• Korean Institute of Interior Design Journal
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• no.34
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• pp.3-9
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• 2002

Entering into the 21st century, digital culture and the innovation of media technology have drastically changed our understanding of the concept of space. Increased availability of information made possible by technological advancement has, directly or indirectly, contributed to the development of space design, which, in turn, offers a possibility for a new paradigm in space design. Given these fundamental changes, this study seeks to explore how to understand the expansion of the concept of space. In order to answer this question, this study investigates de-materialization tendency in modern architectural design such as transparency, anti-gravity, complexity and simultaneity of space. It examines the interaction-oriented nature of space among human, information and time. Finally, based on concepts such as new hyper-surface, which transcend the limitations of space and time, it explores new emerging trends in space design.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.40-41
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• 2002

• Korean Institute of Interior Design Journal
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• v.15 no.3 s.56
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• pp.75-82
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• 2006

Discussions on transparency have been being analyzed as variedly as the diversity itself in contemporary architecture. Though it is inappropriate to summarize the discussions into several specific viewpoints, as the notional classifications on transparency are varied according to the points of individual researchers, it can be said that, by approaching with the standpoint of the designers who may have various difficulties in the course of design, the meaning of this thesis lies largely in the fact that it attempted to study the architectural application techniques of transparency notion both through surface aspect which could be said to decide on the appearance of the architecture shape and through, in physical aspects as a combination of space components comprising the inner space, spatial aspect to which architectural techniques of transparency notion are applied. Through these, we conclude as follows. 1. Representation Techniques of Transparency in Surface : Transparency from surface viewpoint could be categorized into 1) emphasis on property-of-matter, 2) lightness of Literal material itself, 3) visual ambivalence, and 4) dematerialized hyper-surface. 2. Representation Techniques of Transparency in Space : In spatial viewpoint, transparency is summarized into sub-viewpoints as 1) straightforward space 2) ambiguous spacer 3) expanded space

• Computers and Concrete
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• v.24 no.6
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.