• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.3
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• pp.103-115
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• 2000

The formation of brown-colored precipitates is one of the serious problems frequently encountered in the development and supply of groundwater in Korea, because by it the water exceeds the drinking water standard in terms of color. taste. turbidity and dissolved iron concentration and of often results in scaling problem within the water supplying system. In groundwaters from the Pajoo area, brown precipitates are typically formed in a few hours after pumping-out. In this paper we examine the process of the brown precipitates' formation using the equilibrium thermodynamic and kinetic approaches, in order to understand the origin and geochemical pathway of the generation of turbidity in groundwater. The results of this study are used to suggest not only the proper pumping technique to minimize the formation of precipitates but also the optimal design of water treatment methods to improve the water quality. The bed-rock groundwater in the Pajoo area belongs to the Ca-$HCO_3$type that was evolved through water/rock (gneiss) interaction. Based on SEM-EDS and XRD analyses, the precipitates are identified as an amorphous, Fe-bearing oxides or hydroxides. By the use of multi-step filtration with pore sizes of 6, 4, 1, 0.45 and 0.2 $\mu\textrm{m}$, the precipitates mostly fall in the colloidal size (1 to 0.45 $\mu\textrm{m}$) but are concentrated (about 81%) in the range of 1 to 6 $\mu\textrm{m}$in teams of mass (weight) distribution. Large amounts of dissolved iron were possibly originated from dissolution of clinochlore in cataclasite which contains high amounts of Fe (up to 3 wt.%). The calculation of saturation index (using a computer code PHREEQC), as well as the examination of pH-Eh stability relations, also indicate that the final precipitates are Fe-oxy-hydroxide that is formed by the change of water chemistry (mainly, oxidation) due to the exposure to oxygen during the pumping-out of Fe(II)-bearing, reduced groundwater. After pumping-out, the groundwater shows the progressive decreases of pH, DO and alkalinity with elapsed time. However, turbidity increases and then decreases with time. The decrease of dissolved Fe concentration as a function of elapsed time after pumping-out is expressed as a regression equation Fe(II)=10.l exp(-0.0009t). The oxidation reaction due to the influx of free oxygen during the pumping and storage of groundwater results in the formation of brown precipitates, which is dependent on time, $Po_2$and pH. In order to obtain drinkable water quality, therefore, the precipitates should be removed by filtering after the stepwise storage and aeration in tanks with sufficient volume for sufficient time. Particle size distribution data also suggest that step-wise filtration would be cost-effective. To minimize the scaling within wells, the continued (if possible) pumping within the optimum pumping rate is recommended because this technique will be most effective for minimizing the mixing between deep Fe(II)-rich water and shallow $O_2$-rich water. The simultaneous pumping of shallow $O_2$-rich water in different wells is also recommended.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• 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.177-182
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• 2014

Purpose : The goal of this study was to compare and analysis the dose distribution and treatment time between Tomotherapy planning with fixed jaw(FJ) and dynamic jaw(DJ). Materials and Methods : Seven patients were selected in the study including five common clinical cases(brain, head and neck(HN), lung, prostate, spine). 1) Helical Tomotherapy plans with FJ and DJ were generated with the same planning parameters such as Modulation factor, Pitch and Field width. 2) Tomo_edge plans with a larger field width were generated to compare to conventional HT delivery with fixed jaw. Dosimetric evaluation indices for target coverage are Dmin, Conformity index(CI) and for whole body including target are $V_{10%}$, $V_{25%}$, $V_{50%}$, $V_{75%}$ using Dose-volume histogram(DVH). Also, Treatment time and Cumulative MU were used for clinical review on Tomo_edge. Results : In case of using the same field width of Tomotherapy planning with FJ and DJ, the averaged variations were $V_{10%}$: -11.91%, $V_{25%}$: -7.6%, $V_{50%}$ :-4.75%, $V_{75%}$: -1.04%. Tomo_edge with a larger field width provides the averaged variations for target coverage: Dmin: -0.72%, CI: -1.25% and also shows the tendency of a sharp $V_{x%}$ decline in low dose area. The clinical improvements in the larger field width with DJ were observed in the treatment time, ranging from -51.21% to -15.11, and the Cumulative MU decrease, ranging from -57.74% to -15.31%. Conclusion : Target coverage achieved by FJ and DJ with the same field width has little differences. But integral doses on whole body efficiently decreased. Compared to the conventional HT delivery, Tomo_edge with a larger field width presents a little worse target coverage. However, it provides faster treatment delivery and improved cranial-caudal target dose conformity. Therefore, Tomo_edge mode is efficient in improving the treatment time and integral dose while maintaining comparable plan quality in clinic.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.207-216
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• 2014

