• Korean Journal of Environmental Biology
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• v.31 no.4
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• pp.419-428
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• 2013

The objectives of this research were to determine ecological characteristics and chemical gradients in two different loach populations-Misgurnus anguillicaudatus ($M_a$) and Koreocobitis rotundicaudata ($K_r$). Eight chemical parameters were compared to test the chemical preferences between the two species. The population of Kr occurred more in better water quality, compared to the $M_a$-population. The $M_a$-population co-occurred more with tolerant species (TS) and omnivores, whereas the $K_r$-population co-occurred more with sensitive species (SS) and insectivores. The differences in the tolerance and trophic composition matched well with the result of stream health assessment, based on the multi-metric model of Index of Biological Integrity (IBI). The IBI value in habitat with the $M_a$-population was "19.6" (range: 8~40) indicating a fair condition, and the IBI values of $K_r$-population was 29.4 (range: 12~40) indicating good condition. Similar results were found in physical habitat health analysis (QHEI). Overall, data analysis showed that the $M_a$-population belong to the tolerant species and $K_r$-population belong to sensitive species in the tolerance classification.

• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.277-288
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• 2006

Product line engineering (PLE) is a new effective approach to software reuse, where applications are generated by instantiating a core asset which is a large-grained reuse unit. Hence, a core asset is a key element of PLE, and therefore the reusability of the core asset largely determines the success of PLE projects. A tore asset is a reusable part not a whole system, and supports not only variable functions but also common functions. However, there are limitations to evaluate reusability of core asset that has these unique characteristics. This paper proposes a comprehensive quality system for evaluating the reusability of core assets, based on ISO/IEC 9126. We first identify the key characteristics of core assets, and derive the set of quality attributes that characterizes the reusability of core assets. finally, we define metrics for each quality attribute. In addition, we provide guidelines for applying the metrics and perform a case study based on rental product line. Using the proposed quality system, reusability of core assets can be more effectively and correctly evaluated.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.229-236
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• 2022

An experimental investigation was conducted on the influences of median size, dust concentration, dust condition (cloud and layer) for the fire and explosion hazard assessment of dusts with the same powder property. For this purpose, tests have been performed in accordance with 20 L explosion sphere, thermogravi- metric analyze, combustion rate tester (UN method). We investigated the explosion characteristics and flame propagation velocity (FPV) in dust cloud and the flame spread velocity(FSV) over dust layer on 8 dust samples with different particle sizes of 4 types of dusts (Sugar, Mg, Al, Zr). An explosion hazard increased with decreasing particle size in Mg and Al dust clouds, but sugar did not show the effect of explosion hazard due to particle size change in dust clouds. The flame propagation velocity (FPV) of suspended dusts increased significantly when the particle size decreased from micro to nano than the variation of particle size in micro range. The flame spread velocity (FSV) over dust layer showed a tendency to increase over the inclined dust layers (30° slope) rather than the horizontal dust layers (0° slope). The flame spread rate (FSV) over dust layers increased on the inclined dust layer (30° slope) rather than the horizontal dust layer (0° slope) and was higher upward flame than the downward flame in condition of inclined dust layers(30° slope).

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.257-273
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• 2023

Hydrochemical characteristics and microbial communities of spring (Samtong), surface water, and groundwater in Cheorwon, Korea, were analyzed. Field surveys and water quality analyses were undertaken at 10 sampling points for five spring, two surface, and three groundwater samples on 15 December 2022. Hydrochemical analysis revealed that most water samples were Ca-HCO₃ type and that water-rock interactions were the predominant mineral source. Radon concentrations were <1 kBq m^-3 for surface water, 1~10 kBq m^-3 for spring water, and 1~1,000 kq m^-3 for groundwater. Microbial cluster analysis showed that the main phyla were Proteobacteria, Planctomyceta, Verrucomicrobia, Acidobacteria, and Actinomycetota.Non-metric multidimensional scaling (NMDS) analysis indicated that water temperature, pH, and Si content were closely related to microorganism content. NMDS and canonical correspondence analysis results revealed that environmental factors affecting spring water were temperature, and Mg and Si concentrations, particularly for Acidobacteria and Proteobacteria, and Pseudomonas brenneri. Both hydrochemical and microbial community analyses yielded similar results at some spring and groundwater sampling points, likely due to the effects of a basalt aquifer.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.160-170
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• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Korean Journal of Ecology and Environment
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• v.40 no.2
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• pp.184-192
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• 2007

This study was to evaluate ecological health of Sumjin River during April${\sim}$June 2006. The ecological health assessments was based on the Index of Biological Integrity (IBI), Qualitative Babitat Evaluation Index (QHEI), and water chemistry. For the study, the models of IBI and QHEI were modified as 10 and 11 metric attributes, respectively. We also analyzed spatial patterns of chemical water quality over the period of $2002{\sim}2005$, using the water chemistry dataset, obtained from the Ministry of Environment, Korea. In Sumjin River, values of IBI averaged 33 (n= 12), which is judged as a "Fair${\sim}$Good" condition after the criteria of Barbour at al. (1999). There was a distinct spatial variation. Mean IBI score at Site 5 was estimated as 40, indicating a "Good" condition whereas, the mean at Site 3 was 23, indicating a "Poor${\sim}$Fair" condition. Habitat analysis showed that QHEI values in the river averaged 109 (n=6), indicating a "Marginal" condition after the criteria of Harbour et al. (1999). Values of BOD and COD averaged 1.3 mg $L^{-1}$ (scope: $0.9{\sim}1.8$ mg $L^{-1}$) and 3.3 mg $L^{-1}$ (scope: $2.8{\sim}4.0$ mg $L^{-1}$), respectively during the study. It was evident that chemical pollutions by organic matter were minor in the river. Total nitrogen (TN) and total phosphorus (TP) averaged 2.5 mg $L^{-1}$ and 0.067 mg $L^{-1}$, respectively, and the nutrients did not show large longitudinal gradients between the upper and lower reach. Overall, dataset of IBI, QHEI, and water chemistry suggest that river health has been well maintained, compared to other major watersheds in Korea and should be protected from habitat disturbance and chemical pollutions.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used