 2001

소프트웨어의 유지보수 과정에서 효율적인 복잡성 척도와 테스트 경로의 생성은 중요한 문제이다. 대부분의 경우 테스트 경로의 생성과 복잡성 척도의 측정은 독립적인 기법이 필요하다. 본 논문에서는 테스트 경로의 생성과 복잡성 척도를 통합적으로 생성하고 있다. 제안한 기법은 PUT(Program Under Test)를 확장한 페트리네트 그래프(EPG)를 이용하여 모델링하고 이것의 통합적인 분석을 통하여 테스트 경로를 생성하며, 이 과정에서 생성된 실행경로의 제어구조별 평균 발생 빈도수를 이용하여 복잡성 척도 EV(G)를 구하였다. EV(G)는 실제 프로그램의 실행경로에 기반을 두었기 때문에 프로그램의 제어구조별 차이점 외에도 프로그램의 동작 특성을 복잡도에 잘 반영할 수 있다. 본 논문에서 제안한 통합 기법에 의한 테스트 경로 생성 기법과 복잡성 척도를 소프트웨어의 유지보수에 활용한다면 노력과 비용의 절감 및 소프트웨어의 질적 향상을 가져올 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.8
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• pp.1913-1925
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• 2012

In order to efficiently utilize limited radio resources, resource allocation schemes in OFDMA-based wireless networks have gained intensive attention recently. Instead of improving the throughput performance, the utility is adopted as the metric for resource allocation, which provides reasonable methods to build up the relationship between user experience and various quality-of-service (QoS) metrics. After formulating the optimization problem by using a weighted bipartite graph, a modified bipartite matching method is proposed to find a suboptimal solution for the resource allocation problem in OFDMA-based wireless systems with feasible computational complexity. Finally, simulation results are presented to validate the effectiveness of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.110-113
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• 2008

시스템의 복잡도를 정의하고 측정하는 일은 보다 신뢰성있고 효율적인 시스템을 만들기 위한 초석이다. 복잡도가 증가함에 따라 복잡도에 영향을 받는 안전필수 시스템의 안전성을 정의하고 측정할 메트릭스가 필요성도 증가하고 있다. 본 논문에서는 임베디드 시스템의 복잡성과 안전성을 고찰하고, 안전성의 성질을 서술하는 복잡도 메트릭을 제안한다.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.560-567
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• 2020

The complexity of safety critical systems of Nuclear Power Plant continues to increase rapidly due its transition from analog to digital systems. It has thus become progressively more imperative to model these systems prior to their implementation in order to meet the high performance, safety and reliability requirements. Timed Petri Nets (TPNs) have been widely used to model such systems for non-functional analysis. The paper presents a novel methodology for the analysis of the performance metrics using PN modeling. The paper uses the isomorphism property of the TPNs and the Markov chains for the performance analysis of the safety critical systems. The presented methodology has been validated on a Shutdown System of a Nuclear Power Plant.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.352-357
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• 2008

We present a space-time trellis coded OFDM system in slow fading channels. Generalized principal ratio combining (GPRC) is also analyzed theoretically in frequency domain. The analysis shows that the decoding metric of GPRC includes the metrics of maximum likelihood(ML) and PRC. The computer simulations with M-PSK modulation are obtained in frequency flat and frequency selective fading channels. The decoding complexity and simulation running times are also evaluated among the decoding schemes.