Purpose : We present a method to reduce this gap and complete the treatment plan, to be made by the re-optimization is performed in the same conditions as the initial treatment plan different from Monaco treatment planning system. Materials and Methods : The optimization is carried in two steps when performing the inverse calculation for volumetric modulated radiation therapy or intensity modulated radiation therapy in Monaco treatment planning system. This study was the first plan with a complete optimization in two steps by performing all of the treatment plan, without changing the optimized condition from Step 1 to Step 2, a typical sequential optimization performed. At this time, the experiment was carried out with a pencil beam and Monte Carlo algorithm is applied In step 2. We compared initial plan and re-optimized plan with the same optimized conditions. And then evaluated the planning dose by measurement. When performing a re-optimization for the initial treatment plan, the second plan applied the step optimization. Results : When the common optimization again carried out in the same conditions in the initial treatment plan was completed, the result is not the same. From a comparison of the treatment planning system, similar to the dose-volume the histogram showed a similar trend, but exhibit different values that do not satisfy the conditions best optimized dose, dose homogeneity and dose limits. Also showed more than 20% different in comparison dosimetry. If different dose algorithms, this measure is not the same out. Conclusion : The process of performing a number of trial and error, and you get to the ultimate goal of treatment planning optimization process. If carried out to optimize the completion of the initial trust only the treatment plan, we could be made of another treatment plan. The similar treatment plan could not satisfy to optimization results. When you perform re-optimization process, you will need to apply the step optimized conditions, making sure the dose distribution through the optimization process.

• Korean Journal of Environment and Ecology
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• v.34 no.3
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• pp.249-258
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• 2020

This study aims to provide reference material for effective forest management techniques at the catchment scale, based on the field investigation of large woody debris (LWD) in 11 streams within a third-order forest catchment in Gangwon Province, Korea. To achieve this aim, we analyzed the morphological features of LWD pieces, and the storage and distribution status of LWD by stream order throughout the entire investigation. As a result, a total of 1,207 individual pieces of LWD were categorized into three types as follows: (ⅰ) 1,142 pieces (95%) as only trunk and 65 pieces (5%) as a trunk with root wad, (ⅱ) 1,015 pieces (84%) as non-thinned and 192 pieces (16%) as the thinned, and (ⅲ) 1,050 pieces (87%) as conifer and 157 pieces (13%) as broadleaf. Additionally, in-stream LWD loads (㎥/ha) decreased with increasing stream order, yielding 105.4, 71.3, and 35.6 for first-, second-, and third-order streams, respectively. On the other hand, the ratio of LWD jams to the total LWD volume increased with increasing stream order, yielding 11%, 43%, and 49% for first-, second-, and third-order streams, respectively. Finally, a comparison of the in-stream LWD load with previous studies in several countries around the world indicated that in-stream LWD load was positively correlated with forest stand age even though the climate, topography, forest soil type, forest composition, stand growth rate, disturbance regime, and forest management practices were different. These results could contribute to understanding the significance of LWD as a by-product of forest ecosystems and an indicator of riparian forest disturbance. Based on this, we conclude that advanced forest management techniques, including treatment of thinning slash and stand density control of riparian forest by site location (hillslope and riparian zone, or stream order), should be established in the future, taking the forest ecosystem and the aquatic environment from headwater streams to low land rivers into consideration.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.671-677
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• 2016

The impregnated alkali metal (Na, K), and the alkali earth metal (Ca, Mg) activated carbons were produced from the bamboo activated carbon by soaking method of alkali metals and alkali earth metals solution. The carbonization and activation of raw material was conducted at $900^{\circ}C$. The specific surface area and the pore size distribution of the prepared activated carbons were measured. Also, NO and activated carbon reaction were conducted in a thermogravimetric analyzer in order to use for de-NOx agents of the used activated carbon. Carbon-NO reactions were carried out in the nonisothermal condition (the reaction temperature $20{\sim}850^{\circ}C$, NO 1 kPa) and the isothermal condition (the reaction temperature 600, 650, 700, 750, 800, $850^{\circ}C$, NO 0.1~1.8 kPa). As results, the specific volume and the surface area of the impregnated alkali bamboo activated carbons were decreased with increasing amounts of the alkali. In the NO reaction, the reaction rate of the impregnated alkali bamboo activated carbons was promoted to compare with that of the bamboo activated carbon [BA] in the order of BA(Ca)> BA(Na)> BA(K)> BA(Mg) > BA. Measured the reaction orders of NO concentration and the activation energy were 0.76[BA], 0.63[BA(Na)], 0.77[BA(K)], 0.42[BA(Ca)], 0.30 [BA(Mg)], and 82.87 kJ/mol[BA], 37.85 kJ/mol[BA(Na)], 69.98 kJ/mol[BA(K)], 33.43 kJ/mol[BA(Ca)], 88.90 kJ/mol [BA(Mg)], respectively.

