• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• 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 Food Hygiene and Safety
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• v.28 no.3
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• pp.247-251
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• 2013

The objective of present study was to develop a simultaneous determination method of 5 medical compounds, including beclomethasone, dexamethasone, prednisolone, ketoprofen, phenylbutazone in foods, using LC-MS/MS. To optimize MS analytical condition of 5 compounds, each parameter was established by MRM mode. The chromatographic separation was achieved on a C18 column successfully, with a mobile phase made up of A (0.1% formic acid) and B (0.1% formic acid in acetonitrile), at a flow rate of 0.3 mL/min for 17 min with a gradient elution. LOD and LOQ of 5 compounds were in the range of 0.40~4.60 ng/mL and 0.81~11.46 ng/mL, respectively. As a result of analyzing the three concentrations of the standard mixture added to blank samples, the results showed that the mean recovery rate of 5 compounds was in the range of 81.52~103.83%, and RSD (%) of Intra- and Inter-day assay were 0.52-10.45. Since relatively fine selectivity, accuracy and reproducibility were shown in this qualified experimental method, it could be utilized efficiently to investigating those 5 compounds to see if it is added to food products illegally.

• Journal of Sericultural and Entomological Science
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• v.47 no.2
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• pp.51-55
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• 2005

Resveratrol is naturally occurring phytoalexin compounds produced by grape berries, peanuts, and their products in response to stress such as fungal infection, heavy metal ions or UV irradiation. The objective of this study was to develope a reliable high performance liquid chromatographic (HPLC) method for the quantitative determination of trans-resveratrol in mulberry fruit. Samples were extracted in 80% MeOH and filtered with $0.45{\mu}m$ syringe filter. The transresveratrol was separated Waters $C_{18}$ column, using a mobile phase containing 0.025% trifluroacetic acid in 5% acetonitril and 0.035% trifluroacetic acid in 50% acetonitril, detected by photodiode array detector (PDA) at 254 nm and the flow rate was 1ml/min. Under this analytical condition, the mean content of mulberry fruits (fifty varieties) was $777.3{\pm}585.9ppm$. Among the tested samples, 'Mansaengbaekpinosang (II)' was the highest level in 3450.6 ppm. However four accessions including 'Gukbu', 'Sabangso (I)', 'Simseol' and yield mulberry fruit were not able to detected. Eight suitable varieties selected for the production of fruit were 'Jeolgokchosaeng (Chungbuk)' 777.8 ppm, 'Dangsang 7' 771.1 ppm, 'Jangsosang' 133.9 ppm, 'Susungppong' 31.1 ppm, 'Suwonnosang' 639.7 ppm, 'Palcheongsipyung' 1475.9 ppm, 'Kangsun' 864.0 ppm, and 'Jukcheonchosaeng' 1458.5 ppm. 'Daesungppong' which was the first authorized variety for the production of mulberry fruit was 1236.7 ppm. In conclusion, these results suggest that mulberry including fruit and leaf may a good new resource for resveratrol production.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.8
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• pp.1165-1170
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• 2010

A method for the determination of four pesticide compounds, urea (isoproturon), bis-carbamate (phenmedipham), thiocarbamate (pyridate) and vinyllidenediamine (nitenpyram) were examined and analyzed by HPLC with C-18 column ($250\;mm{\times}4.6\;mm$, $5\;{\mu}m$ diameter particle size). Mobile phase consisted of deionized water, acetonitrile and 50 mM $KH_2PO_4$ (pH 2.5). Isoproturon and phenmedipham analytical condition was isocratic elution of the column with 50% solvent A (acetonitrile) and 50% solvent B (deionized water); pyridate was 85% solvent A (acetonitrile) and 15% solvent B (deionized water) at a flow rate of 1 mL/min; and nitenpyram analytical condition was 90% solvent A (50 mM $KH_2PO_4$, pH 2.5) and 10% solvent B (acetonitrile) at a flow rate of 1 mL/min. In results, retention times were 6.12, 8.63, 9.40 and 12.76 min for isoproturon, phenmedipham, pyridate and nitenpyram, respectively. All injection volumes were $10\;{\mu}L$ and the limit of quantitation was 0.05 mg/kg for four pesticide compounds, respectively. Recovery rate test was performed with three farm products, rice, apple and soybean. Four pesticide compounds were spiked at concentrations of 0.05, 0.1 and 0.5 mg/kg. The recovery rates were ranged from 70.18% to 118.08% and the standard deviations of all experiments were within 10%.

