• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.11
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• pp.1020-1026
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• 2013

An accurate railway environmental noise prediction model is required to make the proper solution of the railway noise problems. In this paper, an engineering model for predicting the noise of conventional passenger cars is presented considering the acoustic source strength in octave-band frequencies and the propagation over grounds with varying surface properties. Since the formation of a train can be variable, the source strength of each locomotive and passenger car was estimated by measuring the pass-by noise and analysing the wheel-rail rolling noise. Some validation cases show on the average small differences between the predictions of the present model and the measurement results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.404-408
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• 2005

Having flexibility in a manipulator will degrade trajectory tracking control and manipulator tip positioning. In practice, however, constraints imposed by various operating requirements, will render the presence of such flexibility unavoidable. The dynamic analysis of the flexible manipulator is essential in designing proper control systems. A flexible manipulator consists of infinite number of elastic modes and the modes are usually coupled to each other. For the practicality, however, it is usually assumed that the flexible system consists of finite number of elastic modes and the modes are decoupled. These assumptions result in a linear and decoupled mathematical model of the flexible manipulator and simplify the analysis of the dynamic behavior and the design of the control system. The decoupling and linearization of the flexible link, however, has been assumed without in depth analysis. This paper focuses on the analysis of the significance of the non-linear coupling factors.

• Bulletin of the Korean Chemical Society
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• v.10 no.6
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• pp.585-591
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• 1989

The effects of the charged metal on the overall electrostatic potential profiles and the capacitances of the electrical double layer are brought out. A model with a simplified jellium and a point-charged electrolyte is utilized in the present calculations. Electrons are assumed not to penetrate electrode surface due to a strong screening of electrolyte at the interface. Electron density functions and ion density functions are obtained, which are also based upon the Poisson equation and Boltzmann equation on either side of the interface. A complete potential profile starting from bulk electrode and ending at bulk electrolyte is obtained by connecting the two potential profiles (one inside the metal electrode, the other inside the electrolyte) with proper boundary conditions. In spite of the simplicity of the model, the present model reveals the importance of the effect of the charged metal on the electrostatic potential profile and the electrical double layer capacitances. The results are discussed and compared with the predictions by Gouy Chapman theory.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.29-37
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• 1999

When an escalator is developed or modified to improve its performance, estimation of dynamic characteristics of the escalator is required. But modeling of the escalator for a design purpose is very difficult and time consuming. Especially, it is very difficult for a designer to use the simulation model during design process because preparing a proper model takes time and also the designer needs to know how to use the simulation program. In order to solver these problems, a graphic user interface program that can predict dynamic characteristics of an escalator is developed for the designer. Since this program makes the necessary escalator model for simulation for itself, the designer does not need to know how to use the simulation program while design is in progress. In this paper, a designer oriented interface program is developed and an example is presented to show the effectiveness of the developed program.

• Journal of the Korean Society for Railway
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• v.10 no.3 s.40
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• pp.319-326
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• 2007

During the design phase of a system, which requires high reliability and safety such as aircraft, high speed train and nuclear power plant, reliability engineer must set up the target system reliability. To meet a reliability goal for the system, reliability allocation should be done gradually from the system to its element. For this end, first of all, we need to construct functional block diagram based on the design output and PWBS(Project Work Breakdown System). Another important input data for reliability allocation is the relationship between the cost and the reliability. In this study we investigate various reliability allocation models, which can be applicable to aircraft, vehicle, and power plant, and etc. And we suggest a proper reliability allocation model which can be effectively applicable to KTX-II high speed train to achieve the target system reliability.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.93-100
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• 2019

The interest in the Fourth Industrial Revolution along with the development of ICT makes the software get the attention of the world. This phenomenon naturally leads to the concern for software education. Learning software coding is not easy for students whose major is in humanities or social sciences. This paper is a study of how cognition on software education affects to education of computational thinking. For research method, moderator variables were adopted on the proposed research model to prove that positive cognition can derive good influence on improvement of computational thinking. To find out moderator variables of the research model, we have conducted the questionnaire over three years for total of 928 students who took the software coding courses. As the result of the study, we proved that the positive cognition on software education can make the better improvement of computational thinking within proper moderator variables.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.117-121
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• 2015

Construction managers use scheduling methods to improve the outcome of their project. Despite the many obvious advantages of the critical path method (CPM), its use in construction has been limited. Understanding the reasons why CPM is not used as extensively as expected could improve its level of acceptance in the construction industry. The link between construction scheduling methods and the tasks expected to be performed by schedulers has been an on-going concern in the construction industry. This study proposes a task-technology fit model to understand why CPM is not used as extensively as expected in construction scheduling. A task-technology fit model that aims to measure the extent to which a construction scheduling method functionally matches the tasks expected to be performed by the scheduling staff. The model that is proposed is an answer to the lack of proper instruments for evaluating the extent to which scheduling methods are used in the industry.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.631-635
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• 2015

Construction managers use scheduling methods to improve the outcome of their project. In spite of the many obvious advantages of the critical path method (CPM), its use in construction has been limited. Understanding the reasons why CPM is not used as extensively as expected could improve its level of acceptance in the construction industry. The link between construction scheduling methods and the capabilities of the scheduling staff has been an on-going concern in the construction industry. This study proposes a staffing-technology fit model to understand why CPM is not used as extensively as expected in construction scheduling. A staffing-technology fit model that aims to measure the extent to which a construction scheduling method matches the staff's experience, know-how and capabilities. The model that is proposed is an answer to the lack of proper instruments for evaluating the extent to which scheduling methods are used in the industry.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.