• Asia pacific journal of information systems
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• v.25 no.1
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• pp.25-60
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• 2015

Use cases are commonly used to represent customer requirements during systems development. In a large software development environment, finding relevant use cases from a library of past or related projects is a complex, error-prone and expensive task. This study proposes an ontological methodology to support use case retrieval in an interactive manner. The architecture of a prototype system that implements this methodology is presented. To evaluate whether the proposed approach can provide satisfactory results to users, this study develops a research model and hypotheses based on interaction theory. These hypotheses are empirically tested using a laboratory experiment which controls information filtering and perceived interaction. Our study suggests that a system which interacts with a user intelligently reduces cognitive load and increases self-efficacy and satisfaction.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.415-417
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• 2001

HLA(High Level Architecture)는 차세대 DIS(Distributed Interactive Simulation)를 이어 네트워크 분산 시뮬레이션의 기반이 되는 표준 아키텍쳐이다. 이 HLA 시스템의 개발 과정에서 FEDEP(Federation Development and Execution Process) Model이 제안되었는데, FEDEP의 목적은 federation 개발자가 application의 요구를 충족시킬 수 있도록 federation 개발 및 실행에 대한 guideline을 정하는 것이다. FEDEP의 일련의 process들 중에서 노동집약적인 과정은 CASE(Computer Aided Software Engineering) tool을 사용함으로써 보다 강화되고 능률적으로 이루어질 수 있다. 본 논문에서 소개하는 HLA Application Builder 는 federate 와 federation을 자동적으로 생성하는 CASE tool 로서, 자동적인 FED(Federation Execution Data)file 및 C++ source code를 생성함으로써 HLA Federation 개발에 있어서의 인력(manpower)을 크게 줄일 수 있다. 본 논문에서는 HLA에 대한 배경지식과 HLA Application Builder 개발의 필요성과 구현, 그리고 실제 예를 들어서 HLA Application Builder가 어떻게 federation 개발에 사용되는지에 대해서 설명한다.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1119-1130
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• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.273-275
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• 2022

Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.634-639
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• 2013

The aim of the study is to develop the 3D visualization of Building Information Model and integrated 4D model for optimization of resources in the construction project. This study discuss the process of methodology and creation of 4D model of the project and simulate it to monitor the workflow at the site. Different stages of the construction process and activities are generated by using Revit and MS Project. MS project has been used for creation of the schedules and these are linked with the Revit for 3D modeling. The time used as the fourth dimension and 4D model created by using Navisworks Time liner software. Narges shopping center is presented as a case study to realize the actual uses and benefits of Building Information Model (BIM). Narges shopping mall is located in Tehran, Iran. As a part of Hekmat master plan, Narges shopping center is an 11 stores building with a total area of 30000 Sq.m. This shopping and entertainment center is comprised of 150 retails and two multi-use public halls with a capacity of 400 persons each and underground parking with total 400 parking space. The main purpose of architecture was to create an urban public center along with its revolving, spiral like form and an ever changing continuous façade by means of different colors, materials, which is in harmony with the other building of the master plan. The approximate cost of the project is $17 million and duration of the project schedule is 30 months. The developed Building Information Model enabled us to identify the potential collisions or clashes between various structural and architectural systems. 4D model has been used for limiting the interaction between subcontractors installing the different systems so rework could be avoided and productivity maximized. It is also observed that the utility of BIM for construction stimulation and clash detection is the best suitable method. Clash detection before the implementation of work is highly recommended to avoid rework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3474-3497
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• 2016

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2857-2866
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• 2000

As software technology has made progress, object-oriented CASE tools have become more important. This paper presents the process and similarity in design and implementation of OODesigner, an object-oriented CASE tool, on three platfonns and outlines a kind of generic architecture for the design and the implementation of CASE tools. OODesigner is a tool that was initially developed to support OMT. An initial Unix version has been developed since 1994. In 1997, after the completion of the Unix version, we began developing a Java version and a Windows version supporting UML. The development of a CASE tool is a typical application of the Model-View-ControllerO'vIVC) paradigm. Thus, we obtained a common design pattern among the versions in the MVC point of views. This design similarity can be used to develop several kinds of CASE tools with the corresponding design notations.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.