• Journal of Wetlands Research
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• v.22 no.3
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• pp.194-199
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• 2020

Unlike in the past, the world is facing water shortages due to climate change and difficulties in simultaneously managing the risks of flooding. The Four Major Rivers project was carried out with the aim of realizing a powerful nation of water by managing water resources and fostering the water industry, and the construction period was relatively short compared to the unprecedented scale. Therefore, the prediction and analysis of how the river environment changes after the Four Major Rivers Project is insufficient. Currently, part of the construction section of the Four Major Rivers Project is caused by repeated erosion and sedimentation due to the effects of sandification caused by large dredging and flood-time reservoirs, and the head erosion of the tributaries occurs. In order to solve these problems, the riverbed maintenance work was installed, but it resulted in erosion of both sides of the river and the development of new approaches and techniques to keep the river bed stable, such as erosion and excessive sedimentation, is required. The water agent plays a role of securing a certain depth of water for the main stream by concentrating the flow so much in the center and preventing levee erosion by controlling the flow direction and flow velocity. In addition, Groyne products provide various ecological environments by forming a natural form of riverbeds by inducing local erosion and deposition in addition to the protection functions of the river bank and embankment. Therefore, after reviewing the method of determining the shape of the Groyne structure currently in use by utilizing the mobile limit flow rate and marginal reflux force, a new Critical Movement Velocity(${\bar{U}}_d$) and a new resistance coefficient formula considering the mathematical factors applicable to the actual domestic stream were developed and the measures applicable to Groyne installation were proposed.

• Journal of Distribution Science
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• v.8 no.4
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• pp.17-23
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• 2010

The Banking industry is expanding rapidly. To keep the competitive advantages, participating companies concentrate their resource to provide the distinguishable services by increasing the service quality. This study is to find that how three kinds of service quality(process, output, and service environment) affect on the customer satisfaction. In this paper, WPCSI (Weighted Potential Customer Satisfaction Index) was developed using Kano model and PCSI. Kano's model of service quality classification was used to improve customer satisfaction, customer satisfaction index was calculated. Customer satisfaction index was calculated using the existing potential for improving customer satisfaction index (PCSI Index) to complement the limitations of the weighted potential improve customer satisfaction index (WPCSI) were used. Analysis using PCSI improve the quality of service levels may be useful in assessing. However, this figure is a marginal degree of importance on customers and quality characteristics have been overlooked but has its problems. A service provided to customers with some important differences depending on the interpretation of the scope for improvement is to be classified. In other words, the level of customer satisfaction and the satisfaction of the current difference between the comparison factor for the company to provide information about the priority of the improvement was not significant. Companies are also considered important that the customer does not consider the uniform quality of service provided can be fallible. In this study, the weighted potential to improve it improve customer satisfaction index (WPCSI) proposed a new customer satisfaction index. This is for customers to recognize the importance of quality characteristics by weighting factors, to identify practical and improved priority to provide more useful information than has been. Weighted potentially improve customer satisfaction index (WPCSI) presented in this study by the customers aware of the importance of considering the quality factor is an exponent. The results, 'Employees' working ability', 'provided the desired service level', 'staff to handle this task quickly enough' to the customer of the factors had significant effects on satisfaction are met. On the other hand 'aggressiveness on the product description of employees', 'service environment as a whole, beautiful enough to' meet and shows no significant difference between satisfaction. But 'aggressiveness on the product description of employees' and reverse (逆) were attributable to the quality. Small dogs and overly aggressive products that encourage the customer dissatisfaction that can result in widening should be careful because the quality factor can be said. As a result, WPCSI is more effect to find critical factors which can affect customer satisfaction than PCSI. After that, we discuss effects and advantages of customer satisfaction using WPCSI. This study, along with these positive aspects, the limitations are implied. First, this study directly to the bank so that I could visit any other way for customers, utilizing the Internet or mobile to take advantage of the respondents were excluded from the analysis. Second, in survey questionnaires can help improve understanding of the measures will be taken. In addition to the survey targeted mainly focused on Seoul, according to a sample, so sampling can cause problems is the viscosity revealed intends.

