• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.207-222
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• 2008

This research suggests the method of how to build agricultural and stockbreeding products recommender systems based on RFID technology for monitoring crop and livestock production, tracing production history as an application strategy. In the past the studies on enterprise applications have been barely implemented owing to the rack of business model and limitation of technical development. Currently however there have been enormous technological progress of RFID and agricultural and stockbreeding products retailing sites are increased. Therefore this paper suggests PDCF-ASP(Profile Decay based Collaborative Fltering for Agricultural and Stockbreeding Products) which is designed to reduce customers… search efforts in finding safety and fresh products on the internet shopping mall. For this, product decay function is defined to make sure whether the products are safety or not and to adopt a change in customer preferences. And for the implementation of PDCF-ASP, the system structure including functional agents is schematized.

• Information Systems Review
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• v.8 no.1
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• pp.181-201
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• 2006

As the Internet spreads, many people have interests in e-CRM and product recommender systems, one of e-CRM applications. Among various approaches for recommendation, collaborative filtering and content-based approaches have been investigated and applied widely. Despite their popularity, traditional recommendation approaches have some limitations. They require at least one purchase transaction per user. In addition, they don't utilize much information such as demographic and specific personal profile information. This study suggests new hybrid recommendation model using two data mining techniques, association rule and classification, as well as intelligent agent to overcome these limitations. To validate the usefulness of the model, it was applied to the real case and the prototype web site was developed. We assessed the usefulness of the suggested recommendation model through online survey. The result of the survey showed that the information of the recommendation was generally useful to the survey participants.

• Journal of Biosystems Engineering
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• v.31 no.6 s.119
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• pp.514-523
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• 2006

Production of green pepper has been increased due to customer's preference and a projected ten-year boom in the industry in Korea. This study was carried out to develop an automatic grading and sorting system for green pepper using machine vision. The system consisted of a feeding mechanism, segregation section, an image inspection chamber, image processing section, system control section, grading section, and discharging section. Green peppers were separated and transported using a bowl feeder with a vibrator and a belt conveyor, respectively. Images were taken using color CCD cameras and a color frame grabber. An on-line grading algorithm was developed using Visual C/C++. The green peppers could be graded into four classes by activating air nozzles located at the discharging section. Length and curvature of each green pepper were measured while removing a stem of it. The first derivative of thickness profile was used to remove a stem area of segmented image of the pepper. While pepper is moving at 0.45 m/s, the accuracy of grading sorting for large, medium and small pepper are 86.0%, 81.3% and 90.6% respectively. Sorting performance was 121 kg/hour, and about five times better than manual sorting. The developed system was also economically feasible to grade and sort green peppers showing the cost about 40% lower than that of manual operations.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.571-578
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• 2001

Production of green pepper has increased for ten years in Korea, as customer's preference of a pepper tuned to fiesta one. This study was conducted to develop an on-line fading algorithm of green pepper using machine vision and aimed to develop the automatic on-line grading and sorting system. The machine vision system was composed of a professive scan R7B CCD camera, a frame grabber and sets of 3-wave fluorescent lamps. The length and curvature, which were main quality factors of a green pepper were measured while removing the stem region. The first derivative of the thickness profile was used to remove the stem area of the segmented image of the pepper. A new boundary was generated after the stem was removed and a baseline of a pepper which was used for the curvature determination was also generated. The developed algorithm showed that the accuracy of the size measurement was 86.6% and the accuracy of the bent was 91.9%. Processing time spent far grading was around 0.17 sec per pepper.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.512-516
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• 2012

Recently the Korean government's green energy growth policy has been taken at the national level due to the sufficient supply of renewable energy. Some specific technique should be taken in consideration for the operation of the grid voltage and power quality management. In this case, there may have some chance of operational problems. Typical problems arise when grid-connected solar power produced by Pacific sunshine. The power flow in the reverse direction can create overvoltage on the distribution line and gives value of malfunction on the system. Line voltage and overvoltage adjustment practice can stop these symptoms occurred. Under these circumstances, this paper presents an interconnection test devices for photovoltaic(PV) systems composed of distribution system simulator, PV system simulator and control and monitoring systems using the LabVIEW S/W, and simulates the customer voltage characteristics considering the 3 parameters on the introduction capacity for PV systems, system configuration and Power factor. This paper also proposes a new calculation algorithm for voltage profile to make comparison between calculation values and test device values. The results show that the simulation results for the normal operation characteristics of PV systems which are very practical and effective.

