• 한국지능시스템학회논문지
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• 제17권5호
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• pp.684-690
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• 2007

본 논문은 확률변수들로 이루어진 클러스터의 집합과 확률변수들에 대해 관찰된 데이터가 주어진 상황에서, 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성(directional tendency of conditional dependence in the Bayesian probabilistic graphical model)을 결정하는 방법을 기술한다. 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성을 추정하기 위해 한 클러스터에서 다른 각 클러스터에 가장 가까운 확률변수를 해당 클러스터의 외부연결변수로 결정한다. 외부연결변수들 사이에서의 가장 확률이 높은 조건부 확률적 의존성을 나타내는 방향성 비순환 그래프(directed acyclic graph(DAG))를 찾음으로써, 주어진 클러스터들 사이에 존재하는 조건부 확률적 의존의 방향성을 결정한다. 사용된 방법이 클러스터 사이에 존재하는 조건부 확률적 의존의 방향성을 유의미하게 추정할 수 있음을 실험적으로 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

• 한국멀티미디어학회논문지
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• 제21권11호
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• pp.1305-1316
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• 2018

Graphical user interface(GUI) has a very important role to interact with software users. However, designing and coding of GUI are tedious and pain taking processes. In many studies, the researchers are trying to convert GUI elements or widgets to code or describe formally their structures by help of domain knowledge of stochastic methods. In this paper, we propose the GUI elements detection approach based on object detection strategy using deep neural networks(DNN). Object detection with DNN is the approach that integrates localization and classification techniques. From the experimental result, if we selected the appropriate object detection model, the results can be used for automatic code generation from the sketch or capture images. The successful GUI elements detection can describe the objects as hierarchical structures of elements and transform their information to appropriate code by object description translator that will be studied at future.

• Steel and Composite Structures
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• 제31권2호
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• pp.201-216
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• 2019

This scientific paper provides a theoretical, numerical and experimental analysis of local stability of axially compressed columns made of thin-walled rectangular concrete-filled steel tubes (CFSTs), with the consideration of initial geometric imperfections. The work presented introduces the theory of elastic critical stresses in local buckling of rectangular wall members under uniform compression. Moreover, a numerical calculation method for the determination of the critical stress coefficient is presented, using a differential equation for a slender wall with a variety of boundary conditions. For comparison of the results of the numerical analysis with those collected by experiments, a new model is created to study the behaviour of the composite members in question by means of the ABAQUS computational-graphical software whose principles are based on the finite element method (FEM). In modelling the analysed members, the actual boundary and loading conditions and real material properties are taken into account, obtained from the experiments and material tests on these members. Finally, the results of experiments on such members are analysed and then compared with the numerical values. In conclusion, several recommendations for the design of axially compressed composite columns made of rectangular concrete-filled thin-walled steel tubes are suggested as a result of this comparison.

• 한국군사과학기술학회지
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• 제24권4호
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• pp.408-417
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• 2021

This paper describes the quality attributes of an integrated simulation software for weapon systems named Advanced distributed simulation environment(AddSIM). AddSIM is developed as a key enabler for Defense Modeling & Simulation(M&S) systems which simulate battlefields and used for battle experiments, analyses, military exercises, training, etc. AddSIM shall provide a standard simulation framework of the next Defense M&S systems. Therefore AddSIM shall satisfy not only functional but also quality requirements such as availability, modifiability, performance, testability, usability, and others. AddSIM consists of operating softwares of hierarchical components including graphical user interface, simulation engines, and support services(natural environment model, math utility, etc.), and separated weapon system models executable on the operating softwares. The relation between software architectures and their quality attributes are summarized from previous works. And the AddSIM architecture and its achievements in the aspect of quality attributes are reviewed.

• 전기전자학회논문지
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• 제25권2호
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• pp.285-290
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• 2021

기존의 조준 감사는 외국에서 수입한 조준 감사기재를 사용하는 실정이다. 하지만 그 수량이 매우 부족해서 조준 감사에 많은 시간이 소요되고 유지보수가 어렵다. 때문에 시스템을 국산화 시켜 조준 감사시간을 줄이고 유지보수와 보급을 원활하게 하는 것이 목적이다. 본 논문에서는 표적 탐지 딥러닝 모델을 통해 표적을 탐지하고 사격 결과에 대한 모니터링이 가능한 시스템을 개발하였다. 이 시스템은 표적의 실시간 탐지가 가능하고 먼 표적에 대한 여러 전처리를 통해 식별률을 크게 상승시켰다. 또한 사용자 인터페이스를 구성하여 사용자의 카메라 조작과 훈련결과 데이터의 저장 및 관리를 용이하게 하였다. 이 시스템으로 현재 사용되고 있는 조준 감사기재와 비사격 훈련을 대체할 수 있다.

