• Journal of the Korean GEO-environmental Society
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• v.15 no.11
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• pp.21-27
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• 2014

In this study, the prediction methods for the crest settlement after impounding and the maximum internal settlement during dam construction were proposed through the analysis on settlement data at 46 monitored points of 37 Center-Cored Rockfill Dams (CCRDs). Results from this analysis provided that the crest settlement increases with elapsed time, and from the relationship between the dam height and the maximum internal settlement during dam construction, it is confirmed that the internal settlement was largely evaluated when the coarse-grained material was used as the dam core. This internal settlement increased in proportion to the dam height. In addition, the crest settlement of the CCRD with the core compacted with fine-grained material was relatively large. It is expected that the results of this study would provide the practical tool for the design, construction and management of CCRDs.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.586-596
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• 2011

In the IT industry, processing units of IT applications are getting smaller and high efficient. Furthermore, the realization of various smart functions is highly feasible now due to advances in sensing technology. The service infrastructures on high efficient and compact mobile devices are applied to various areas. These also could be possessed by users and is built into the devices. Currently, studies on the UX(User Experience) field to attempt an analysis and prediction of user's information are continuing with reference to the UI(User Interface). However, research on the common framework of classification and storing the user-information, and standardization of form has not been attempted yet. In this study, we proposed the AUX(Associative user Experience) model and process structure to store various empirical data by users. The AUX model expressed a diversity of user's empirical data using extended E-TCPN model. And also, we expressed the data structure using XML with reference to the application of AUX model. This expressed model and separation of process structure guarantee its specialty, productivity and flexibility through the humanistic characteristics of users and the independence of technical process structure. The AUX model maps out the AUX information process architecture and expressed the process with the improved MPP algorithm, to analyze of its performance. The simulation of movements applying to MPP traffic allocation of VOD is used to analyze of its performance. The playback deviation of MPP Graphic Allocation Algorism where the AUX model was applied was improved by 10.41% more than the one where it was not applied. As a result of that, playback performance has improved due to the conversion of AUX with accessing media, content of users and dynamic traffic allocation such as MPI and CPI.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2066-2070
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• 2008

본 연구는 인공신경망(Artificial Neural Networks: ANNs)기법의 일종인 자기조직화(Self Organizing Map: SOM) 이론을 이용한다. 자기조직화 특성을 이용하여 스스로 학습이 가능하고, 구조상 수행이 빨라 학습 단계에 소요되는 시간을 줄 일 수 있는 장점을 가진 자기조직화 이론을 도입하고, 수질자료 중 전체 유기물의 양을 나타내며 난분해성 물질에 대한 해석이 가능하고 재현성이 탁월한 TOC 와 강우-유출량 자료의 분포적 양상과 특징을 분석하여 예측을 위한 모형화 과정에 기여하고자 한다. 최적의 Map Size와 Map Array 결정을 위해 수집된 강우와 유출량자료 및 TOC 자료에 대해 Garcia의 경험식을 이용하여 Map을 구성하는 단위구조의 총 수(M)를 산정하여 M값에 따른 종방향 및 횡방향 크기를 결정하는 다수의 Map 크기를 검토하고, 또한 Map 배열은 2차원 배열의 사각형배열(Rectangular array)과 육각형배열(Hexagonal array)에 대해서도 복합적으로 검토하여 최적의 특성조건을 결정하여 강우-유출 및 TOC 관계의 분할특성을 분석한다.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.3
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• pp.104-112
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• 1997

Line heating process has been used in forming hull surfaces long before and it has depended on skillful workers. As the reduction of production cost is major concern of shipbuilding companies, line heating work must be improved for higher productivity. In this paper, as the first step to automatic hull forming, a method is proposed to predict deformations due to line heating. It includes a simplified thermal elasto-plastic analysis to increase computing efficiency and to do real time visualization of deformed shapes. For the prediction of deformation, a method to estimate heat flux of the torch is also introduced. Predicted deformations for line heated plates show good agreement with experimental results. The proposed method can be used in control and simulation of line heating process with ease.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.1 s.20
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• pp.29-33
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• 2004

Design of general steel structure had applied to achieve elastic designing concept so far. Because elastic design supposes that whole structure complies with elasticity formula so that achieve via allowable stress of material. It is concept that calculate stress distribution of construction about action external load and estimate load when the maximum stress reaches equally with allowable stress that is established by maximum safety load of the structure. But, absence that compose actuality structure by deal with external load increase small success surrender and structure hardness falls and structure in limited state finally on the whole as showing complicated process by interference between collapse and buckling under compression. Applied ANSYS (elasto-plasticity large deformation finite element method) to be mediocrity finite element program for analysis method and analysis control used in Newton-Raphson method & Arc-length method.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.531-536
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• 2019

The Ministry of the Interior and Safety amends the enforcement ordinance of the public service design method and applies it to the project tasks of each institution and cases of social problem solving and public service innovation are increasing according to service design methodology. This study is to develop the $360^{\circ}$ VR content element in order to deliver the understanding and effective improvement of the service to the consumers in the process of problem discovery and the prototype that visualize the key solution according to the service design methodology. By applying it to the actual public service design project, it was possible to predict the result with high satisfaction after improvement. This paper is meaningful in that it proposes new approach and empirical value to consumers and contribute to the improvement of the service design process as a convergence study on the future service design using the $360^{\circ}$ VR prototype.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.72-82
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• 2016

Various studies have been done in medical service area but they have just focused on the examination of the relationships between cause and effect variables. This study, thus, empirically analyzed qualitative data regarding medical service problems using word cloud technique. The major results of the paper are as follows. The data reveal ten sources in medical service - forced treatment, excess inspection, misdiagnosis, carelessness, inexperienced service, waiting for emergency, reservation problem, unkindness, process problem, and inconvenience. Major words in the category of irrecoverable service failure are misdiagnosis, careless treatment, and inexperienced service whereas those in recoverable service failure are unkind attitude and negative experience in reservation system. Those who experienced a medical service problem are usually engaged in a public act and they make public protests and legal action against very severe problems. The conclusion of this study also suggests a summary, implication, and agenda of the research.

• Economic and Environmental Geology
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• v.48 no.2
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• pp.115-130
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• 2015

Extended elastic impedance (EEI) is an extension of elastic impedance (EI) which is a generalization of acoustic impedance (AI) for nonzero angles of incidence and can be tuned to be proportional to reservoir properties. In this study, we evaluated EEI inversion by estimating the P-($V_p$) and S-wave velocities ($V_s$), P-wave to S-wave velocity ratio ($V_p/V_s$), and Poisson's ratio of the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA. We also applied the EEI inversion technique to estimate porosity, gamma-ray values, and density of the Second Wall Creek Sand. Data used in the study include 3-D pre-stack seismic data from the southern part of the field and four wells, selected from a large well database. The $V_s$ logs at the wells were constructed from the $V_p$ logs using the empirical relationships. The percent prediction errors for the four velocity properties are less than about 5% except for Poisson's ratio at one well, supporting that the EEI inversion can be used in the prediction of rock properties. However, the results from the EEI inversion analysis of porosity, gamma-ray values, and density at the wells were unsatisfactory and thus these properties, which are not directly computed from velocities, may not be suitable for EEI inversion.