• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• 제15권4호
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• pp.193-216
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• 2020

Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.

• Journal of Intelligence and Information Systems
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• 제27권3호
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• 제25권4호
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of Intelligence and Information Systems
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• 제24권2호
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of radiological science and technology
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• 제38권3호
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• pp.245-252
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• 2015

To obtain the best SNR (signal to noise ratio) due to changes in CTDI (computed tomography dose index) made for the purpose of setting the optimum image obtained by reducing the dose in abdominal CT. Abdominal CT scans of 59 patients a $400-499cm^2$ (n = 12), $500-599cm^2$ (n = 21), $600-699cm^2$ (n = 17), $700-799cm^2$ (n = 9) were separated by four groups and the effective dose was used in the Excel to get the area of the patient using the ImageJ program. Patients of CTDI, DLP, SNR, the effective dose were analyzed. Abdominal CT area was increased to 13 mGy in CTDI is 7.3 mGy, DLP to 732 in $394.4mGy{\cdot}cm$, also effective dose was 5.9 mSv increase in 11mSv. SNR is 15 dB was maintained at 12.7. CTDI according to the average of the abdominal area of 8.9 mGy, the average of the DLP was $481.54mGy{\cdot}cm$, the effective dose is calculated to be 7.2 mSV. Effective dose was calculated by multiplying the load factor of DLP in the abdomen showed no statistically significant difference of (p < .05), there was a significant difference in SNR (p > . 05). To improve image quality of abdominal CT scan image in consideration of the CTDI according to the volume of the patient it should be able to reduce the radiation exposure of the patients.

• Korean Journal of Medicinal Crop Science
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• 제15권4호
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• pp.233-240
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• 2007

Extractions of Artemisia princeps Pampanini were obtained by using water and ethanol at different temperatures ($60^{\circ}C,\;100^{\circ}C$) with or without ultrasonification process (40 kHz). Yield of ultrasonificated extracts were about 20% higher than that of control group. Cytotoxicity of all conditions through adding 1.0 mg/$m{\ell}$ was below 37%, and treated with ultrasonification group was lower than the other group, about $5{\sim}8%$. $100^{\circ}C$ water extract with ultrasonification was higher anticancer activities as maximum 73% and higher selectivities at concentrations over 0.8 mg/$m{\ell}$. The extracts treated with ultrasonification were higher anticancer activities than the control. Densitometric analysis of bcl-2 revealed that extracts of high anticancer activity had low density. This results suggest that expression of bcl-2 protein by adding of Artemisia princeps Pampanini extracts relative to taking cancer. To conclude, optimum condition for efficient extraction of Artemisia princeps Pampanini is using water with ultrasonification at over $60^{\circ}C$ below $100^{\circ}C$.

• Journal of environmental and Sanitary engineering
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• 제20권1호
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• pp.76-83
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• 2005

The process of reusing the treated water generated during this process and that of recovery of molybdenum from the excessive water were studied. The results were as follows. Molybdenum recollection 1. Reusing processing water generated after dissolving process on FL/20 type, the following were the remaining Mo.'s weights after the 1st, 2nd, 3rd, 4th, 5th, & 6th dissolutions respectively. 1) The result of measuring the quantity of Mo. in processing water(the 1st solving water) generated after the 1st dissolving Mo. process was $369g/\ell$ 2) The result of measuring the quantity of Mo. in processing water(the 2nd solving water) generated after the 1st dissolving Mo. process reusing the 1st solving water was $627.3g/\ell$ 3) The result of measuring the quantity of Mo. in processing water(the 3rd solving water) generated after the dissolving Mo. process reusing the 2nd solving water was $808.11g/\ell$ 4) The result of measuring the quantity of Mo. in processing water(the 4th solving water) generated after the dissolving Mo. process reusing the 3rd solving water was $934.68g/\ell$ 5) The result of measuring the quantity of Mo. in processing water(the 5th solving water) generated after the dissolving Mo. process reusing the 4th solving water was $1023.27g/\ell$ 6) The result of measuring the quantity of Mo. in processing water(the 6th solving water) generated after the dissolving Mo. process reusing the 5th solving water was $1085.29g/\ell$ 2. The followings were the results of recollectings Mo. in processing water respectively generated after dissolving Mo. to produce complete goods df FL/20 type filament. 1) the percentage of recollecting Mo. in the 1st solving water was $93.0\%$ 2) the percentage of recollecting Mo. in the 2nd solving water was $94.5\%$ 3) the percentage of recollecting Mo. in the 3rd solving water was $95.5\%$ 4) the percentage of recollecting Mo. in the 4th solving water was $96.0\%$ 5) the percentage of recollecting Mo. in the 5th solving water was $96.2\%$ 6) the percentage of recollecting Mo. in the 6th solving water was $96.4\%$ 3. The followings were the results of analyzing, with ICP, holding quantities of Mo. in the 6 processing waters to produce FL/20 type filament after passing a 3 staged solid-liquid separator through, dehydrating and drying for more than 3 hours in a dryer to recollect solving Mo. in them 1) the Mo. holding percentage in the 1st solving water was $76.6\%$ 2) the Mo. holding percentage in the 2nd solving water was $76.6\%$ 3) the Mo. holding percentage in the 3rd solving water was $76.6\%$ 4) the Mo. holding percentage in the 4th solving water was $76.6\%$ 5) the Mo. holding percentage in the 5th solving water was $76.6\%$ 6) the Mo. holding percentage in the 6th solving water was $76.6\%$ It was noted that with the number of times the recollecting Mo. percentage become higher, and in spite of much recollecting, without any large effect on the goods the solving water could be reused as the processing water. Because the collected Mo. holding percentages were more than $76\%$, it is considered they are very good one than Chinese Mo. ores with $50\%$ degrees of purity, worthy of recollecting Mo.

