• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.15-26
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• 2018

This study examines the effects of failure experience, re-startup's motivation, government support business and education for re-startup on the performance of re-startup firms after failure. In addition, we analyzed how the above factors affect the performance of re-startup firms by the condition of debt and credit problems. As a result of the analysis, the failure experience had no significant effect on the re-startup performance regardless of the credit problem, while re-startup's motivation, government support business and education for re-startup had a significant effect on re-startup firms' performance. In the re-startup group with the credit problem, the re-startup's motivation and the failure experience had a significant influence on the re-startup firms' performance. On the other hand, in the group that did not solve the credit problem, the re-startup's motivation and the failure experience had no significant effect on the re-startup performance, but the government support business and education for re-startup had a significant effect on re-startup performance. The results of this study are as follows: First, it shows that the characteristics of re-startups and the determinants of re-startups are different according to credit problems. Second, this study shows that it takes 56 months on average from the close of business to the re-start, and it may take more than 7 years due to the credit problems and bankruptcy. This suggests the necessity to consider re-startup in the concept of obsolete in consideration of time, when studying the direct/indirect influence of failure experience and re-startup, and establishing policy.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.154-158
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• 1999

Purpose : The purpose of this study was to find the optimum TE value for enhancing $T_2^{*}$ weighting effect and minimizing the SNR degradation and to compare the BOLD effects according to the changes of TE in 1.5T and 3.0T MRI systems. Materials and Methods : Healthy normal volunteers (eight males and two females with 24-38 years old) participated in this study. Each volunteer was asked to perform a simple finger-tapping task (sequential opposition of thumb to each of the other four fingers) with right hand with a mean frequency of about 2Hz. The stimulus was initially off for 3 images and was then alternatively switched on and off for 2 cycles of 6 images. Images were acquired on the 1.5T and 3.0T MRI with the FLASH (fast low angle shot) pulse sequence (TR : 100ms, FA : $20^{\circ}$, FOV : 230mm) that was used with 26, 36, 46, 56, 66, 76ms of TE times in 1.5T and 16, 26, 36, 46, 56, 66ms of TE in 3.0T MRI system. After the completion of scan, MR images were transferred into a PC and processed with a home-made analysis program based on the correlation coefficient method with the threshold value of 0.45. To search for the optimum TE value in fMRI, the difference between the activation and the rest by the susceptibility change for each TE was used in 1.5T and 3.0T respectively. In addition, the functional $T_2^{*}$ map was calculated to quantify susceptibility change. Results : The calculated optimum TE for fMRI was $61.89{\pm}2.68$ at 1.5T and $47.64{\pm}13.34$ at 3.0T. The maximum percentage of signal intensity change due to the susceptibility effect inactivation region was 3.36% at TE 66ms in 1.5T 10.05% at TE 46ms in 3.0T, respectively. The signal intensity change of 3.0T was about 3 times bigger than of 1.5T. The calculated optimum TE value was consistent with TE values which were obtained from the maximum signal change for each TE. Conclusion : In this study, the 3.0T MRI was clearly more sensitive, about three times bigger than the 1.5T in detecting the susceptibility due to the deoxyhemoglobin level change in the functional MR imaging. So the 3.0T fMRI I ore useful than 1.5T.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.1-15
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• 2016

Startups in Korea are experiencing for themselves the laws of survival through competition in the local and international market, and are performing active business movements based on these. Korea's economic growth rate is 2.6% due to the slump in the domestic demand and reduced exports brought by the MERSC incident in 2015. The Korea Development Institute has estimated the economic growth rate in 2016 to be around 3.0%. South Korea's economy is facing the crisis of low-growth solidification due to the decrease in economic growth, and it is forecasted that growth without employment and polarization will worsen. Startups in the high-tech industrial generation of a particular field wherein the market environment is rapidly changing must maintain a competitive advantage with the capabilities and functions exclusive to them. It is very important that they maintain a competitive edge by utilizing the capabilities exclusive to startup companies. Likewise, the accumulation of resources is also crucial in determining the success of a startup business. In a poor local startup ecosystem, majority of the startup companies are performing their business activities while striving for survival, rather than success. About 80% are struggling to survive and are failing to overcome the "Death Valley" faced 3-5 years after establishing the company. Since majority of the startups fail to achieve results during the initial stages of foundation, the importance of research on business activities and achievement during the early stages of establishment is being raised. In accordance to this, this research has performed an actual analysis on how the activities of startups during their preparation phase and early stages affect their achievements. A survey was done on the CEOs or executives (people in a position to make decisions) of local small and medium-sized enterprises that are considered start-ups, and 203 valid data were collected and analyzed. Results showed that the discoveries and utilized activities necessary for the businesses of startups have a significant impact on their achievement through the entrepreneur resources and external partners' cooperation; additionally, the related implications were discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.49-63
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• 2016

