• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.15-26
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• 2015

The purpose of this study was to examine the area, theme and method of domestic articles on the successful establishment of venture companies, which were presented between 1998 and 2014, according to related basic classification criteria in an effort to shed light on the characteristics of these studies. It's basically meant to make a contribution to the development of research on the success of venture business and to be of practical use for the success of venture companies in our country. After related earlier studies were analyzed, 164 articles were collected, and 64 articles were selected from among them. The articles that were overlapped, not presented or not registered were excluded. The related studies had been conducted by different learned societies that were interested in the same research themes, and the studies were primarily led by the Korean Society of Business Venturing as of 2014, especially by Asia-Pacific Journal of Business Venturing and Entrepreneurship that was a journal published by this society. This society seemed to have taken the initiative in this field. There was a tendency that the related studies increased in number due to the shifts of modern industrial society and the changing policies of the government, as the number of the studies has been on the steady rise since 2012. In terms of method, quantitative research methods, especially descriptive studies, were prevailing. To step up the development of research into this field, a wide variety of research methods should be utilized in the future. As for the sphere of research, the characteristics of founders, strategic characteristics and environmental characteristics were mainly covered as the success factors of venture business. More diverse variables should be explored as well to make more advanced, extended research in the years to come. The findings of the study are expected to provide both theoretical and practical information on the establishment and success of venture business to make a contribution to the development of research.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.4
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• pp.195-205
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• 2013

Excessive input of organic matters from fish cage farming has been considered as one of the major factors disturbing benthic ecosystem, especially in semi-enclosed coastal waters. Recently offshore aquaculture in the vicinity of Jeju-do has been introduced to minimize that kind of negative impact. This study was conducted to investigate the ecological impacts of offshore aquaculture on the macrobenthic polychaete communities. A total of ten sampling works were carried out for 28 months, spanning from 10 days after starting giving feed to 3 months after stopping giving feed. During the study period, mean current velocity was quite strong with the range of 50 cm/s to 70 cm/s. TOC of surface sediment was constantly low. Significant changes in polychaete community were detected just three months after starting giving feed, which were the increase of the number of species and density at all stations. Up to 18 months after the start of farming, the amount of feed provided played an important role in the fluctuation of the number of species and density, especially at 0 m and 10 m stations. After reducing the amount of feed provided, dominance of some opportunistic species within 10 m distance from fish cages still lasted to the end of aquaculture. However, opportunistic species disappeared 3 months after the end of farming, which indicated the sign of recovery from the disturbance. From these results, the amount of food input and the period of cultivation were critical factors disturbing polychaete community and ensuing changes in this offshore and oligotrophic waters as well. In addition, study on the changes of polychaete community structure before and after fish farming showed more detailed changes in benthic ecological state than geochemical approach did.

• Journal of Bio-Environment Control
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• v.29 no.1
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• pp.80-88
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• 2020

This study was conducted to examine the effect by application method and concentration of the indole-3-butyric acid (IBA), which is auxin-based plant growth regulator, on the growth and runner plants production of strawberry in the greenhouse. The seedlings of strawberry were transplanted in the pot (150 ×135 × 90 mm) filled with coir medium on April 12, 2019. The IBA was applied with a foliar spray or drench as 50, 100, 150, and 200 mg·L^-1 (50 mL per plant), respectively. The treatment was started on April 29, 2019. The foliar spray and drench treatment of IBA were repeated at 2-week intervals for 9 weeks from the start date of treatment. At 9 weeks after treatment, the petiole length of mother plants was the shortest in the control. The number of runner plants showed a tendency to decreased in the foliar spray. The number of lateral buds showed a tendency to decreased in the IBA treatment, and the least in the foliar with 100 mg·L^-1. There was not significantly difference in the fresh and dry weights of the first and second runner plants. However, in the third runner plants, the fresh and dry weights were the greatest in the drench with 100 mg·L^-1. Therefore, when considering the growth of third runner plants and lateral bud suppression, the drench with the 100 mg·L^-1 could be better application method and concentration of IBA treatment for growth of the third runner plants and runner plants production of strawberry, and the results can be used as a basic research of plant growth regulator application to save the labor force and enhance the seedling quality in strawberry seedling stage.

• Archives of design research
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• v.16 no.3
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• pp.319-328
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• 2003

The purpose of this study was to investigate the actual size of tables and chairs in small restaurants. The examined objets were 48 restaurants serving flour-based, korean style and japanese style meals. Those restaurants are currently being operated at six universities in Seoul, and their sizes are limited under $66m^2$. The results of this study were as follows ; 1 . In small restaurants, the size of the seat for one person is $0.346m^2$on the average. The range of the size starts from $0.285m^2$to $0.403m^2$, showing a great difference. 2. The most common size of tables in small restaurants is the oblong style of $600{\times}900$ for four people. The smallest size is $440{\times}710$, while the biggest one is $700{\times}1000$, showing a great number of variation. If the most common size of tables is set to be the general size of tables in small restaurants, it will be $600{\times}900$. 3. The sizes of the seats for chairs used in small restaurants start from $270{\times}270$ to $430{\times}430$. The most common size of the seats for chairs is $400{\times}400$, only showing a small number. For the cases of tables with the most common size of $600{\times}900$, the width of the seats for chairs should be under 400. 4. For the height between the table and the seats of the chair, the distance from 270 to 300 is relevant. However, according to the results of the investigation, it seems that there are more cases of the distance from 250 to 260 than that from 270 to 300.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Korean Journal of Environmental Biology
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• v.40 no.3
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• pp.281-289
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• 2022

