• Nuclear Engineering and Technology
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• v.47 no.3
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• pp.293-305
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• 2015

In this paper, we suggest the conceptual design of a water-cooled reactor system for a low-pressure inherent heat sink nuclear desalination plant (LIND) that applies the safety-related design concepts of high temperature gas-cooled reactors to a water-cooled reactor for inherent and passive safety features. Through a scoping analysis, we found that the current LIND design satisfied several essential thermal-hydraulic and neutronic design requirements. In a thermal-hydraulic analysis using an analytical method based on the Wooton-Epstein correlation, we checked the possibility of safely removing decay heat through the steel containment even if all the active safety systems failed. In a neutronic analysis using the Monte Carlo N-particle transport code, we estimated a cycle length of approximately 6 years under 200 $MW_{th}$ and 4.5% enrichment. The very long cycle length and simple safety features minimize the burdens from the operation, maintenance, and spent-fuel management, with a positive impact on the economic feasibility. Finally, because a nuclear reactor should not be directly coupled to a desalination system to prevent the leakage of radioactive material into the desalinated water, three types of intermediate systems were studied: a steam producing system, a hot water system, and an organic Rankine cycle system.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.851-858
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• 2021

Due to the increase in renewable energy and distributed resources, not only traditional data but also various energy-related data are being generated in the new energy industry. In other words, there are various renewable energy facilities and power generation data, system operation data, metering and rate-related data, as well as weather and energy efficiency data necessary for new services and analysis. Energy big data processing technology can systematically analyze and diagnose data generated in the first half of the power production and consumption infrastructure, including distributed resources, systems, and AMI. Through this, it will be a technology that supports the creation of new businesses in convergence between the ICT industry and the energy industry. To this end, research on the data analysis system, such as itemized characteristic analysis of the collected data, correlation sampling, categorization of each feature, and element definition, is needed. In addition, research on data purification technology for data loss and abnormal state processing should be conducted. In addition, it is necessary to develop and structure NIFI, Spark, and HDFS systems so that energy data can be stored and managed in real time. In this study, the overall energy data processing technology and system for various power transactions as described above were proposed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.9-16
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• 2005

The purpose of this research is to acquire emotional data on luminous source by measuring and evaluating human emotional response to the change of the optical feature of luminous environment Luminous sources used in actual architectural space were selected with the optical feature of luminous soured then to measure and analysis human emotional response on Luminous Source. As a result of that 1) In the result of performance measurement by the item of the clear vision of an optic function the fluorescent lamp of daylight indicated the most excellent Performance. 2) In the item of fatigue and stress, the metal halide lamp and mercury lamp showed the most 3) In $\ulcorner$ suitable in light$\lrcorner$, $\ulcorner$a similar with daylight$\lrcorner$ adjective of the amenity item the fluorescent lamp of daylight which color temperature was high turned up to be high also, in $\ulcorner$brilliant$\lrcorner$, adjective, the metal halide lamp and mercury lamp turned up to be low. 4) In the result of factor analysis, three factors $\ulcorner$activity$\lrcorner$, $\ulcorner$potency$\lrcorner$, $\ulcorner$evaluation$\lrcorner$ were abstracted and $\ulcorner$activity$\lrcorner$ factor has the most influential on evaluating the mood of interior space. 5) For the affection in the mood evaluation by each luminous sources, $\ulcorner$activity$\lrcorner$ factor was the most influential by metal halide lamp and fluorescent lamp of daylight, $\ulcorner$potency$\lrcorner$ factor was most influential by kind of incandescent lamp, $\ulcorner$evaluation$\lrcorner$ factor was most influential by fluorescent lamp of low color temperature.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.505-516
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• 2018

The purpose of the present study was to examine the mediating effect of self-concealment on the relationship between self-critical perfectionism and disordered eating behavior. Toward this aim, 348 participants responded to the measures of self-critical perfectionism, self-concealment and disordered eating behavior. Correlation analysis revealed that self-critical perfectionism was positively correlated with self-concealment and disordered eating behavior. In addition, self-concealment was positively correlated with disordered eating behavior. Structural equations analysis found that the relationship between self-critical perfectionism and disordered eating behavior had a significant partial mediating effect on self-concealment, meaning that self-critical perfectionism increased disordered eating behavior through high levels of self-concealment. This finding suggests that individuals who place high standards on themselves, and feature fear of negative evaluation from others, are at greater risk for disordered eating behavior. Based on this finding, we discussed suggestions for future research and clinical implications.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.275-282
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• 2020