• Korean Journal of Cognitive Science
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• 제28권4호
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• pp.315-328
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• 2017

Even though assessment using information and communication technology will most likely lead the future of educational assessment, there is little domestic research on this topic. Computerized assessment will not only cut costs but also measure students' performance in ways not possible before. In this context, this study introduces a tool which can overcome the problems of multiple choice tests, which are most widely used type of assessment in current Korean educational setting. Multiple-choice tests, in which options are presented with the questions, are efficient in that grading can be automated; however, they allow for students who don't know the answer, to find the correct answer from the options. Park(2005) has developed a modified multiple-choice testing system (CMMT) using the interactivity of computers, that presents questions first, and options later for a short time when the student requests for them. The present study was conducted to find out if penalizing wrong answers could lower the possibility of students choosing an answer among the options when they don't know the correct answer. 116 students were tested with the directions that they will be penalized for wrong answers, but not for no response. There were 4 experimental conditions: 2 conditions of high or low percentage of penalizing, each in traditional multiple-choice or CMMT format. The results were analyzed using a two-way ANOVA for the number of no response, the test score and self-report score. Analysis showed that the number of no response was significantly higher for the CMMT format and that test scores were significantly lower when the penalizing percentage was high. The possibility of applying CMMT format tests while penalizing wrong answers in actual testing settings was addressed. In addition, the need for further research in the cognitive sciences to develop computerized assessment tools, was discussed.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• 제10권1호
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• pp.27-36
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• 2012

Metal chloride waste is generated as a main waste streams in a series of electrolytic processes of a pyrochemical process. Different from carbonate or nitrate salt, metal chloride is not decomposed into oxide and chlorine but it is just vaporized. Also, it has low compatibility with conventional silicate glasses. Our research group adapted the dechlorination approach for the immobilization of waste salt. In this study, the composition of SAP ($SiO_2-Al_2O_3-P_2O_5$) was adjusted to enhance the reactivity and to simplify the solidification process as a subsequent research. The addition of $Fe_2O_3$ into the basic SAP decreased the SAP/Salt ratio in weight from 3 for SAP 1071 to 2.25 for M-SAP( Fe=0.1). The experimental results indicated that the addition of $Fe_2O_3$ increased the reactivity of M-SAP with LiCl-KCl but the reactivity gradually decreased above Fe=0.1. Also, introducing $B_2O_3$ into M-SAP requires no glass binder for the consolidation of reaction products. U-SAP ($SiO_2-Al_2O_3-Fe_2O_3-P_2O_5-B_2O_3$) could effectively dechlorinate the LiCl-KCl waste and its reaction product could be consolidated as a monolithic form without a glass binder. The leaching test result indicated that U-SAP 1071 was more durable than other SAPs wasteform. By using U-SAP, 1 g of waste salt could generated 3~4 g of wasteform for final disposal. The final volume would be about 3~4 times lower than the glass-bonded sodalite. From these results, it could be concluded that the dechlorination approach using U-SAP would be one of prospective methods to manage the volatile waste salt.

• KSBB Journal
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• 제25권2호
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• pp.155-166
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• 2010

Studies on the production of cell-wall bound innerpolysaccharides (IPS) (soluble ${\beta}$-D-glucan) have been performed by use of suspended myelial cultures of Inonotus obliquus. This product has promising potentials as an effective antidiabetic as well as an immunostimulating agents. As a first step to enhanced production of IPS, Intensive strain improvement programs were carried out by obtaining a large amounts of protoplasts for the isolation of single cell colonies. Rapid and large screening of high-yielding producers was possible because about fivefold higher amount of protoplasts ($2.3{\times}10^6$ protoplasts/mL) could be recovered with relatively high regeneration rates of $10^{-2}{\sim}10^{-3}$ by applying a modified filtration method, as compared to the previously used trapping method. A basic protocol necessary for UV-mutation of the protoplasts was also developed, resulting in several overproducing variants with good fermentation properties. Since the amount of IPS extracted from the mycelial cell walls of I. obliquus turned out to be almost constant per g DCW, increase in cell mass was considered the most important factor for the enhancement in IPS production. Therefore, attempts were made to screen mutant cells showing rapid mycelial growth rate in the final suspended cultures. Notably, the mutant strains showing an active cellgrowth in the preceding solid growth cultures were observed to produce higher amount of IPS in the suspended fermentations as well. A striking mutant, OBLQ756-15-5 strain, obtained from the survivors of a harsh UV-treated condition (97% death rate) was found to stably produce as high cell mass as 22 g DCW/L in the final fermentations. Currently, this strain is being tested for development of a scaled-up fermentation process for mass production of IPS.