• Information Systems Review
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• v.4 no.2
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• pp.155-168
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• 2002

This paper examines whether the functions of eCRM solutions affect the site visit and purchase by customers. The functions of eCRM solutions are extracted and classified into three categories of c-marketing, e-sales, and e-service. E-marketing includes campaign/event marketing, e-mail marketing, and questionnaire marketing; e-sales consists of recommendation system and incentive/discount promotion.; e-service is composed of e-mail call center and web call center. From the online survey, 146 responses are collected and analyzed. The analysis shows that the level of experience in campaign/event marketing, e-mail marketing, e-mail call center, and web call center significantly affect the website visit by customers and that the level of experience in all eCRM functions except e-mail marketing significantly affect the purchase by customers. The effects of those functions in eCRM on the website visit are moderate, while the effects of the functions on the purchase are low. The results from this study imply that eCRM needs to strengthen the effect on the purchase with more thorough analysis of the customer profile.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.41-50
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• 2008

This paper proposes the recommendation system which is a new method using RFM method in mobile internet environment. Using a implict method which is not used user's profile for rating, is not used complicated query processing of the request and the response for rating, it is necessary for user to keep the RFM score about users and items based on the whole purchased data in order to recommend the items. As there are some problems which didn't exactly recommend the items with high purchasablity for new customer and new item that do not have the purchase history data. in existing recommendation systems, this proposing system is possible to solve existing problems, and also this system can avoid the duplicated recommendation by the cross comparison with the purchase history data. It can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system with high purchasablity for one to one web marketing through the mobile internet.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.105-114
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• 2020

This study aimed to assess of beam-matching accuracy for an 8 MV beam between the same model linear accelerators(Linac) commissioned over two years. Two models were got the customer acceptance procedure(CAP) criteria. For commissioning data for beam-matched linacs, the percentage depth doses(PDDs), beam profiles, output factors, multi-leaf collimator(MLC) leaf transmission factors, and the dosimetric leaf gap(DLG) were compared. In addition, the accuracy of beam matching was verified at phantom and patient levels. At phantom level, the point doses specified in TG-53 and TG-119 were compared to evaluate the accuracy of beam modelling. At patient level, the dose volume histogram(DVH) parameters and the delivery accuracy are evaluated on volumetric modulated arc therapy(VMAT) plan for 40 patients that included 20 lung and 20 brain cases. Ionization depth curve and dose profiles obtained in CAP showed a good level for beam matching between both Linacs. The variations in commissioning beam data, such as PDDs, beam profiles, output factors, TF, and DLG were all less than 1%. For the treatment plans of brain tumor and lung cancer, the average and maximum differences in evaluated DVH parameters for the planning target volume(PTV) and the organs at risk(OARs) were within 0.30% and 1.30%. Furthermore, all gamma passing rates for both beam-matched Linacs were higher than 98% for the 2%/2 mm criteria and 99% for the 2%/3 mm criteria. The overall variations in the beam data, as well as tests at phantom and patient levels remains all within the tolerance (1% difference) of clinical acceptability between beam-matched Linacs. Thus, we found an excellent dosimetric agreement to 8 MV beam characteristics for the same model Linacs.

• Food Science of Animal Resources
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• v.44 no.1
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• pp.74-86
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• 2024

Eggs that have been hard-boiled are frequently used as ready-to-eat food. Refrigerated and frozen storage of hard-boiled eggs causes issues, such as customer rejection owing to textural changes. The objective of this research is to ascertain how storage temperature affects hard-boiled eggs' alteration in texture over time. Medium-sized brown shell eggs were acquired from a local market, boiled at 100℃ for 15 min, and then stored at room temperature (25℃), refrigeration (4℃), and freezing (-18℃) conditions for 0, 12, 24, and 48 h. Fourier transform infrared spectroscopy (FTIR), texture profile, visual observation using a gemological microscope, free amino acid content, and color were measured. Freezing had a substantial impact on the eggs' hardness, gumminess, chewiness, and cohesiveness (p<0.05). The FTIR spectrums confirmed the textural changes in bonds of amide A (3,271 cm^-1), amide I (1,626.2 cm^-1), amide II (1,539.0 cm^-1), C=O stretch of COO^- (1,397 cm^-1), asymmetric PO₂^- stretch (1,240 cm^-1). Microscopic images confirmed structural changes in eggs stored at -18℃. The free amino acid content was lower in fresh and frozen eggs than in the rest (p<0.05). However, there was no discernible variation in the egg white's color when eggs were kept at 4℃ (p>0.05). Salmonella spp. was found exclusively in eggs kept at room temperature. In conclusion, hard-boiled eggs did not exhibit structural or chemical changes when stored at 4℃ for up to 48 h compared to freezing and room temperature conditions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.77-90
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• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.