• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4226-4230
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• 2022

The Korea Atomic Energy Research Institute (KAERI) has designed a Hybrid-Low Power Research Reactor (H-LPRR) which can be used for critical assembly and conventional research reactor as well. It is an open tank-in-pool type research reactor (Thermal Power: 50 kW_th) of which the most important applications are Neutron Activation Analysis (NAA), Radioisotope (RI) production, education and training. There are eight irradiation holes on the edge of the reactor core: IR (6 holes for RI production) and NA (2 holes for NAA) holes. In order to quantify the elemental concentration in target samples through the Instrumental Neutron Activation Analysis (INAA), it is necessary to measure neutron spectrum parameters such as thermal neutron flux, the deviation from the ideal 1/E epithermal neutron flux distribution (α), and the thermal-to-epithermal neutron flux ratio (f) for the irradiation holes. In this study, the MCNP6.1 code and FORTRAN 90 language are applied to determine the parameters for the two irradiation holes (NA-SW and NA-NW) in H-LPRR, and in particular its α and f parameters are compared to values of other research reactors. The results confirmed that the neutron irradiation holes in H-LPRR are designed to be sufficiently applied to neutron activation analysis, and its performance is comparable to that of foreign research reactors including the TRIGA MARK II.

• Korean Journal of Construction Engineering and Management
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• v.16 no.3
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• pp.49-58
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• 2015

In order to determine how much a new green building reduce the environmental impacts, it is necessary to establish the reference target for comparison. Therefore, this study aims to establish the reference target by evaluating the environmental impacts of existing buildings. To ensure this end, this study evaluated the environmental impacts(Global warming potential, ozone layer depletion potential, acidification potential, eutrophication potential, photochemical ozone creation potential, and abiotic depletion potential) of 17 existing elementary school buildings, which are located in Seoul, Busan, Daegu, and Gwangju, by using the hybrid LCA model. As a result, the environmental impacts of the case buildings were clearly distinguished in different regions. Therefore, this study presented the reference targets which are appropriate to each region. For example, the reference targets for global warming potential, which can be used in Seoul, Busan, Daegu, and Gwangju, are 3.76E+03, 1.90E+03, 2.63E+03, $2.81E+03kg-CO_2\;eq./m^2$, respectively. The presented reference targets are expected to be useful for understanding how much environmental impacts can be reduced when a new green school building is constructed.

• Korean Journal of Plant Resources
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• v.36 no.4
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• pp.341-368
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• 2023

Distribution of plant resources in Mt. Baekseok located at Pyeongchang-gun, Gangwon-do, were surveyed for a total 17 times from April 2021 to September 2022. The result of this survey revealed 628 taxa, consisting of 99 families, 346 genera, 552 species, 20 subspecies, 49 varieties, 6 forms, and one hybrid. Among them, 21 taxa were endemic plants to Korea, 12 taxa were red list plants by the Ministry of Environment and 560 taxa were red list plants by the Korea Forest Service. The floristic target species amounted to 164 taxa, specifically one taxon of grade V, 20 taxa of grade IV, 52 taxa of grade III, 53 taxa of grade II, and 38 taxa of grade I. In addition, 34 taxa were classified as plants adaptable to climate change. 42 taxa of alien plants and 3 taxa of ecosystem disturbance species were also found in this area. Useful plants listed consists of 246 taxa (39.2%) of edible plants, 215 taxa (34.2%) of pasture plants, 187 taxa (29.8%) of medicinal plants, 75 taxa (11.9%) of ornamental plants and 22 taxa (3.5%) of timber plants, respectively.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• Journal of Life Science
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• v.17 no.8 s.88
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• pp.1063-1067
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• 2007

The Bcl-2 family proteins play critical roles in regulation of apoptosis, and the balanced interaction of pro- and anti-death members is a key factor in determining the cell fate. Noxa, a BH3-only Bcl-2-family member, has been originally identified as a target gene of p53. To understand the mechanism by which human Noxa (hNoxa) regulates the cell death, we screened the hNoxa binding partner using the yeast two hybrid screening and found that anti-death protein Mcl-1 binds to hNoxa. The binding of hNoxa to Mcl-1 was confirmed by immunoprecipitation in human colon cancer cell line HCT 116 cells. Mcl-1 significantly inhibited the hNoxa-induced cell death in HCT 116 cells. During the cell death induced by hNoxa, Mcl-1 protein was degraded. Its degradation was inhibited by z-VAD-fmk, a pancaspase inhibitor, suggesting caspase is responsible for Mcl-1 degradation in response to hNoxa. Together, the results indicate that hNoxa binds to Mcl-1 that is degraded by cas-pases during hNoxa-induced cell death.

