• Knowledge Management Research
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• v.24 no.2
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• pp.87-115
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• 2023

This study aims to examine the causal relationships between sales and employment for public technology-based startups. Although there is a limit to statistical generalization due to the poor understanding of the actual conditions of public technology start-up companies, these companies were analyzed by classifying them into high-growth companise, potential growth companies, and other companies. In order to understand the causal relationship, and to estimate the time required to be effective, panel vector autoregression was applied. As a result, the performance creation mechanism was identified as government supoort and private investment was mutually causal with employment, sales did not cause employment, and employment caused sales. In other words, it was found that employment plays an mediator role in public technology based startups' performance mechanism. In addition, private investment had the effect of improving employment and sales in the short time than governments support, and showed that firms with high employment can attract government support and private investment. This study are academically meaningful in that they empirically revealed the process of performance creation, whereas previous studies had only shown whether there was an effect on performance. It also has a policy contribution by suggesting the need for effective policy promotion by considering the 'employment' factor, such as human resource support, as more important.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.195-202
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• 2006

The infiltration route analysis is a military application using geospatial information technology. The result of the analysis would present vulnerable routes for potential enemy infiltration. In order to find the susceptible routes, optimal path search algorithms (Dijkstra's and $A^*$) were used to minimize the cost function, summation of detection probability. The cost function was produced by capability of TOD (Thermal Observation Device), results of viewshed analysis using DEM (Digital Elevation Model) and two related geospatial information coverages (obstacle and vegetation) extracted from VITD (Vector product Interim Terrain Data). With respect to 50m by 50m cells, the individual cost was computed and recorded, and then the optimal infiltration routes was found while minimizing summation of the costs on the routes. The proposed algorithm was experimented in Daejeon region in South Korea. The test results show that Dijkstra's and $A^*$ algorithms do not present significant differences, but A* algorithm shows a better efficiency. This application can be used for both infiltration and surveillance. Using simulation of moving TOD, the most vulnerable routes can be detected for infiltration purpose. On the other hands, it can be inversely used for selection of the best locations of TOD. This is an example of powerful geospatial solution for military application.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.6
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• pp.465-473
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• 2004

Purpose : The essential triad for nerve regeneration is nerve conduit, supporting cell and neurotrophic factor. In order to improve the peripheral nerve regeneration, we used polyglycolic acid(PGA) tube and brain-derived neurotrophic factor(BDNF) gene transfected Schwann cells in sciatic nerve defects of SD rat. Materials and methods : Nerve conduits were made with PGA sheet and outer surface was coated with poly(lactic-co-glycolic acid) for mechanical strength and control the resorption rate. The diameter of conduit was 1.8mm and the length was 17mm Schwann cells were harvested from dorsal root ganglion(DRG) of SD rat aged 1 day. Schwann cells were cultured on the PGA sheet to test the biocompatibility adhesion of Schwann cell. Human BDNF gene was obtained from cDNA library and amplified using PCR. BDNF gene was inserted into E1 deleted region of adenovirus shuttle vector, pAACCMVpARS. BDNF-adenovirus was multiplied in 293 cells and purified. The BDNF-Adenovirus was then infected to the cultured Schwann cells. Left sciatic nerve of SD rat (250g weighing) was exposed and 14mm defects were made. After bridging the defect with PGA conduit, culture medium(MEM), Schwann cells or BDNF-Adenovirus infected Schwann cells were injected into the lumen of conduit, respectively. 12 weeks after operation, gait analysis for sciatic function index, electrophysiology and histomorphometry was performed. Results : Cultured Schwann cells were well adhered to PGA sheet. Sciatic index of BDNF transfected group was $-53.66{\pm}13.43$ which was the best among three groups. The threshold of compound action potential was between 800 to $1000{\mu}A$ in experimental groups which is about 10 times higher than normal sciatic nerve. Conduction velocity and peak voltage of action potential of BDNF group was the highest among experimental groups. The myelin thickness and axonal density of BDNF group was significantly greater than the other groups. Conclusion : BDNF gene transfected Schwann cells could regenerate the sciatic nerve gap(14mm) of rat successfully.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Korean Journal of Poultry Science
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• v.40 no.1
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• pp.11-16
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• 2013

