• Explosives and Blasting
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• v.30 no.2
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• pp.29-42
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• 2012

Variables influencing the free face movement due to rock blasting include the physical and mechanical properties, in particular the discontinuity characteristics, explosive type, charge weight, burden, blast-hole spacing, delay time between blast-holes or rows, stemming conditions. These variables also affects the blast vibration, air blast and size of fragmentation. For the design of surface blasting, the priority is given to the safety of nearby buildings. Therefore, blast vibration has to be controlled by analyzing the free face movement at the surface blasting sites and also blasting operation needs to be optimized to improve the fragmentation size. High-speed digital image analysis enables the analyses of the initial movement of free face of rock, stemming optimality, fragment trajectory, face movement direction and velocity as well as the optimal detonator initiation system. Even though The high-speed image analysis technique has been widely used in foreign countries, its applications can hardly be found in Korea. This thesis aims at carrying out a fundamental study for optimizing the blast design and evaluation using the high-speed digital image analysis. A series of experimentation were performed at two large surface blasting sites with the rock type of shale and granite, respectively. Emulsion and ANFO were the explosives used for the study. Based on the digital images analysis, displacement and velocity of the free face were scrutinized along with the analysis fragment size distribution. In addition, AUTODYN, 2-D FEM model, was applied to simulate detonation pressure, detonation velocity, response time for the initiation of the free face movement and face movement shape. The result show that regardless of the rock type, due to the displacement and the movement velocity have the maximum near the center of charged section the free face becomes curved like a bow. Compared with ANFO, the cases with Emulsion result in larger detonation pressure and velocity and faster reaction for the displacement initiation.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.337-343
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• 2014

Purpose : The aim of this study is to evaluate unwanted scattered dose to ovary by scattering and leakage generated from treatment fields of Tomotherapy for childbearing woman with breast cancer. Materials and Methods : The radiation treatments plans for left breast cancer were established using Tomotherapy planning system (Tomotherapy, Inc, USA). They were generated by using helical and direct Tomotherapy methods for comparison. The CT images for the planning were scanned with 2.5 mm slice thickness using anthropomorphic phantom (Alderson-Rando phantom, The Phantom Laboratory, USA). The measurement points for the ovary dose were determined at the points laterally 30 cm apart from mid-point of treatment field of the pelvis. The measurements were repeated five times and averaged using glass dosimeters (1.5 mm diameter and 12 mm of length) equipped with low-energy correction filter. The measures dose values were also converted to Organ Equivalent Dose (OED) by the linear exponential dose-response model. Results : Scattered doses of ovary which were measured based on two methods of Tomo helical and Tomo direct showed average of $64.94{\pm}0.84mGy$ and $37.64{\pm}1.20mGy$ in left ovary part and average of $64.38{\pm}1.85mGy$ and $32.96{\pm}1.11mGy$ in right ovary part. This showed when executing Tomotherapy, measured scattered dose of Tomo Helical method which has relatively greater monitor units (MUs) and longer irradiation time are approximately 1.8 times higher than Tomo direct method. Conclusion : Scattered dose of left and right ovary of childbearing women is lower than ICRP recommended does which is not seriously worried level against the infertility and secondary cancer occurrence. However, as breast cancer occurrence ages become younger in the future and radiation therapy using high-precision image guidance equipment like Tomotherapy is developed, clinical follow-up studies about the ovary dose of childbearing women patients would be more required.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.5
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• pp.53-68
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• 2014

This study is aimed at analyzing the factors that affect the behaviors of employee's investment, such as a decision making process in a variety of views and proving the extent of how those factors influence on their investment. The basic assumption is that the preceding factors that can be determined by the personal investment propensity, a psychological factor asserted by Behavior Financial Theory and financial-economic and social environment. This study uses Hershey's Investment Behavior Model(2007) as the main analysis tool to explain the investment behavior of individuals and deals with personal investment inclination in the psychological perspective of overconfidence, self-control and the risk tolerance propensity and add the financial and economic factors in terms of financial literacy and economic distress. Also the new preceding social environmental factors like social interaction and the effect of reference group are added to make this research to be more precise. This study analyze the adjustment effect of professional invest-consulting service that affect the fluctuation influence between the individual variables(those factors) and subordination variable(the level of investment satisfaction). The study reveals that overconfidence and self-control in direct ways have a positive effect on the level of investment satisfaction in terms of investment behavior and economic distress has a negative effect on the level of investment satisfaction. The adjustment effect provided by financial experts in investment consulting service is affirmed as the critical factor that increase the influence between self-control and the level of investment satisfaction. To conclude, the research reveals that the psychological factors are the main criteria when the workers as employees have to make investment decisions. To make investors be reasonable, a systematic financial education system provided by experts is needed from the early adolescent stages and financial companies should develop the relevant services of consulting service department as a key financial sector and financial investment products and consulting program and marketing tool pertinent to investors ages, vocational traits and their inclinations.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.23 no.6
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• pp.283-290
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• 2013