• Korean Journal of Veterinary Research
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• v.5 no.1
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• pp.61-96
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• 1965

Observations were made on the blood picture of total 196 heads of healthy Korean cattles, including 98 males and 98 females in the purpose of determination of hematological values and its sex difference, and seasonal variations during one year period from December 1963 to November 1964. The blood sampling were scheduled by random in four different seasons and the sample size of both sex included in each season were designated to be same size. The ranges, averages or mean values of the erythrocytes, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, total white blood cell count and differential count were determined in this studies and their respective standard deviation, standard error of means, sex defferences and seasonal variations were as follows; 1. The erythrocyte count of male showed a range of $5.0{\times}10^6/c.mm$ to $8.75{\times}10^6/c.mm$ with a mean of $6.5{\pm}0.096{\times}10^6/c.mm$. Female showed a range of $5.0{\times}10^6/c.mm$ to $8.30{\times}10^6/c.mm$, with a mean of 6. $131{\pm}0.078{\times}10^6/c.mm$. There was a highly significant sex difference and seasomal variation was not found to be significant. 2. The hemoglobin value of male showed a range of 9.0g/100cc. to 14.5g/100cc. with a mean of $11.074{\pm}0.143g/100cc$. Female showed a range of 9.0g/100cc to 13.0g/100cc. with a mean of $10.745{\pm}0.034g/100cc$. There was a highly significant sex difference and seasonal variation was not found to be significant. 3. The hematocrit value of male showed a range of 28% to 45% and with a mean of $34.867{\pm}0.468%$. Female showed a range of 28% to 42% with a mean of $32.888{\pm}0.322%$. There was a highly significant sex difference and seasonal variation was not found to be significant. 4. The mean corpuscular hemoglobin of male showed a range of 14.4rr. to 19.6rr. with a mean of $17.1{\pm}0.112rr$. Female showed a range of 14.7rr. to 19.5rr. with a mean of $17.6{\pm}0.113rr$. 5. The mean corpusular volume of male showed a range of $42.5{\mu}^3$ to $62.2{\mu}^3$ with a mean of $53.9{\pm}0.419{\mu}^3$, Female showed a range of $44.2{\mu}^3$ to $60.0{\mu}^3$ with a mean of $53.8{\pm}0.375{\mu}^3$. 6. The mean corpuscular hemoglobin concentration of male showed a range of 28.1 % to 34.9% with a mean of $31.4{\pm}0.161%$. Female showed a range of 28.0% to 34.9% with a mean of $30.9{\pm}0.169%$. 7. The total leucocyte count of male showed a range of 4,000/c.mm to 13,100/c.mm. with a mean of $9,338{\pm}218.23/c.mm$. Female showed a range of 4,000/c.mm. to 14,000/c.mm. with a mean of $9,338{\pm}235.90/c.mm$. Six difference was not found to be significant and there was a highly significant seasonal variation. 8. The differential count of male, the means of neutrophil, stab, segmented cell, Iymphocyte, monocyte, eosinophil and basophil were $31.173{\pm}0.570%$, 0.3%. $30.867{\pm}0.564%$, $55.112{\pm}0.603%$, $3.745{\pm}0.082%$, $9.867{\pm}0.422%$ and 0.14% rspectively. Female showed means of $31.010{\pm}0.572%$, 0.2%, $30.806{\pm}0.569%$, $53.929{\pm}0.634%$, $4.082{\pm}0.109%$, $10.908{\pm}0.503%$ and 0.12% respectively. There were significant sex differences in monocyte and highly significant sex difference in eosinophil, and seasonal variation were found to be highly significant in neutrophil, monocyte and eosinophil. 9. Hematological comparison made between cattles infested with so called "small type piroplasma" and non-infested group. The result of investigation showed no significant difference upon the red blood cell, hemoglobin and hematocrit values between lighty infested group and non-infested group. 10. Age distribution of test group in this study ranged from 2 years to 6 years in both sex and their average age were $4.45{\pm}0.114$(male) and $4.50{\pm}0.116$(female). There found to be no significant sex difference and seasonal variations in the age of test group.

• Korean journal of food and cookery science
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• v.16 no.1
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• pp.47-56
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• 2000

The purpose of this study was to develop a drying method of Gangjung. a traditional Korean snack, thus to reduce the drying time and to improve the quality of Gangjung. Two drying methods, hot air drying and far infrared ray drying were used by changing conditions such as air velocity(0.4, 1.2, 1.6 m/s), temperature(40, 50, 60$\^{C}$), and aging. Optimal moisture content of dried Gangjung pellet was 17% which was proper for frying. Cracks appeared on the surface of Gangjung pellet at lower levels of moisture content. Far infrared ray drying saved drying time about 20%. Both hot air drying and far infrared ray drying at 0.4 m/s of air velocity tended to show better quality of Gangjung than those dried at higher air velocities. The expansion volume and texture of Gangjung drying at 40$\^{C}$ was better than other temperature conditions, regardless of drying methods. Quality of Gangjung, dried at single stage without aging, was superior to those dried at double stage including aging process. Moreover, single stage drying save the drying time at least 24 hr. Gangjung dried at high temperature became hard and less brittle in sensory evaluation. In image analysis, air cell distribution in inner structure of Gangjung became uniform and fine as drying temperature decreased to 40$\^{C}$. Overall, Gangjung made of Gangjung pellet by the use of far infrared ray drying at 40$\^{C}$ without aging, showed the best quality in terms of physical and sensory properties.