• The Journal of Asian Finance, Economics and Business
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• 제10권2호
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• pp.49-60
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• 2023

The study attempts to examine the relationship among revenue growth factors from different angles and provides a comprehensive overview of tax revenue collection for developing countries. The impact of income tax, customs duty, and value-added tax on the gross domestic product is examined using the ordinary least-square (OLS) multiple regression approach. To confirm the association, a multiple regression model is applied to time-series data. SPSS software, MS Excel, is used to draw the empirical results, trend analysis, and some graphical presentation to reach the study's objective. The findings show that while the value-added tax has a significant impact and the highest coefficient, regardless of country, income tax and customs duty may or may not be significant depending on the circumstances. It triggers effectual and efficacious economic growth. The paper has implications in policy-making areas where governments are seeking how to stimulate revenue growth effectively and efficiently. To promote economic growth, the tax net and tax rate on luxury goods should be increased along with human resources in the tax administration for the short term. But in the long term, decentralization & digitization of tax administration, dismantling the existing tax barriers and good governance are necessary.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.129-129
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• 2023

연안 환경은 기후 및 도시개발과 같은 자연·인공적 요인에 따라 끊임없이 변화한다. 근래에는 연안 도시 인구증가, 기후변화 등의 영향으로 인해 그 변화가 가속화되고 있으며, 특히 연안 침식 및 이에 따른 해안선 변화에 대한 심각성이 대두되고 있다. 연안 침식은 해류와 해안 유사의 마찰로 발생하는 유사이송 현상으로 야기되며, 해안 환경을 변화를 초래하며 인간사회에 경제적인 피해를 주기도 한다. 연안침식이 사회적인 문제로 부상했음에도 여전히 이에 대한 대중적 문제의식은 부족한 실정이다. 이는 대중매체를 통한 시각적인 노출이 가능한 다른 재해에 비해 재해의 물리적 과정에 대한 시각적인 관측이 어렵다는 배경이 있다고 판단된다. 더불어, 재해의 간접체험이 가능한 플랫폼이 부족하다는 점도 원인으로 여겨진다. 기술이 발달함에 따라 시뮬레이션을 통한 재해의 간접체험이 가능한 플랫폼이 개발되어왔으며, 이는 직접 경험하기 어려운 재해에 대해 위험성 인지 및 경각심 고취에 활용되어왔다. 본 연구에서는 수치해석 플랫폼인 Celeris Advent(Tavakkol and Lynett, 2017)를 기반으로 실시간 유사이송 해석이 가능한 인터랙티브 수치모형을 개발하여 문제를 개선하고자 하였다. GUI(Graphical User Interface)를 통해 조작이 가능한 Celeris Advent는 수치해석 결과를 실시간으로 가시화하며, 이에 대한 사용자 상호작용이 가능하다. 이를 기반으로 유사의 흐름에 대해 모의가 가능하도록 모형을 구성하여 실시간 사용자 입력 및 유사이송 물리현상 관측이 가능하도록 모형을 개발하였다. 수치모형 지배방정식은 2차원 천수방정식과 유사이송방정식을 양방향 결합하여 구성하였다. 개발된 모형의 정확성 평가를 위해 1차원 및 2차원 수리실험 데이터를 활용하여 수치실험을 수행하였으며, 전반적인 결과는 실험데이터와 잘 일치하였다.

• 한국정밀공학회지
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• 제13권7호
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• pp.90-99
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• 1996

In the area of assembly process of micro-chips and electronic parts on the printed circuit board, surface mounting device(SMD) is used as a fundamental tool. Generally speaking, the motion of the SMD is based on the ball screw system operated by any type of actuators. The ball screw system is a mechanical transformed which converts the mechanical rotational motion to the translational one. Also, this system could be considered as an efficient motion device against mechanical backlash and friction. Therefore a dynamic modeling and state sensitivity analysis of the ball screw system in SMD have to be done in the initial design stage. In this paper, a simple mathematical dynamic model for this system and the sensit- ivity analysis are mentioned. Especially, the bond graph approach is used for graphical modeling of the dynamic system before analysis stage. And the direct differentiation method is used for the state sensit- ivity analysis of the system. Finally, some trends for the state variables with respect to the design variables could be suggested for the better design and faster operating based on the results of dynamic and state sensitivity.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.