A stoichiometric mixture of evaporating materials for $MgGa_2Se_4$ single crystal thin films was prepared from horizontal electric furnace. To obtain the single crystal thin films, $MgGa_2Se_4$ mixed crystal was deposited on thoroughly etched semi-insulating GaAs(100) substrate by the Hot Wall Epitaxy (HWE) system. The source and substrate temperatures were $610^{\circ}C$ and $400^{\circ}C$, respectively. The crystalline structure of the single crystal thin films was investigated by double crystal X-ray diffraction (DCXD). The temperature dependence of the energy band gap of the $MgGa_2Se_4$ obtained from the absorption spectra was well described by the Varshni's relation, $E_g(T)=2.34 eV-(8.81{\times}10^{-4}eV/K)T^2/(T+251K)$. The crystal field and the spin-orbit splitting energies for the valence band of the $MgGa_2Se_4$ have been estimated to be 190.6 meV and 118.8 meV, respectively, by means of the photocurrent spectra and the Hopfield quasicubic model. These results indicate that the splitting of the ${\Delta}so$ definitely exists in the ${\Gamma}_5$ states of the valence band of the $MgGa_2Se_4$/GaAs epilayer. The three photocurrent peaks observed at 10 K are ascribed to the $A_{1^-}$, $B_{1^-}$exciton for n = 1 and $C_{27}-exciton$ peaks for n = 27.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.18 no.5
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• pp.217-224
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• 2008

A stoichiometric mixture of evaporating materials for $ZnIn_2Se_4$ single crystal thin films was prepared from horizontal electric furnace. To obtain the single crystal thin films, $ZnIn_2Se_4$ mixed crystal was deposited on thoroughly etched semi-insulating GaAs(100) substrate by the Hot Wall Epitaxy (HWE) system. The source and substrate temperatures were $630^{\circ}C$ and $400^{\circ}C$, respectively. The crystalline structure of the single crystal thin films was investigated by the photoluminescence and double crystal X-ray diffraction (DCXD). The carrier density and mobility of $ZnIn_2Se_4$ single crystal thin films measured from Hall effect by van der Pauw method are $9.41\times10^{16}cm^{-3}$ and $292cm^2/v{\cdot}s$ at 293 K, respectively. The temperature dependence of the energy band gap of the $ZnIn_2Se_4$ obtained from the absorption spectra was well described by the Varshni's relation, $E_g(T)=1.8622eV-(5.23\times10^{-4}eV/K)T^2/(T+775.5K)$. The crystal field and the spin-orbit splitting energies for the valence band of the $ZnIn_2Se_4$ have been estimated to be 182.7 meV and 42.6 meV, respectively, by means of the photocurrent spectra and the Hopfield quasicubic model. These results indicate that the splitting of the ${\Delta}so$ definitely exists in the ${\Gamma}_5$ states of the valence band of the $ZnIn_2Se_4/GaAs$ epilayer. The three photo current peaks observed at 10 K are ascribed to the $A_{1}-$, $B_{1}-exciton$ for n = 1 and $C_{27}-exciton$ peaks for n = 27.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.316-326
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• 2014

Determination of the late marginal heading date (LMHD), which would allow estimation of the late marginal seeding date and the late marginal transplanting date, would help identification of potential double cropping areas and, as a result, establishment of cropping systems. The objective of this study was to determine the LMHD at 51 sites in Korea. For these sites, weather data were obtained from 1971 to 2000 and from 1981 to 2010, which represent past and current normal climate conditions, respectively. To examine crop productivity on the LMHD, climatic yield potential (CYP) was determined to represent the potential yield under a given climate condition. The LMHD was calculated using accumulated temperature for 40 days with threshold values of $760^{\circ}C$, $800^{\circ}C$, $840^{\circ}C$ and $880^{\circ}C$. The value of CYP on a given LMHD was determined using mean temperature and sunshine duration for 40 days from the LMHD. The value of CYP on the LMHD was divided by the maximum value of CYP (CYPmax) in a season to represent the relative yield on the LMHD compared with the potential yield in the season. Our results indicated that the LMHD was delayed at most sites under current normal conditions compared with past conditions. Spatial variation of the LMHD differed by the threshold temperature. Overall, the minimum value of CYP/CYPmax was 81.8% under all of given conditions. In most cases, the value of CYP/CYPmax was >90%, which suggested that yield could be comparable to the potential yield even though heading would have occurred on the LMHD. When the LMHD could be scheduled later without considerable reduction in yield, the late marginal transplanting date could also be delayed accordingly, which would facilitate doublecropping in many areas in Korea. Yield could be affected by sudden change of temperature during a grain filling period. Yet, CYP was calculated using mean temperature and sunshine duration for 40 days after heading. Thus, the value of CYP/CYPmax may not represent actual yield potential due to change of the LMHD, which suggested that further study would be merited to take into account the effect of weather events during grain filling periods on yield using crop growth model and field experiments.

• The Journal of Small Business Innovation
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• v.19 no.2
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• pp.53-68
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• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.