• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.93-98
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• 2009

Comprehension of physical properties distribution of underground cavity must be made primarily to show the clear image of the state of the cavity. A physical scale model experiment is executed assuming that underground cavity in filled with air or water of different ratio. The state of cavity wall is considered wet. Cavity model is made of agar. As a experimental result, even if the cavity wall is wet, high air and water ratio cavity shows high anomaly.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.235-240
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• 2006

Software architectures may be described using text-based architecture description language(ADL). The key goals of an ADL are to communicate alternate designs between different stakeholders, to detect reusable structures, and to record design decisions. This paper provided a solution to the representation problem by creating a tool for three-dimensional representation of architectural viewpoints. The tool consisted of an architecture description language(VTADL) to first describe the software architectures and viewpoints on the architectures; and a VTADL-to-VRML translator to translate each viewpoint into a separate virtual reality world The goal of the paper was to devise algorithms for translating an ADL into effective VRML representations based on the desired viewpoint. The VRML representations were intended to enhance comprehension on the overall design and to improve communications between diverse stakeholders.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.49-59
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• 2004

An eye-tracking experiment was conducted to examine resolving processes of grammatical marking ambiguities of Korean. and to evaluate predictions from the garden-path model and the constraint-based models on the processing of Korean morphological information. The complex NP clause structure that can be parsed according to the minimal attachment principle was compared to the embedded relative clause structures that have one of the nominative marker (-ka), the delimiter (-man, which roughly corresponds to the English word 'only'), or the topic marker (-nun) on the first NPs. The results clearly showed that Korean marking ambiguities are resolved by the minimal attachment principle, and the topic marker affects reparsing procedures. The pattern of eye fixation times was more compatible with the garden-path model, and was not consistent with the predictions of the constraint-based accounts. Suggestions for further studies were made.

• Archives of Plastic Surgery
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• v.32 no.6
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• pp.796-797
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• 2005

In plastic and reconstructive craniomaxillofacial surgery, careful preoperative planning is essential to get a successful outcome. Many craniomaxillofacial surgeons have used imaging modalities like conventional radiographs, computed tomography(CT) and magnetic resonance imaging(MRI) for supporting the planning process. But, there are a lot of limitations in the comprehension of the surgical anatomy with these modalities. Medical models made with rapid prototyping (RP) technique represent a new approach for preoperative planning and simulation surgery. With rapid prototyping models, surgical procedures can be simulated and performed interactively so that surgeon can get a realistic impression of complex structures before surgical intervention. The great advantage of rapid prototyping technique is the precise reproduction of objects from a 3-dimensional reconstruction image as a physical model. Craniomaxillofacial surgeon can establish treatment strategy through preoperative simulation surgery and predict the postoperative result.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.8
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• pp.150-159
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• 1999

Exhaust system is composed of several parts. Among, them , design of muffler system strongly influences on engine efficiency and noise reduction. So , through comprehension of flow characteristics inside muffler is necessary . In this study , three-dimensional steady and unsteady compressible flow analysis was performed to understand the flow characteristics, pressure loss and amplitude variation of pulsating pressure. The computational grid generation was carried out using commercial preprocessor ICEM CFD/CAE. And the three-dimensional fluid motion inside the muffler was analyzed by STAR-CD, the computational fluid dynamics code. RNG k-$\varepsilon$ tubulence model was applied to consider the complexity of the geometry and fluid motion. The steady and unsteady flow field inside muffler such as velocity distribution, pulsating pressure and pressure loss was examined. In case of unsteady state analysis, velocity of inlet region was converted from measured pulsating pressure. Experimental measurement of pressure and temperature was carried out to provide the boundary and initial condition for computational study under three engine operating conditions. As a result of this study, we could identify the flow characteristics inside the muffler and obtain the pressure loss, amplitude variation of pulsating exhaust gas.

• The Korean Journal of Emergency Medical Services
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• v.23 no.2
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• pp.7-18
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• 2019

Purpose: This study aimed to determine the core competencies for 119 emergency medical technicians (EMTs) and to provide evidence for the development and utilization of 119 EMTs, education and training programs. Methods: Data were collected with a questionnaire that consisted of items on the general characteristics of the subjects (8 items) and importance and satisfaction levels for 10 competencies, including 34 items on subfactors. The Borich Needs Assessment Model was used in designing the questionnaire. A locus for focus model was used to derive the top priority competencies for the improvement of core competency in the profession. Data were analyzed with by SPSS ver.21 (IBM, Armonk, NY, USA). Results: The core competencies for 119 EMTs were derived from technical competency, information competency, communication competency, problem-solving competency, self-development competency and interpersonal competency. In addition, among the sub-factors of these competencies, 16 abilities (including leadership), ability to apply technical knowledge, technical comprehension, conflict management ability, ability to create documents, problem handling ability, ability to think, technical selection ability, and computer literacy were included in the priority category. Conclusion: This study derived and presented the core competencies for 119 EMTs to enable them to meet the modern day requirements of their roles, which may contribute to enhancing their professionalism.

• Communications of Mathematical Education
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• v.38 no.1
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• pp.49-67
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• 2024

Recent interest in the diverse applications of artificial intelligence (AI) language models has highlighted the need to explore didactical uses in mathematics education. AI language models, capable of natural language processing, show promise in solving mathematical word problems. This study tested the capability of ChatGPT, an AI language model, to solve word problems from elementary school textbooks, and analyzed both the solutions and errors made. The results showed that the AI language model achieved an accuracy rate of 81.08%, with errors in problem comprehension, equation formulation, and calculation. Based on this analysis of solution processes and error types, the study suggests implications for the didactical application of AI language models in education.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.281-290
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• 2020

The Vision-and-Language Navigation(VLN) task is a complex intelligence problem that requires both visual and language comprehension skills. In this paper, we propose a new learning model for visual-language navigation agents. The model adopts a hybrid learning that combines imitation learning based on demo data and reinforcement learning based on action reward. Therefore, this model can meet both problems of imitation learning that can be biased to the demo data and reinforcement learning with relatively low data efficiency. In addition, the proposed model uses a novel path-based reward function designed to solve the problem of existing goal-based reward functions. In this paper, we demonstrate the high performance of the proposed model through various experiments using both Matterport3D simulation environment and R2R benchmark dataset.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.357-370
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• 2022

In this paper, Tobit and Heckit models are introduced. These models have been used for analyzing censored data. Censoring occurs at a specific point and a large number of observations are distributed with a positive probability at a certain point. Censoring can occur due to observing limitation or exogenous variables. Tobit and Heckit models are used to correct sample selection bias, which can occur when an ordinary linear regression model is fitted to censored data. However, the difference between the two models is not clearly accounted for; hence, they have often been used interchangeably. Therefore, the suitability of the models was validated through simulated data, and demonstrated through real data. As the result, it was confirmed that both Tobit and Heckit models are well-fitted to the data censored due to observing limitation, although Tobit model was fitted parsimoniously. In contrast, only Heckit model is well-fitted to the data censored due to exogenous variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.