• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.112-118
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• 2009

Skin color segmentation techniques have been widely utilized for face/hand detection and tracking in many applications such as a diagnosis system using facial information, human-robot interaction, an image retrieval system. In case of a video image, it is common that the skin color model for a target is updated every frame for the robust target tracking against illumination change. As for a single image, however, most of studies employ a fixed skin color model which may result in low detection rate or high false positive errors. In this paper, we propose a novel method for effective skin color segmentation in a single image, which modifies the conditions for skin color segmentation iteratively by the image feedback of segmented skin color region in a given image.

• East Asian mathematical journal
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• v.30 no.4
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• pp.451-474
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• 2014

The purpose of this study is to analyze error patterns third-year middle school students make on quadratic function graph problems and to examine about the possible correct them by providing supplementary tutoring. To exam the error patterns that occur during problem solving processes, to 82 students, We provided 25 quadratic function graph problems in the preliminary-test. The 5 types of errors was conceptual errors, false intuition errors, incorrect use of conditions in problems, technical errors, and errors from slips or carelessness. Statistical analysis of the preliminary-test and post-test shows that achievement level was higher in the post-test, after supplementary tutoring, and the t-test proves this to be meaningful data. According to the per subject analyses, the achievement level in the interest of symmetry, parallel translation, and general graph, respectively, were all higher in the post-test than the preliminary-test and this is meaningful data as well. However, no meaningful relation could be found between the preliminary-test and the post-test on other subjects such as graph remodeling and relations positions of the parabola. For the correction of errors, try the appropriate feedback and various teaching and learning methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.89-95
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• 2020

Recently, studies are being conducted to recognize a sketch image of a GUI (Graphical User Interface) based on a deep learning and to make it into a code implemented in an application. UI / UX designers can communicate with developers through storyboards when developing mobile applications. However, UI / UX designers can create different widgets for ambiguous widgets. In this paper, we propose an automatic UI detection method using symbol markers to improve the accuracy of DNN (Deep Neural Network) based UI identification. In order to evaluate the performance with or without the symbol markers, their accuracy is compared. In order to improve the accuracy according to of the symbol marker, the results are analyzed when the shape is a circle or a parenthesis. The use of symbol markers will reduce feedback between developer and designer, time and cost, and reduce sketch image UI false positives and improve accuracy.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• Journal of Animal Reproduction and Biotechnology
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• v.37 no.2
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• pp.96-105
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• 2022

The ovary undergoes substantial physiological changes along with estrus phase to mediate negative/positive feedback to the upstream reproductive tissues and to play a role in producing a fertilizable oocyte in the developing follicles. However, the disorder of estrus cycle in female can lead to diseases, such as cystic ovary which is directly associated with decline of overall reproductive performance. In gene expression studies of ovaries, quantitative reverse transcription polymerase chain reaction (qPCR) assay has been widely applied. During this assay, although normalization of target genes against reference genes (RGs) has been indispensably conducted, the expression of RGs is also variable in each experimental condition which can result in false conclusion. Because the understanding for stable RG in porcine ovaries was still limited, we attempted to assess the stability of RGs from the pool of ten commonly used RGs (18S, B2M, PPIA, RPL4, SDHA, ACTB, GAPDH, HPRT1, YWHAZ, and TBP) in the porcine ovaries under different estrus phase (follicular and luteal phase) and cystic condition, using stable RG-finding programs (geNorm, Normfinder, and BestKeeper). The significant (p < 0.01) differences in Ct values of RGs in the porcine ovaries under different conditions were identified. In assessing the stability of RGs, three programs comprehensively agreed that TBP and YWHAZ were suitable RGs to study porcine ovaries under different conditions but ACTB and GAPDH were inappropriate RGs in this experimental condition. We hope that these results contribute to plan the experiment design in the field of reproductive physiology in pigs as reference data.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Hospice and Palliative Care
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• v.6 no.1
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• pp.22-33
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• 2003

Purpose : The purpose of this study was to evaluate the present status of patients' satisfaction and the reasons for any satisfaction or dissatisfaction in cancer pain management Methods : A cross-sectional survey was used to obtain the feedback about pain management. The results of the survey were collected from 59 in- or out-patient who had cancer treatment at two of the teaching hospitals in Seoul from July, 2002 to November, 2002. The data was obtained by a structured questionnaire based on the American Cancer Society Patient Outcome Questionnaire(APS-POQ) and other previous research. The clinical information for all patients were compiled by reviewing their medical records. Resuts : 1) The subjects' mean score of the worst pain was 6.77, the average pain score was 3.80, and the pain score after management was 2.93 for the past 24 hours. The mean score of total pain interference was $25.03{\pm}12.82$. Many of the subjects had false beliefs about pain such as 'the experience of pain is a sign that the illness has gotten worse', 'pain medicine should be 'saved' in case the pain gets worse' and 'people get addicted to pain medicine easily'. 2) 66.1% of the subjects were properly medicated with analgesics. 33.9% of the subjects reported use of various methods in controlling pain other than the prescribed medication. Only 33.9% of the subjects had a chance to be educated about pain management by doctors or nurses. 3) The mean score of patients' satisfaction with pain management was $4.19{\pm}1.14$. 72.9% of the subjects answered 'satisfied' with pain management. The reasons for dissatisfaction were 'the pain was not relieved even after the pain management', 'I was not quickly and promptly treated when I complained of pain', 'doctors and nurses didn't pay much attention to my complaints of pain.', and 'there was no appropriate information given on the methods of administration, effect duration and side effects of pain medicine.' The reasons for satisfaction were: 'the pain was relieved after the pain management.', 'doctors and nurses quickly and promptly controlled my pain.', 'doctors and nurses paid enough attention to my complaints of pain.' and 'trust in my physician'. 4) In pain severity or pain interference, no significant difference was found between the satisfied group and dissatisfied group. On the belief 'good patients avoid talking about pain', a significant difference was found between the satisfied group and dissatisfied group. Conclusions : The patients' satisfaction with cancer pain management has increased over the years but still about 30% of patients reported to be 'not satisfied' for various reasons. The results of this study suggest that patients' education should be done to improve satisfaction in the pain management program.