• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.97-110
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• 2019

This study analyzed various factors of influence affecting the will to start a business and established and empirically analyzed a research model to see which factors significantly affect the will to start a business. To this end, we investigated the general characteristics and experiences of individuals, conducted a study on the will to start a business, and analyzed the entrepreneurship passion for startups, the ability to find business opportunities, and the ability to connect with partner companies. The intent to start a business survey was investigated in a recertive style with a 7 point scale, and the reliability and feasibility review were analyzed through the PLS analysis method, which enables the implementation of a measurement model and a structural model. To collect valid data, the survey was conducted using an entrepreneurial curriculum class hours to collect and analyze 421 data. In summary, the results are as follows: First, college students have many opportunities to develop their capabilities through competitions held by universities and support institutions, and by utilizing them, they have no fear of starting a business. Second, the ability of students to discover product clients themselves has been improved by fostering entrepreneurship in the special lectures on startup in universities. Third, it can be seen that it has received various information on startups from support agencies to enhance its commitment to startups. The implications are as follows. First, they should foster entrepreneurship among college students by offering practical oriented courses that can broaden their understanding of startups. Second, it needs to be improved from entrepreneurial enthusiasm to a program that can grow into a company that can collaborate with partner companies and confirm its commitment to corporate establishment and product development and determine market opportunities. Third, it is necessary to establish an ecosystem of start-ups that can carry out systematic planning and performance management as it is weak to carry out projects with will to startups.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Food Hygiene and Safety
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• v.26 no.1
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• pp.70-75
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• 2011

A new and improved analytical method involving alkaline pyridine extraction was proposed to quantity chlorophyll contents in syrup and candy type chlorella products. The performance of analytical method was compared with the conventional Korea food standard method which involves acetone extraction. The application of sonication chlorophyll extraction form alkaline pyridine sample was also explored. The analytical procedure was validated by evaluating accuracy, precision and reproducibility. For liquid samples, the pyridine extraction method showed higher accuracy and precision compared to acetone extraction method. The CV values of pyridine extract method and the acetone extraction method were 18.82 and 40.0, and the accuracy to theoretical values were 106.3% and 78.1%, respectively. When sonication extraction method was applied to the pyridine extraction, the precision was improved as indicated by reduced CV values from 18.82 to 11.36. The improved performance of pyridine-sonication extraction was also validated by recovery test of chlorophyll that was previously spiked into the sample matrix. For solid matrix, the pyridine extraction method showed better performance in analysis of chlorophyll in solid food matrix (CV = 7.05) compared to conventional acetone extraction method (CV = 30.0). However, the accuracy to theoretical values of pyridine and acetone extraction methods only showed only 62.7% an 40%, respectively. The relatively low accuracy of pyridine extraction method (62.7%) was improved to 99.4% by applying additional sonication extraction method. The improved performance of applying additional sonication extraction was validated by standard deviation, CV values and accuracy to theoretical values.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.45-52
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• 2020

In this paper, we propose an intention to study the purchase of smart music by Chinese consumers. To study the influence of the originality and usefulness of intelligent music products on the purchase intention of Chinese consumers, and to explore how the originality and usefulness of intelligent music products affect the purchase intention. To achieve this goal, 372 questionnaires were collected through the Internet for frequency analysis, factor analysis, confidence analysis and structural equation analysis of data collection, and were carried out by SPSSV22.0 and AMOSV22.0 methods. Research the validation of assumptions in the model to reveal the psychological and behavioral responses of consumers to smart music products. The results show that the originality and usefulness of new products not only directly affect the purchase intention of Chinese consumers, but also indirectly affect their purchase intention by enhancing their attractiveness. The conclusion of this study is of guiding significance for the development of intelligent music product development and marketing strategy.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.3 no.1
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• pp.32-37
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• 2017