Salmonella is closely related with human health of modern society which has concern increased in livestock goods consumption as well as give economic damage throughout the chicken industry such as farm, hatchery, slaughter house and processing plant. From 2007 to 2011, this study investigated Salmonella prevalence from 200 old hen delivery trucks which deliver old egg-laying hens and broiler breeders in Korea. The prevalence of Salmonella species was 38.0% in old hen delivery truck. Serogroup C1 was the most frequently detected serogroup of Salmonella, followed by the serogroups D1, C2 and B. A total of 25 serotypes were identified and Salmonella infantis was the most frequently isolated serotype. In addition, we applied disinfectant to old hen delivery truck for the reduction of Salmonella contamination. The disinfectant consists of formaldehyde, glutaladehyde and quaternary ammonium compound was applied to the trucks. Salmonella isolation rate was significantly decreased after disinfection from 38.0% to 7.5%. Disinfectant could not effectively reduce Salmonella contamination at a dilution of 1:200 which is recommended by manufacturer, but Salmonella isolation rate was significantly decreased at a dilution of 1:50. Since old hen delivery truck could be a potential vector to carry Salmonella into farms and abattoirs, chicken delivery truck should be disinfected thoroughly and sufficiently to control contamination of farms and abattoirs.

• Korean journal of applied entomology
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• v.47 no.3
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• pp.287-292
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• 2008

Repellent and acaricidal activities of eucalyptus oil, permethrin, and DEET against Leptotrombidium pallidum larvae, which are a vector transmitting tsutsugamushi disease, were evaluated under laboratory conditions using a filter paper impregnated method. The $LD_{50}$ values of eucalyptus oil and DEET were 0.025 and 0.018 $mg/cm^2$, respectively while that of permethrin was higher than 0.2 $mg/cm^2$. In the repellency test of these materials at 6.14 $mg/cm^2$, eucalyptus oil gave complete repellency, and the larvae crossed the treated zone killed. But permethrin showed 60% repellency at 9.20 $mg/cm^2$ and the mites croosed the zone were not killed. The percent repellency of DEET at 0.53 $mg/cm^2$ was 8.3 and 2.8 times higher than that of permethrin and eucalyptus oil, respectively. The acaricidal activities of emulsifiable concentrates-pump sprayers containing the eucalyptus oil as an active ingredient were assayed. The emulsifiable concentrates containing 1% and 3% eucalyptus oil showed weak mortality at 1 hour after treatment, while all ones containing more than 6% oil produced 100% activity against L. pallidum larvae. The mortality also increased as exposure time to the concentrates increase. These results suggest that the potential of eucalyptus oil highly expected to be used as a control or repellent agent against L. pallidum larvae may be very high.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Korean Journal of Microbiology
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• v.46 no.1
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• pp.21-26
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• 2010

As the causative agent of Anthrax, Bacillus anthracis causes an acute fatal disease in herbivores such as cattle, sheep, and horses as well as humans. The therapeutics and prevention of anthrax currently available are based on antibiotics and the live attenuated vaccine strains, which may be problematic due to the emergency of antibiotic resistant strains or residual virulence in those vaccine strains. Therefore, it has been required to develop novel therapeutics and vaccines which are safer and applicable to humans. Recently, the development of the multivalent vaccine targeting both spores and vegetative cells of B. anthracis along with anthrax toxin has been reported. In our attempts to screen potential candidates for those multivalent vaccines, the whole genomic expression library of B. anthracis was constructed in this study. To the end, the partial digests of the genomic DNA from B. anthracis (ATCC 14578) with Sau3AI were ligated with the inducible pET30abc expression vectors, resulting in approximately $1{\times}10^5$ clones in E. coli BL21(DE3). The redundancy test by DNA nucleotide sequencing was performed for the randomly selected 111 clones and found 56 (50.5%) B. anthracis genes, 17 (15.3%) vector sequences, and 38 (34.2%) unknown genes with no sequence homology by BLAST. An inducible expression of the recombinant proteins was confirmed by Western blot. Interestingly, some clones could react with the antiserum against B. anthracis. These results imply that the whole genomic library constructed in this study can be applied for analyzing the immunomes of B. anthracis.