In this study, we analyzed the effects of regional hydrologic variability during warm season (June-September) in South Korea due to ENSO (El $Ni{\tilde{n}}o$-Southern Oscillation) pattern changes over the Tropical Pacific Ocean (TPO). We performed composite analysis (CA) and statistical significance test by Student's t-test using observed hydrologic data (such as, precipitation and streamflow) in the 113 sub-watershed areas over the 5-Major River basin, in South Korea. As a result of this study, during the warm-pool (WP) El $Ni{\tilde{n}}o$ year shows a significant increasing tendency than normal years. Particularly, during the cold-tongue (CT) El $Ni{\tilde{n}}o$ decaying years clearly decreasing tendency compared to the normal years was appeared. In addition, the La $Ni{\tilde{n}}a$ years tended to show a slightly increasing tendency and maintain the average year state. In addition, from the result of scatter plot of the percentage anomaly of hydrologic variables during warm season, it is possible to identify the linear increasing tendency. Also the center of the scatter plot shows during the WP El $Ni{\tilde{n}}o$ year (+17.93%, +26.99%), the CT El $Ni{\tilde{n}}a$ year (-8.20%, -15.73%), and the La $Ni{\tilde{n}}a$ year (+8.89%, +15.85%), respectively. This result shows a methodology of the tele-connection based long-range water resources prediction for reducing climate forecasting uncertainty, when occurs the abnormal SSTA (such as, El $Ni{\tilde{n}}o$ and La $Ni{\tilde{n}}a$) phenomenon in the TPO region. Furthermore, it can be a useful data for water managers and end-users to support long-range water-related policy making.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.397-432
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• 2015

This paper investigates the effects of technology collaboration between the main supplier and buyer on buyer's new product development under dynamic environment. Based on 428 Korean manufacturing firms, we conducted regression analysis. The technology collaboration between the main supplier and buyer is adopted as a independent variable and quality, cost and lead time performance of new product development projects are used as dependents variables. Environment dynamic is also used as a moderate variables. We found that the in general, technology collaboration is positively associated with the performance of buyers' new product development, but in the high degree of dynamic environment, technology collaboration is negatively associated with the performance of buyers' new product development unlike our expectation. Thus, we divide our sample into two groups; shipbuilding industry with the low degree of environment dynamic and electronic and IT device industry with the high degree of environment dynamic and conducted a post hoc analysis. As a result, in ship building industry, the technology collaboration is significant to improve NPD projects performance, while in electronic and IT device industry, the technology collaboration with a main supplier is not significant as well as coefficient is negative. In that, under the highly dynamic condition with the fast change of technology and products obsolescence the NPD collaboration with the main supplier does not works unlike a stable environment. This implies that the NPD attributes of buyer are different by their environmental factor and the fit between given environmental feature and the collaboration synergy is critical factor for improving the effect of NPD collaboration between supplier and buyer.

• Research in Plant Disease
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• v.21 no.4
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• pp.280-289
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• 2015

Maize (Zea mays L.) is an economically important crop in worldwide. While the consumption of the maize is steadily increasing, the yield is decreasing due to continuous mono-cultivation and infection of soil-borne fungal pathogens such as Fusarium species. Recently, stalk rot disease in maize, caused by F. subglutinans and F. temperatum has been reported in Korea. In this study, we isolated bacterial isolates in rhizosphere soil of maize and subsequently tested for antagonistic activities against F. subglutinans and F. temperatum. A total of 1,357 bacterial strains were isolated from rhizosphere. Among them three bacterial isolates (GC02, GC07, GC08) were selected, based on antagonistic effects against Fusarium species. The isolates GC02 and GC07 were most efficient in inhibiting the mycelium growth of the pathogens. The three isolates GC02, GC07 and GC08 were identified as Bacillus methylotrophicus, B. amyloliquefaciens and B. thuringiensis using 16S rRNA sequence analysis, respectively. GC02 and GC07 bacterial suspensions were able to suppress over 80% conidial germination of the pathogens. GC02, GC07 and GC08 were capable of producing large quantities of protease enzymes, whereas the isolates GC07 and GC08 produced cellulase enzymes. The isolates GC02 and GC07 were more efficient in phosphate solubilization and siderophore production than GC08. Analysis of disease suppression revealed that GC07 was most effective in suppressing the disease development of stalk rot. It was also found that B. methylotrophicus GC02 and B. amyloliquefaciens GC07 have an ability to inhibit the growth of other plant pathogenic fungi. This study indicated B. methylotrophicus GC02 and B. amyloliquefaciens GC07 has potential for being used for the development of a biological control agent.

