• Bulletin of the Korean Chemical Society
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• v.29 no.4
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.2
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• pp.45-52
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• 2019

BEMS(Building Energy Management Systems) have been applied to office buildings and collect relevant building energy data, e.g. temperatures, mass flow rates and energy consumptions of building mechanical systems and indoor spaces. The aforementioned measured data can be beneficially utilized for developing data-driven machine learning models which can be then used as part of MPC(Model Predictive Control) and/or optimal control strategies. In this study, the authors developed ANN(Artificial Neural Network) models of an AHU (Air Handling Unit) and a chiller for a real-life office building using BEMS data. Based on the ANN models, the authors developed optimal control strategies, e.g. daily operation schedule with regard to optimal start and stop of the AHU and the chiller (500 RT). It was found that due to the optimal start and stop of the AHU and the chiller, 4.5% and 16.4% of operation hours of the AHU and the chiller could be saved, compared to an existing operation.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.4
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• pp.83-91
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• 2018

Artificial lighting contributes greatly to developing civilizations. It allows daytime activities to continue throughout the dark hours of the day and thus increasing work productivity as well as allowing people to enjoy nighttime activities. In addition, artificial lighting is used to beautify landscapes, architectural monuments, and thus highlighting the social-economic development of a given place. However, excessive and improper usage of artificial lighting can lead to light pollution. Light pollution is a serious issue that is detrimental to human health. It has been linked to a number of health conditions including sleep disorder, visual discomfort as well as cancer. The effects of light pollution extend throughout the entire ecosystem, affecting both plants and animals. Furthermore, sky-glow from light pollution hinders astronomical observation. The current paper presents a study conducted on lit environment of a nightscape. The quality of the sky was measured in 144 spots using Sky Quality Meter (SQM) devices. The measured spots were chosen on the basis of land use as well as distance from the Halla Mountain.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Journal of the Korean Chemical Society
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• v.67 no.2
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• pp.89-98
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• 2023

The cell membrane, also known as the biological membrane, surrounds every living cell. The main components of cell membranes are lipids and therefore called as lipid membranes. These membranes are mainly made up of a two-dimensional lipid bilayer along with integral and peripheral proteins. The complex nature of lipid membranes makes it difficult to study and hence artificial lipid membranes are prepared which mimic the original lipid membranes. These artificial lipid membranes are prepared from phospholipid vesicles (liposomes). The liposomes are formed when self-forming phospholipid bilayer comes in contact with water. Liposomes can be unilamellar or multilamellar vesicles which comprises of phospholipids that can be produced naturally or synthetically. The phospholipids are non-toxic, biodegradable and are readily produced on a large scale. These liposomes are mostly used in the drug delivery systems. This paper offers comprehensive literature with insights on developing basic understanding of lipid membranes from its structure, organization, and phase behavior to its potential use in biomedical applications. The progress in the field of artificial membrane models considering methods of preparation of liposomes for mimicking lipid membranes, interactions between the lipid membranes, and characterizing techniques such as UV-visible, FTIR, Calorimetry and X-ray diffraction are explained in a concise manner.

• Steel and Composite Structures
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• v.48 no.5
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.27-34
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• 2023

This paper studied the 3D modeling process for the restoration of the 'Three-story Stone Pagoda of Bulguksa Temple in Gyeongju', a stone pagoda from the Unified Silla Period, using artificial intelligence (AI). Existing 3D modeling methods generate numerous verts and faces, which takes a considerable amount of time for AI learning. Accordingly, a method of performing more efficient 3D modeling by lowering the number of verts and faces is required. To this end, in this study, the structure of the stone pagoda was deeply analyzed and a modeling method optimized for AI learning was studied. In addition, it is meaningful to propose a new 3D modeling methodology for the restoration of stone pagodas in Korea and to secure a data set necessary for artificial intelligence learning.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.190-200
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• 2023

As the field of application of in-situ gamma spectroscopy is diversified, proficiency is required for consistent and accurate analysis. In this study, a program was developed to virtually create gamma energy spectra of artificial nuclides, which are difficult to obtain through actual measurements, for training. The virtual spectrum was created by synthesizing the spectra of the background radiation obtained through actual measurement and the theoretical spectra of the artificial radionuclides obtained by a Monte Carlo simulation. Since the theoretical spectrum can only be obtained for a given geometrical structure, representative major geometries for in-situ measurement (ground surface, concrete wall, radioactive waste drum) and the detectors (HPGe, NaI(Tl), LaBr₃(Ce)) were predetermined. Generated virtual spectra were verified in terms of validity and harmonization by gamma spectrometry and energy calibration. As a result, it was confirmed that the energy calibration results including the peaks of the measured spectrum and the peaks of the theoretical spectrum showed differences of less than 1 keV from the actual energies, and that the calculated radioactivity showed a difference within 20% from the actual inputted radioactivity. The verified data were assembled into a database and a program that can generate a virtual spectrum of desired condition was developed.