• Journal of Navigation and Port Research
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• v.26 no.1
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• pp.22-27
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• 2002

The 25 Watt hybrid MIC SSPA has been developed in the frequency rang from 1.6265 GHz to 1.6465 GHz for uplink of INMARST's earth station. To simplify the fabrication process, the whole system is designed of two parts composed of a friving amplifier and a power amplifier. The Motorolas MRF-6401 is used for driving part, the Motorolas MRF-16006 and MRF-16030 is used the power amplifier. We reduced weight and volume of high power amplifier through arranging the bias circuits in the same housing. The realized SSPA has more than 30 dB for gain within 20 MHz bandwidth, and the voltage standing wave ratios(VSWR) of input and output port are less than 1.7, respectively. The output power of 44 dBm is achieved at the 1 dB gain compression point of 106365 GHz These results reveal a high power amplifier of 25 Watt which is the design target. The Proposed SSPA manufacture techniques in this paper can be applied to the implementation of high power amplifiers for some radars and SCPC.

• Tunnel and Underground Space
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• v.26 no.1
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• pp.1-11
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• 2016

This study deals with the case that was the field application of the microseismic monitoring techniques for the stability monitoring in a domestic mine. The usefulness and limitations of the microseismic techniques were examined through analyzing the microseismic monitored data. The target limestone mine adopted a hybrid room-and-pillar mining method to improve the extraction ratio. The accelerometers were installed in each vertical pillar within the test bed which has the horizontal cross-section $50m{\times}50m$. The measured signals were divided into 4 types; blasting induced signal, drilling induced signal, damage induced signal, and electric noise. The stability analysis was performed based on the measured damage induced signals. After the blasting in the mining section close to the test bed, the damage of the pillar was increased and rockfall near the test bed could be estimated from monitored microseismic data. It was possible to assess the pillar stability from the changes of daily monitored data and the proposed safety criteria from the accumulated monitored data. However, there was a difficulty to determine the 3D microseismic source positions due to the 2D local sensor arrays. Also, it was needed to use real-time monitoring methods in domestic mines. By complementing the problems encountered in the mine application and comparing microseismic monitored data with mining operations, the microseismic monitoring technique can be used as a better safety method.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.2
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• pp.128-136
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• 2012

Hybrid surveys using hydroacoustic and fish sampling gears such as trammel net, trap were conducted to investigate total biomass, distribution and dominant fish species of the demersal fishes in the marine ranching area (MRA) of Jeju. Four surveys were carried out in June, August, October and November using 38kHz quantitative echo sounder. Catch data using trammel net and trap were used to calculate biomass and to examine dominant fish species. Fish schools were mainly detected in the waters of 20 meters below and around Chagwido waters. By the result of fishing experiments, fish species living in MRA of Jeju were about 40 species, dominant fish species of a detectable fish such as Family Sparidae and Family Monacanthidae etc. were identified 59.4~68.8% of total biomass. Based on the hydroacoustic data, fish length-weight function and target strength information, mean density of the demersal fish estimated were as follows; 0.88g/$m^2$ on June, 1.12g/$m^2$ on August, 1.35g/$m^2$ on October and 1.18g/$m^2$ on November. An estimated average biomass in MRA of Jeju was founded 20.5 tons in 2007, 20.6 tons in 2008, 23.0 tons in 2009, 25.9 tons in 2010. The results showed that biomass of MRA is getting increased slowly. Therefore the hybrid survey method using hydroacoustic and fish sampling gears is assured an effective biomass survey in the waters of mixed species.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.