The effect of five different constant temperatures (18, 21, 24, 27, and 30±1℃) and a photoperiod of 14 : 10 (L :D) h on the reproduction parameters of Spodoptera frugiperda was studied. The longevity of adult female S. frugiperda decreased with increasing temperature (22.4 days at 21℃ and 13.9 days at 30℃) but not at 18℃. The pre-oviposition period and oviposition period was the shortest at 30℃ compared to the other temperatures. The total fecundity egg count was 887.4, 1,246.4, 1,348.9, 1,154.9, and 1,034.2 at 18, 21, 24, 27 and 30℃, respectively, during its life span. The survival rate of female S. frugiperda decreased rapidly after 13 days at 18℃, after 14 days at 21℃, after 15 days at 27℃, and after 9 days at 24℃, and 30℃. On the third day after the start of oviposition, 50% of the total fecundity was accomplished. In corn fields at less than the 10-leaf stage, the distribution of S. frugiperda egg masses was observed in the middle and lower plant regions, corresponding to 46.8% and 41.4% of the total egg masses, respectively. Egg masses were mostly found on the underside of the leaf blade (abaxial) of corn(66.7%). After releasing S. frugiperda adults on May 12, May 17, May 25, and May 30, the number of eggs per egg mass was 89.9, 88.5, 126.6, and 127.9, respectively. Egg masses of the subsequent generations of S. frugiperda were observed from late June, and the number of eggs per egg mass was 155.8 in late June, 270.7 in early July, and 303.5 in mid-July.

• Korean Journal of Poultry Science
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• v.31 no.4
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• pp.265-271
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• 2004

A feeding trial was conducted to investigate the influence of feeding Lactobacillus reuteri culture (LR) on productive performance, intestinal microflora and availability in laying hens. Four hundred and eighty, Isa-Brown layers, 49 weeks of age, were fed diets supplemented with LR at the level of 0 (control), 0.1, 0.2, and $0.4\%$ of the diets for eight weeks. Egg production and egg weight were measured daily. Feed intake was weighed every two weeks. Egg quality was measured three times at the start, mid-term, and end of the experiment. Intestinal microflora were examined for Lactobacillus spp., E. coli and Salmonella at the end of the experiment. Overall egg production was the highest in $0.2\%$ LR (P<0.05), but that of $0.1\%$ or $0.4\%$ LR treatments did not significantly differ from that of control. Egg weight was significantly higher in LR feeding group than the control (P<0.05). Daily egg mass was significantly higher in $0.2\%$ and $0.4\%$ LR treatments compared to the control and $0.1\%$ LR (P<0.05). The number of jumbo and extra large eggs were increased in LR supplemented groups, especially in $0.1\%$ LR. Feed intake of layers fed LR supplemented diets tended to be lower than the control. However, feed conversion ratio significantly improved in LR supplemented groups (P<0.05). Availability of dry matter and crude protein improved significantly in $0.4\%$ LR treatment (P<0.05). But, those of ether extract and crude ash were not significantly different among treatments. Eggshell breaking strength and eggshell thickness were not significantly influenced by LR supplementation, and Haugh unit and yolk index were also similar to the control. Total number of Lactobacillus spp. in ileum and cecum fed LR supplemented diets were significantly higher than those of the control (P<0.05). There were no significant differences in intestinal E. coli and Salmonella in all treatments. Therefore, it is concluded that dietary supplementation of Lactobacillus reuteri culture can improve the laying performance, feed efficiency and intestinal Lactobacillus.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.115-122
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• 2002

There is various points that should be improved in Fairness such as our contract practice to propose construction projects, project managing and the stakeholders' way of thinking and culture. We consider that the revision of construction related provisions and systems is required but even more, an overall change in business management through the implementation of Integrated Construction Information Management System that will enable the owner, which drives the project, and contractor sharing construction information is required. To mange construction related information in an integrated manner, designing information should be smoothly transferred to purchasing information, and changes are required in order to move ahead to process-oriented work system. Finally information created from various construction organizations should be delivered in an aligned and standardized manner as well. The domestic Nuclear Power Plant Construction has been accepting various technology transfers from U.S, France, Canada and UK, which enabled us to self-support technology and recently even proceeded to the phase exporting our technology to others. However, continuous effort is required to improve internal business efficiency and to respond to external environmental change such aselectricity market deregulation. Recently, in accordance with the result in number of CEO's intention to make progress in IT and improve business efficiency, the number of enterprises introducing Enterprise Resource Planning is increasing. ERP is an innovative tool which changes the way of performing work from organization and department orientation to process-orientation in order to optimize the resources, such as human and material resources, through out the Enterprise by performing BPR which will maximize overall business efficiency of the enterprise, such includes not only construction management, but also business management. KHNP continued to performing large scaled construction projects such as nuclear power plant construction for past 30 years and took the initiatives of large scale project management and Quality management ability in domestic industry by having independent capability of over all construction planning, purchasing and, construction and start up management etc. To maintain our leading position of improving construction management technology based on our accumulated project management experience and technology, KHNP included construction into our ERP project in purpose of innovating construction business. We would like to discuss the characteristics of nuclear construction business, project management system, information system infrastructure and information sharing system among construction related entities, and implementation practices for information system, and consider how to resolve our practice that should be improved in this thesis.

• 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