• The Journal of Engineering Geology
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• v.27 no.3
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• pp.331-343
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• 2017

In this study to improve stability, workability and economics of the H-Pile+Earth plate or H-Pile+Earth plate+Cutoff grouting currently in use, we had developed HCS method belonging to the retaining wall which is consisting of a combination H-Pile, Plastic Sheet Pile and Steel Square Pipe for gap maintenance and reinforcement of flexible plastic Sheet Pile, and the behavior of each member composing HCS method is investigated by three-dimensional finite element analysis. To numerically analyze the behavior of the HCS method, we have performed extensive three-dimentional finite element analysis for three kinds of plastic Sheet Pile size, two kinds of H-Pile size and three kinds of H-Pile installation interval, one kinds of Steel Square Pipe and three kinds of Steel Square Pipe installation interval. After analyzing the numerical results, we found that the combinations of $P.S.P-460{\times}131.5{\times}7t$ (PS7) and H-Pile $250{\times}250{\times}9{\times}14$ (H250), $P.S.P473{\times}133.5{\times}9t$ (PS9) and H-Pile $300{\times}200{\times}9{\times}14$ (H300) is the most economical because these combinations are considered to have a stress ratio (=applied stress/allowable stress) close to that as the stiffness of H-Pile, plastic Sheet Pile and Steel Square Pipe composite increased, the horizontal displacement of the retaining wall and the vertical displacement of the upper ground decreased. Especially, due to the arching effects caused by the difference in stiffness between H-Pile and plastic Sheet Pile, a large part of the earth pressure acting on plastic Sheet Pile caused a stress transfer to H-Pile, and the stress and displacement of plastic Sheet Pile were small. Through this study, we can confirm the behavior of each member constituting the HCS method, and based on the confirmed results of this study, it can be used to apply HCS method in reasonable, stable and economical way in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.243-253
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• 2018

This study aimed to investigate the effects of DCI on glucose control, quality of life(SF-36 Version 2.0, Korean) and SDSCA(Summary of Diabetes Self-Care Activities) in patients with type 2 diabetes mellitus. A randomized, double-blind, placebo-controlled study was performed on 46 patients with HbA1c 7.0% taking triple anti-diabetic drug regimen who visited the department of Endocrinology and Metabolism in Chungnam National University Hospital between March 2015 and May 2016. As a result, DCI treatment in the intervention group resulted in significantly reduced HbA1c levels $8.75{\pm}0.79%$(baseline), $8.36{\pm}1.03%$(after 12weeks), and $8.65{\pm}0.81%$(after 24weeks). However, patients in the control group did not show any significant change. Interestingly, both DCI treatment group and the control group significantly showed improvements in SDSCA. Participants in the intervention group showed a small yet significant improvement in their only fasting blood glucose test in SDSCA and revealed significant increase in the quantitative levels of quality of life, from $73.05{\pm}16.85$ to $82.74{\pm}10.68$. By using pathway analysis, improvement of SDSCA scores(${\beta}=-0.505$, t=-2.743) was the most influential factor to the fasting blood glucose. The quality of life of patients with type 2 diabetes mellitus was affected by changes of SDSCA scores(${\beta}=0.411$, t=2.024) and fasting c-peptide(${\beta}=-0.445$, t=-2.668) in DCI treatment group. In conclusion, treatment of DCI effectively improved glucose control in patients with type 2 DM(HbA1c level>7.0%) after 12 weeks of treatment, although it had no impact on glucose control after 24 weeks of treatment. Improved glucose control may encourage diabetic patients to conduct self-care activities and improve the quality of life. Based on the present study, we suggest that diabetes self-management, as well as consideration of comprehensive laboratory findings, may be important factor in regulating the quality of life in type 2 DM patients.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.