Cis-(1S,4S)-4-(3,4-dichlorophenyl)-1,2,3,4-tertrahydro-N-methyl-1-naphthalenamine (sertraline) hydrochloride from among selective serotonin reuptake inhibitors (SSRIs) is a treatment of major depression. For the differential diagnosis by metabolizing serotonin in a patient with neurological disorders, the radiolabeled $^{11}C$-sertraline was developed for non-invasive positron emission tomography in living brain and use the evaluation of new drug for SSRIs. We release the results of a fast and easy radiolabeling method applied a one-step loop method with $[^{11}C]CH_3OTf$ for routine clinical applications of $^{11}C$-sertraline. 1 mg of a precursor for $^{11}C$-sertraline in 0.1 mL DMF and $5{\mu}L$ of 1N NaOH, were injected into the loop of semi-prep high-performance liquid chromatography (HPLC). $[^{11}C]CH_3OTf$ was passed through the loop at room temperature (RT). The $^{11}C$-sertraline was separated by the semi-preparative HPLC. $^{11}C$-sertraline was eluted at 28.0 min was collected and evaluated by analytical HPLC and mass spectrometer. The total radiolabeling efficiency of $^{11}C$-sertraline was $30.7{\pm}8.7%$. The specific activity was $64.8{\pm}51.4GBq/{\mu}mol$. The radiochemical and chemical purities were higher than 99%. The mass spectrum of the product showed m/z peaks at 307.1 (M+1), indicating the mass of sertraline. By the one-step loop method with $[^{11}C]CH_3OTf$, $^{11}C$-sertraline could be quickly and easily prepared for clinical application.

• Resources Recycling
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• v.21 no.5
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• pp.79-87
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• 2012

With the increasing size and universalization of lithium-ion batteries, the development of cathode materials has emerged as a critical issue. The energy density of 18650 cylindrical batteries had more than doubled from 230 Wh/l in 1991 to 500 Wh/l in 2005. The energy capacity of most products ranges from 450 to 500Wh/l or from 150 to 190 Wh/kg. Product developments are focusing on high capacity, safety, saved production cost, and long life. As Co is expensive among the cathode active materials $LiCoO_2$, to increase energy capacity while decreasing the use of Co, composites such as $LiMn_2O_4$, $LiCo_{1/3}N_{i1/3}Mn_{1/3}O_2$, $LiNi_{0.8}Co_{0.15}Al_{0.05}O_2$, and $LiFePO_4$-C (167 mA/g) are being developed. Furthermore, many studies are being conducted to improve the performance of battery materials to meet the requirement of large capacity output density such as 500Wh/kg for electric bicycles, 1,500Wh/kg for electric tools, and 4,000~5,000Wh/kg for EV and PHEV. As new cathodes active materials with high energy capacity such as graphene-sulfur composite cathode materials with 600 Ah/kg and the molecular cluster for secondary battery with 320 Ah/kg are being developed these days, their commercializations are highly anticipated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.481-491
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• 2016

This study analyzed the customer's revisit concepts and storytelling by restaurant customers to identify the elements that can attract the attention of customers. The restaurant concept or advertising, restaurant decor will also have to change to emphasize the comparative advantage of the features in the menu of a restaurant or other restaurants / service that tells a story. Membership cards or money, or the same convenience and use of the restaurant non-monetary 'transition cost' can compensate for the negative emotions and low satisfaction level of restaurant customers and help them choose to revisit the restaurant. Therefore, if such a transition takes full account of the effects of the cost to the customer, it can be used as an effective means. In class restaurants, such as the food and customer service, the increased levels of the restaurant atmosphere and empirical elements, such as store concept and physical environment, can improve the positive consumer sentiment, strengthen the customer satisfaction and have a positive effect on the customers' revisit intention. It is also important to improve the level of visual texture using light. In addition, positive consumer sentiment can be induced using the store concept, the physical environment, and experiential elements. In other words, membership cards, mileage points, and various financial and non-financial inducements as a marketing tool will have a positive impact on the customer's revisit intention.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.17-28
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• 1999

In this paper, an ASIC emulation system called ACE (ASIC Emulator) is proposed. It can produce the prototype of target ASIC in a short time and verify the function of ASIC circuit immediately The ACE is consist of emulation software in which there are EDIF reader, library translator, technology mapper, circuit partitioner and LDF generator and emulation hardware including emulation board and logic analyzer. Technology mapping is consist of three steps such as circuit partitioning and extraction of logic function, minimization of logic function and grouping of logic function. During those procedures, the number of basic logic blocks and maximum levels are minimized by making the output to be assigned in a same block sharing product-terms and input variables as much as possible. Circuit partitioner obtain chip-level netlists satisfying some constraints on routing structure of emulation board as well as the architecture of FPGA chip. A new partitioning algorithm whose objective function is the minimization of the number of interconnections among FPGA chips and among group of FPGA chips is proposed. The routing structure of emulation board take the advantage of complete graph and partial crossbar structure in order to minimize the interconnection delay between FPGA chips regardless of circuit size. logic analyzer display the waveform of probing signal on PC monitor that is designated by user. In order to evaluate the performance of the proposed emulation system, video Quad-splitter, one of the commercial ASIC, is implemented on the emulation board. Experimental results show that it is operated in the real time of 14.3MHz and functioned perfectly.