• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 춘계학술발표대회
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• pp.714-717
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• 2008

유비쿼터스 컴퓨팅(Ubiquitous Computing) 시대에 기업의 사무 환경은 다양한 정보들과 많은 사용자들이 유기적인 관계를 형성한다. 이러한 관계에서 접근 제어는 다양한 정보 객체에 허가된 사용자만이 접근할 수 있는 권한을 갖는 기능을 제공하는 것이고, 사무 환경에서 보안상 필수적이며 중요한 역할을 한다. 하지만 기존의 접근 제어 모델들은 상황 정보를 고려하지 않아 동적인 접근 제어를 하지 못하는 문제점을 가지고 있다. 본 논문은 워크플로우 기반의 오피스 환경에서 동적이고 능동적인 접근제어 관리를 제공하기 위한 상황 정보와 역할 기반의 워크플로우 데이터 접근제어 모델을 제안한다. 이 모델은 수많은 상황 정보 및 사무 정보와 사용자가 동적으로 변화하는 사무환경에서 사용자에게 접근을 제어하기 적합하다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권10호
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• pp.1276-1285
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• 2004

본 논문에서는 웹 브라우저를 통하여 모바일 기기에서 수행하도록 개발한 소프트웨어 컨텐츠의 모바일 적합성을 분석하는 시스템의 구현을 위한 모델을 제시한다. 컨텐츠의 모바일 적합성은 컨텐츠가 모바일 기기에서 실행되기에 적절하도록 개발되었는가를 말하는 것으로, 아무리 좋은 컨텐츠라도 모바일 기기에서 실행되기에 부적절하다면 서비스를 할 수 없으므로 컨텐츠의 모바일 적합성에 대한 분석은 매우 중요하다. 본 논문에서는 컨텐츠의 모바일 적합성 분석 범주를 제시하여 컨텐츠의 모바일 적합성 분석을 위한 가이드로 사용할 수 있도록 하고, Factory Method 패턴과 Facade 패턴을 바탕으로 모델을 만들고, 모델의 각 클래스간의 협력 관계를 통하여 컨텐츠의 모바일 적합성 분석을 위한 작업 수행 흐름을 보여 준다. 또한 모델의 구현을 위해 필요한 최소한의 자바 API를 제시하여 자바를 이용한 시스템의 구현에 사용할 수 있도록 하였으며, HDML로 작성된 컨텐츠의 모바일 적합성 분석 시스템을 구현하여 활용하는 사례를 보였다. 본 논문에서 제시한 모델은 새로운 종류의 컨텐츠에 대한 분석 모듈을 추가하거나 기존의 모듈을 제거하는 것을 쉽게 할 수 있도록 유연성을 가진 모델이며, JSP와 자바 빈을 기반으로 설계된 모델이므로, EJB나 다른 기술을 이용하여 개발할 필요가 있다면 모델을 확장시켜 시스템을 구현할 수도 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Biotechnology and Bioprocess Engineering:BBE
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• 제10권5호
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권6호
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• pp.285-292
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• 2017

전장공간은 다양한 무기체계가 유기적으로 활용되고 있으며, 자원의 성능 및 통신환경이 제약되고 환경의 가변성이 높은 특징이 있다. 이러한 전장환경에서 운용되는 자원의 접근성과 통합성의 한계를 극복하고 유연한 조합을 통해 임무를 수행할 수 있는 방안으로 서비스 지향 구조 기반의 기술을 접목시키는 연구들이 진행되고 있다. 본 연구에서는 지휘관이 전장 환경에서 임무에 필요한 다양한 SW를 서비스 기반으로 운용하기 위해 서비스 기반의 임무 계획 및 수행을 지원하는 계층형 임무서비스 모델을 제안한다. 또한 워크플로우 기술과 시맨틱 능력기반 추천을 활용한 개발도구를 구현하고 전투 임무 시나리오에 적용하여 실효성을 보이고자 한다.

• Genomics & Informatics
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• 제11권1호
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• pp.46-51
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• 2013

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.1007-1014
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• 2003

It is important to integrate an enterprise application for automating of the business profess, which is responded by a flow of market environment. There are two categories of method that integrate enterprise applications. One is Synchronous Integration, and the other is Asynchronous Integration. EAI(Enterprise Application Integration) and Web service which of the asynchronous integration is focused in the automating method of the business process. After we construct the application integration for automating of the business process, we have to concern about managing of the business transaction. Many Organizations have proposed the process method of business transaction based on 2-phase commit protocol. But this method can′t supply the phase that classify the transaction by transaction weight. In this paper, we propose an efficient method of transaction process for business transactions, which is composed by ′Classify Phase′ that classify transactions. We called this model "3-Phase Commit Method Applied by Classify Phase", we design this model to manage an resource of enterprise efficiently. The proposed method is compared by the method based on 2-Phase commit that could be a problem of management the resource of enterprise, and the advantage of this method is certified to propose the solution of that problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1217-1233
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• 2017

Effective monitoring and testing during each step are essential for document verification in research and development (R&D) projects. In software development, proper testing is required to verify it carefully and constantly because of the invisibility features of software. However, not enough studies on test processes for R&D projects have been done. Thus, in this paper, we introduce a Test Maturity Model integration (TMMi)-based software field R&D test process that offers five integrity levels and makes the process compatible for different types of projects. The Software & Systems Process Engineering Metamodel (SPEM) is used widely in the software process-modeling context, but it lacks built-in enactment capabilities, so there is no tool or process engine that enables one to execute the process models described in SPEM. Business Process Model and Notation (BPMN)-based workflow engines can be a solution for process execution, but process models described in SPEM need to be converted to BPMN models. Thus, we propose an approach to support enactment of SPEM-based process models by converting them into business processes. We show the effectiveness of our approach through converting software R&D test processes specified in SPEM in a case study.

• Korean Journal of Radiology
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• 제24권7호
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권3호
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• pp.125-138
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• 2016

서비스 지향 개발에서 서비스 식별은 워크플로우, 목표와 시나리오, 유스케이스, 컴포넌트, 휘처, 패턴 등에 기반해서 이루어져 왔다. 그러나, 비즈니스 가치 관점에서 의미적 접근에 의한 서비스의 식별은 아직 구체화되어 있지 않다. 본 논문은 비즈니스 서비스 식별의 정확성을 향상시키기 위하여, XL-BPMN 모델 대상의 구조적 및 의미적 분석에 의한 비즈니스 서비스를 식별하는 방법을 제시한다. 비즈니스 시나리오에 기반해서 비즈니스 프로세스들을 식별하고, 이 프로세스는 XL-BPMN 비즈니스 프로세스 모델로 디자인한다. 이 비즈니스 프로세스 모델에서, 액티비티들간 구조적 패턴과 속성 기반 의미적 유사성의 통합된 분석 결과에 의해 밀접한 액티비티를 바인딩해서 단위 비즈니스 서비스를 식별한다. 이를 통해, 상위 비즈니스 가치 관점의 XL-BPMN 모델을 통한 정확성과 모듈성이 높은 단위 비즈니스 서비스 식별을 할 수 있다. 식별된 서비스의 재사용을 통해서 서비스 지향 개발을 더욱 가속화를 도모할 수 있을 것이다.