• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.281-284
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• 2004

본 연구는 컴포넌트 재사용을 효과적으로 수행하기 위해 사용자 피드백을 지원할 수 있는 검색 시스템을 제안하였다. 컴포넌트 검색을 위해 퍼지 함수를 이용한 신뢰값을 사용하였으며, 사용자 집단의 요구에 능동적으로 반응할 수 있도록 퍼지 함수를 변화시켜 컴포넌트의 검색 우선순위를 변경시키는 방법을 제안하였다. 컴포넌트의 행위적 특성에 따른 검색은 응용 도메인에 따른 소프트웨어의 재사용에 매우 효과적이다. 본 연구는 후보 컴포넌트들 중 사용자가 어떤 컴포넌트를 선택하느냐에 따라 시스템이 유연하게 반응할 수 있는 적응형 검색 방법이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1282-1285
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• 2022

기존의 video domain adaptation은 closed set 환경에서 주로 연구되었다. 하지만 이는 source와 target의 label이 같다는 비현실적인 전제를 요구한다. 따라서 본 논문에서는 target의 label space가 source보다 넓은 open set video domain adaptation 문제를 다룬다. 우린 open set image domain adaptation에서 사용되는 방법들을 video로 확장 시켜 모델을 설계하고 UCF to HMDB, HMDB to UCF 와 같은 video dataset에서 실험하였다. 그 결과 source only 대비 UCF to HMDB에서 12%, HMDB to UCF 7.1% 향상된 결과를 얻었다.

• Journal of KIISE
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• v.42 no.12
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• pp.1503-1521
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• 2015

Agent-based modeling and simulation (ABMS) is used to model of market and social phenomena by utilizing agents' fine-grained behaviors and interactions that cannot be implemented in a conventional simulation. However, ABMS represents irrational agents and hinders the achievement of individual or overall goals since ABMS is based on agent-based software, which follows the principle of rationality at the knowledge level [1]. This problem was solved in the agent-based software engineering (ABSE) field by using behavior laws for the social level [2]. However, they still do not propose the specific development methodology for how to develop the social level in a systematic way. Therefore, in order to propose agent-based modeling and simulation methods that reflect the behavior laws of social level characteristics, our study used the Tropos that can combine ABSE and social behavior laws for the presentation of concrete tasks and deliverables for each development step by step. In addition, the proposed method will be specified through experiments with specific application examples and case studies on the self-adaptive smart grid and the military domain system.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.29-37
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• 2020

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system. In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques. In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention. Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance. Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system. Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data. In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data. The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11B
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• pp.945-952
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• 2006

In hierarchical mobile IPv6 networks, when an inter-domain handover occurs, mobile nodes suffer from excessive signaling traffic and long handover latency, resulting in possible disruption of ongoing connections. Further, the selection of MAP and its load status critically affect the overall system performance. Therefore, we propose a dynamic MAP selection scheme that seeks to distribute load among MAPs as well as reduces inter-domain handovers. Performance is evaluated from not only an analytic model of average signaling cost but also simulation. The analytical and simulation results show that our proposed scheme improves load distributedness and reduces inter-domain handovers and signaling cost compared to another existing IETF based approach.

• Journal of KIISE:Information Networking
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• v.32 no.5
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• pp.625-632
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• 2005

Recently, real time services have been demanding a lot and the number of mobile devices is increasing extremely. Many researchers are focusing on decreasing handoff or signaling cost, produced when mobile devices are moving around. With these efforts, HMIPv6(Hierarchical Mobile Internet Protocol Version 6) was proposed. Mobile nodes do not need to register their locations to Home Agents whenever crossing over subnets within a MAP domain. In HMIPv6, mobile nodes choose the farthest MAP without considering node mobility pattern. However, a large MAP domain is not always efficient for a slow moving node and required additional work to choose a MAP in HMIPv6. Hence, this paper proposes 'Performance Analysis of HMIPv6 applying adaptive MAP Domain Site'.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.19-28
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• 2011

Adaptive Random Testing (ART) aims to enhance the performance of pure random testing by detecting failure region in a software. The ART algorithm generates effective test cases which requires less number of test cases than that of pure random testing. However, all ART algorithms currently proposed are designed for the tests of monolithic system or unit level. In case of integrated system tests, ART approaches do not achieve same performances as those of ARTs applied to the unit or monolithic system. In this paper, we propose an extended ART algorithm which can be applied to the integrated system testing environment without degradation of performance. The proposed approach investigates an input distribution of the unit under a test with limited number of seed input data and generates information to be used to resizing input domain partitions. The simulation results show that our approach in an integration environment could achieve similar level of performance as an ART is applied to a unit testing. Results also show resilient effectiveness for various failure rates.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1010-1026
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• 2004

The goal of product line engineering is to support the systematic development of a set of similar software systems by understanding and controlling their common and distinguishing characteristics. The product line engineering is a process that develops reusable core assets and develops a set of software-intensive systems from a common set of core assets in a prescribed way. Currently, many software development technologies are accomplished in context of product line. However, much of the product line engineering research have focused on the reuse of work products relating to the software's architecture, detail design, and code. The product lines fulfill the promise of tailor-made systems built specifically for the needs of particular customers or customer groups. In particular, commonality and variability play central roles in the all product line development processes. These must be treated already during the requirement analysis phase. Requirements in product line engineering are basis of software development just like as traditional system development engineering, and basis of deciding other core assets' property - commonalities and variabilities. However, it is difficult to elicit, analyze and manage correct requirements. Therefore, it is necessary to develop systematic methods which can develop and manage requirement as core asset, which can be stable in anticipative change and can be well adapted to unpredictable change. In this paper, we suggest a method of managing requirements as core asset in product line. Through this method, the reuse of domain requirements can be enhanced. As a result, the cost and time of software development can be reduced and the productivity can be increased.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.412-414
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• 2005

유비쿼터스 컴퓨팅 환경의 실현 가능성이 높아지면서 사용자가 존재하는 장소와 그곳의 환경에 따른 맞춤 서비스의 제공이 요구된다. 이러한 맞춤 서비스를 제공하기 위해서는 사용자 주변 환경을 인지 및 판단하여 서비스를 제공하는 소프트웨어가 필요하다. 소프트웨어가 환경을 인지하여 처리하려면 환경은 소프트웨어가 이해할 수 있도록 모델링 되고 언어로 표현되어야 한다. 기존의 Context 모델링 방법은 특정 상황에 초점이 맞추어져있어 다른 상황에의 적용이 쉽지 않다. 본 논문에서는 다양한 도메인에 적용 가능한 다단계 상황 모델링 방법, 이를 ontology언어 OWL을 사용하여 나타내는 방법, 이것을 적용하기 위한 적응형 소프트웨어 개발 framework를 제시한다.