• Journal of KIISE:Software and Applications
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• v.27 no.12
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• pp.1141-1154
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• 2000

본 논문에서는 Java 프로그램의 복잡도를 측정하기 위해 필요한 인자들을 제안하였다. 이러한 인자들을 추출하기 위해 Java 프로그램을 분석하여 객체지향 설계 척도 값들을 계산하고 통계적 분석을 수행하였다. 그 결과 기존의 연구에서 발견되었던 클래스의 크기 인자 외에도 메소드 호출 빈도, 응집도, 자식 클래스의 수, 내부 클래스 및 상속 계층의 깊이가 주요 인자임이 파악되었다. 클래스의 크기 척도로 분류되었던 자식 클래스의 수는 다른 크기 척도들과 다른 성질을 가진다는 것을 발견하였다. 또한 프로그램의 크기가 커지고 결합도가 높아질수록 응집도가 떨어진다는 것을 입증하였다. 그리고 인자 분석을 바탕으로 인간의 인지 능력과 인자의 상관관계를 고려한 가중치를 적용하기 위해 인자별로 회귀분석을 수행하였다. 보다 적은 척도를 가지고 인자를 설명할 수 있는 회귀식을 도출하였다. 두 그룹에 대한 교차 검증 결과 회귀식이 높은 신뢰도를 가지는 것으로 나타났다. 따라서 본 논문에서 제안한 인자들을 이용하는 경우 Java 프로그램의 복잡도를 측정할 수 있는 새로운 척도로 사용할 수 있다.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.404-414
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• 2016

Autonomy of unmanned systems is important because the unmanned system with high level of autonomy is able to perform desired tasks in unstructured environments without continuous human guidance. Evaluation of their autonomy is vital to realize the autonomous operation ability of unmanned system. Compared to the methods of evaluating the level of autonomy(LOA) for an unmanned ground vehicle(UGV) and unmanned aerial vehicle(UAV), the method of expressing the LOA of unmanned maritime system(UMS) is not established yet. Since UMS has a unique characteristics in terms of operational area, mission complexity and required technologies, compared to the UGV and UAV, it is required to establish for expressing the LOA for UMS. This paper reviews the current approaches to assess the LOA of unmanned system and proposes potential metrics for UMS in order to determine the autonomy levels of UMS.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.605-607
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• 2023

Recent research has highlighted the effectiveness of Deep Learning (DL) techniques in automating the detection of lung sound anomalies. However, the available lung sound datasets often suffer from limitations in both size and balance, prompting DL methods to employ data preprocessing such as augmentation and transfer learning techniques. These strategies, while valuable, contribute to the increased complexity of DL models and necessitate substantial training memory. In this study, we proposed a streamlined and lightweight DL method but effectively detects lung sound anomalies from small and imbalanced dataset. The utilization of 1D dilated convolutional neural networks enhances sensitivity to lung sound anomalies by efficiently capturing deep temporal features and small variations. We conducted a comprehensive evaluation of the ICBHI dataset and achieved a notable improvement over state-of-the-art results, increasing the average score of sensitivity and specificity metrics by 2.7%.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.66-74
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• 2008

Criticality prediction models to identify most fault-prone modules in the system early in the software development process help in allocation of resources and foster software quality improvement. Many models for identifying fault-prone modules using design complexity metrics have been suggested, but most of them are training models that need training data set. Most organizations cannot use these models because very few organizations have their own training data. This paper builds a prediction model based on a well-known supervised learning model, error backpropagation neural net, using design metrics quantifying SDL system specifications. To solve the problem of other models, this model is trained by generated virtual training data set. Some simulation studies have been performed to investigate feasibility of this model, and the results show that suggested model can be an alternative for the organizations without real training data to predict their software qualities.

• The Transactions of the Korea Information Processing Society
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• v.2 no.2
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• pp.185-198
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• 1995

The development of automated quality evaluation tool is desperately needed to decrease the cost of maintenance, to measure quality of source program in developing course for the purpose of developing high quality software. Main goal in this paper is to develop the automated tool for software quality evaluation related with high quality and high reliability of software. In this paper, we proposed the four software quality model to evaluate software quality; functional -size model, understandability model, complexity model and object-oriented model. Also, we made a scanner and a parser to analyze the c and C＋＋ source program and to produce the metrics and function value. The measurement value is statistically analyzed for the distribution of the measurement value. we can extracted the characteristics of measurement value and this one is assisted to make scores of software quality evaluation. Finally, we made a software quality evaluation tool to support software evaluation activities.