Recently, the occurrence of a ship capsizing was analyzed as the main cause of the lack of stability or loss because of the improper management of the center of gravity, the movement of cargo or heavy weight when excessive steering occurs or when navigating during bad weather. Thus, to prevent a ship from capsizing, it is necessary to secure stability to enable the ship's return to its upright position, even if a dangerous heel occurs. The GM is a crucial evaluation factor regarding stability, which the navigation officer uses to preserve stability. In this study, based on the stability data collected from the operating of ships for five years, The GM by ship's type according to the operating status was analyzed specifically such as a ship's length, breadth, and gross tonnage. The feature of the GM distribution according to a ship's length was confirmed, and after performing the correlation analysis between the breadth and the GM, the ratio of the GM to breadth was calculated, and the result was compared with the previous ratio. Additionally, a simple approximation formula and minimum GM for the estimation of the GM by ship type were proposed by the regression analysis of the GM using the gross tonnage (GT)/breadth (B) to reflect the trend of larger ships being built. The results of this study are expected to be used as data for the review of securing a stable GM on ships.

• Atmosphere
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• v.25 no.4
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• pp.693-706
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• 2015

A shift of first fowering date (FFD) of spring blossoms (cherry, peach and pear) over the northest Asia under global warming is investiaged using dynamically downscaled daily temperature data with 12.5 km resolution. For the study, we obatained gridded daily data with Historical (1981~2010), and Representative Concentration Pathway (RCP) (2021~2100) 4.5 and 8.5 scenarios which were produced by WRFv3.4 in conjunction with HadGEM2-AO. A change on FFDs in 21st century is estimated by applying daily outputs of WRFv3.4 to DTS phonological model. Prior to projection on future climate, the performances of both WRFv3.4 and DTS models are evaluated using spatial distribution of climatology and SCR diagram (Normalized standard deviation-Pattern correlation coefficient-Root mean square difference). According to the result, WRFv3.4 and DTS models well simulated a feature of the terrain following characteristics and a general pattern of observation with a marigin of $1.4^{\circ}C$ and 5~6 days. The analysis reveals a projected advance in FFDs of cherry, peach and pear over the northeast Asia by 2100 of 15.4 days (9.4 days). 16.9 days (10.4 days) and 15.2 days (9.5 days), respectively, compared to the Historical simulation due to a increasing early spring (Februrary to April) temperature of about $4.9^{\circ}C$ ($2.9^{\circ}C$) under the RCP 8.5 (RCP 4.5) scenarios. This indicates that the current flowering of the cherry, peach and pear over analysis area in middle or end of April is expected to start blooming in early or middle of April, at the end of this century. The present study shows the dynamically downscaled daily data with high-resolution is helpeful in offering various useful information to end-users as well as in understanding regional climate change.

• International Journal of Oral Biology
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• v.41 no.3
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• pp.133-139
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• 2016

The ultrastructural parameters related to synaptic release of endings which are presynaptic to tooth pulp afferent terminals (p-endings) were analyzed to understand the underlying mechanism for presynaptic modulation of tooth pulp afferents. Tooth pulp afferents were labelled by applying wheat-germ agglutinin conjugated horseradish peroxidase to the rat right lower incisor, whereafter electron microscopic morphometric analysis with serial section and reconstruction of p-endings in the trigeminal oral nucleus was performed. The results obtained from 15 p-endings presynaptic to 11 labeled tooth pulp afferent terminals were as follows. P-endings contained pleomorphic vesicles and made symmetrical synaptic contacts with labeled terminals. The p-endings showed small synaptic release-related ultrastructural parameters: volume, $0.82{\pm}0.45{\mu}m^3$ ($mean{\pm}SD$); surface area, $4.50{\pm}1.76{\mu}m^2$; mitochondrial volume, $0.15{\pm}0.07{\mu}m^3$; total apposed surface area, $0.69{\pm}0.24{\mu}m^2$; active zone area, $0.10{\pm}0.04{\mu}m^2$; total vesicle number, $1045{\pm}668.86$; and vesicle density, $1677{\pm}684/{\mu}m^2$. The volume of the p-endings showed strong positive correlation with the following parameters: surface area (r=0.97, P<0.01), mitochondrial volume (r=0.56, P<0.05), and total vesicle number (r=0.73, P<0.05). However, the volume of p-endings did not positively correlate or was very weakly correlated with the apposed surface area (r=-0.12, P=0.675) and active zone area (r=0.46, P=0.084). These results show that some synaptic release-related ultrastructural parameters of p-endings on the tooth pulp afferent terminals follow the "size principle" of Pierce and Mendell (1993) in the trigeminal nucleus oralis, but other parameters do not. Our findings may demonstrate a characteristic feature of synaptic release associated with p-endings.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.283-298
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• 2022

As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.