• Journal of Food Hygiene and Safety
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• v.32 no.5
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• pp.411-417
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• 2017

This study was conducted to establish the analysis method for the contents of choline in infant formulas and follow-up formulas by ion chromatograph (IC). To optimize the method, we compared several conditions for extraction, purification and instrumental measurement using spiked samples and certified reference material (CRM; NIST SRM 1849a) as test materials. IC method for choline was established using Ion Pac CG column and 18 mM $H_2SO_4$ mobile phase. The parameters of validation were specificity, linearity, LOD, LOQ, recovery, accuracy, precision and repeatability. The specificity was confirmed by the retention time and the linearity, $R_2$ was over 0.999 in range of 0.5~10 mg/L. The detection limit and quantification limit were 0.14, 0.43 mg/L. The accuracy and precision of this method using CRM were 95%, 2.1% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested products were acceptable contents of choline compared with component specification for nutrition labeling. The standard operating procedures were prepared for choline to provide experimental information and to strengthen the management of nutrient in infant formula and follow-up formula.

• Korean Journal of Food Science and Technology
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• v.44 no.3
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• pp.263-268
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• 2012

Novobiocin is a coumarin-containing antibiotic, and has a longer half-life in various animals than other veterinary medicines. A simple and rapid high-performance liquid chromatography assay for the determination of residual novobiocin levels in chicken, beef and milk has been developed and validated. The separation condition for HPLC/UVD was optimized by a MG II $C_{18}$ (4.6 mm $ID{\times}250$ mm, 5 ${\mu}m$) column with 0.1% formic acid in $H_2O$/0.1% formic acid in Acetonitrile (40/60, v/v) as the mobile phase at a flow rate of 1.0 mL/min and the detection wavelength was set at 340 nm. Residues were extracted from tissue by blending with methanol. After liquid-liquid partitioning, lipid materials were removed with n-hexane and purification as Silica (1 g, 6 mL) cartridge with 10 mL acetone/dichloromethane (10/90, v/v). Limit of quantification and linearity performed by the analytical method were 0.02 mg/kg and 0.999 ($r^2$), and the recovery range was $88.8{\pm}5.6-100.3{\pm}4.4$, $88.8{\pm}7.2-97.0{\pm}3.2$ and $88.1{\pm}4.3-92.8{\pm}3.6%$. It is expected that this analytical method with regards to novobiocin in chicken, beef and milk could be applied as an official method to administer food safety on veterinary medicines.

• Management & Information Systems Review
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• v.38 no.4
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• pp.1-24
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• 2019

This study confirms the responses of consumers when the composition of emoticon bundles can be selected by individuals in MIM service. This aims to verify that customized bundling is a valid marketing strategy in the MIM emoticon market. Currently, the emoticon bundling used in Korean MIM services is in the form of pure bundling. As a result, Consumers must purchase an entire bundle even though he/she doesn't need to use all the emoticons contained in it. Some researches(e.g. Hitt & Chen, 2005; Wu & Anandalingam, 2002) show that when consumers value only part of the products or services included in pure bundling, customized bundling is much more profitable. In their works, customized bundling is appropriate when marginal costs are near zero. Information goods, such as emoticons, meet the condition. On the other hand, customized bundling increase the choosable options, so it can pose a problem of complexity (Blecker et al., 2004). And consumers may experience information overload(Huffman & Kahn, 1998). Thus, judgement on the necessity to introduce customized bundling needs to be made through empirical analyses in the light of characteristics of the product and the reaction of consumers. Results show that when customized bundling was introduced, consumers' purchase intention and willingness to pay significantly increased. Purchase intention for customized bundles has increased by 0.44 based on the five point Likert scale than the purchase intention for existing pure bundles. The increase in purchase intention for customized bundles was statistically independent of the existing purchasing experience. In addition, the willingness to pay was increased by about 2.8% compared to the price of the existing emoticon bundles in the whole group. The group with experience in purchasing pure bundles were willing to pay 5.9% more than pure bundles. The other group without experience in purchasing pure bundles were willing to buy if they were about 5% cheaper than the existing price. Overall, introducing customized bundling into emoticon bundles can lead to positive consumers responses and be a viable marketing strategy.