• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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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.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1276-1285
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• 2004

A web-based model for implementation of mobile compliance analysis system for software contents is proposed. It is said that the content is compliant if the content can be executed properly in mobile environment. The mobile compliance analysis of contents is very important because contents can't be serviced if they arc not proper for mobile environment. The categories of mobile compliance analysis for software contents are proposed. The model of this paper uses the Factory Method pattern and the Facade pattern. The workflow of the s${\gamma}$stem is described through collaborations of classes in the model. As a case study, core Java APIs for implementation of the model arc represented and the mobile compliance analysis system for HDML contents has been built. The model is flexible so that it is easy to add new modules or remove some modules for contents analysis. Although the model is based on the JSP and Java beans, it can be expanded to support any other programming technique such as EJB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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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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• v.10 no.5
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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.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.285-292
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• 2017

There are technological, operational and environmental constraints at tactical edge, which are disconnected operation, intermittent connectivity, and limited bandwidth (DIL), size, weight and power (SWaP) limitations, ad-hoc and mobile network, and so on. To overcome these limitations and constraints, we use service-oriented architecture (SOA) based technologies. In our research, we propose a hierarchical mission service model that supports service-oriented mission planning and execution in order for a commander to operate various SW required for mission in battlefield environment. We will also implement development tools that utilize the workflow technology and semantic capability-based recommendation and apply them to combat mission scenarios to demonstrate effectiveness.

• Genomics & Informatics
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• v.11 no.1
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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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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• v.11 no.2
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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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• v.24 no.7
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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.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.125-138
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• 2016

The service identification in service-oriented developments has been conducted by based on workflow, goals, scenarios, usecases, components, features, and patterns. However, the identification of service by semantic approach at the business value view was not detailed yet. In order to enhance accuracy of identifying business service, this paper proposes a method for identifying business service by analyzing syntax and semantics in XL-BPMN model. The business processes based on business scenario are identified, and they are designed in a XL-BPMN business process model. In this business process model, an unit business service is identified through binding closely related activities by the integrated analysis result of syntax patterns and properties-based semantic similarities between activities. The method through XL-BPMN model at upper business levels can identify the reusable unit business service with high accuracy and modularity. It also can accelerate more service-oriented developments by reusing identified services.