• Asia pacific journal of information systems
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• v.20 no.1
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

• The Korean Journal of Pesticide Science
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• v.17 no.1
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• pp.60-64
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• 2013

This study was performed in order to assess the risk of pesticide operator in korean rice paddy. The amount of exposure was calculated based on the informations of 48 kind of items that are used the form of a spraying in korean rice paddy. The risks of these pesticides were assessed to compare the exposure dosage with AOEL (acceptable operator exposure level) which was released form developed countries. When the operator spraying pesticides using motor sprayer, 17 items showed the risk for operator if the operator have not worn the personal protective equipment (PPE), and 6 items showed the risk for operator if the operator have worn the PPE. These result were indicated that the risk of pesticide operator was very high in korean rice paddy.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.67-80
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• 2022

Recently, many laundry home appliances have been released with Internet of Things (IoT) functions, but there are few quality evaluation tests for IoT functions. In particular, since IoT test items are not prepared for laundry home appliances applied with IoT functions, it is difficult to find the cause even if defects occur, and test institutions are limited in selecting test items related to IoT and conducting proper performance tests. In this paper, we design a test item framework that separates IoT test items into commonality and variability to identify product defects and causes for laundry home appliances with IoT features among products in the field of home appliances. Through the proposed research, manufacturers and test institutions can test the proper performance of laundry products with IoT functions, which can improve the completeness of the products and ensure reliability.

• Proceedings of the Membrane Society of Korea Conference
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• 1999.10a
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• pp.39-39
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• 1999

Membrane separation technology has become a more attractive technology on waste water treatment and water recycle in recent years. On this application, membrane does not take main part of treatment, such as decomposition or handling of organic matter in the waste water, but it is very important supporting method in the total system. Activated sludge is most popular method as main part. In the system , membrane works as a separator to obtain clear water after biological treatment, by which the permeate could be released, recycled or applied to further additional treatment, instead of conventional sedimentation, coagulation and sand filtration. We would like to introduce our system cases for waste water treatment and water recycle, in which membrane separation technology works. In most of cases, membranes are applied to solid- liquid separation of activated sludge. Our experiences will be introduced as following items.

• The Korean Journal of Pesticide Science
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• v.17 no.1
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• pp.50-59
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• 2013

This study was performed in order to assess the risk of korean orchard worker due to pesticide exposure. The amount of pesticide exposure was calculated based on the informations of 97 kind of items that are used the form of a spraying in Korean orchard. The risks of these pesticides were assessed to compare the exposure amount with AOEL (acceptable operator exposure level) which was released form developed countries. When the operator sprayed pesticides using speed sprayer, 74.2% of pesticide items showed the risk for operator if the operator have not worn the personal protective equipment (PPE), and 42.3% of pesticide items showed the risk for operator if the operator have worn the PPE. In case of using motor sprayer, 64.1% of pesticide items showed the risk for operator if the operator have not worn the PPE, and 19.4% of pesticide items showed the risk for operator if the operator have worn the PPE. This result was indicated that the risk of pesticide against the operator was very high in korean orchard operator.

• Culinary science and hospitality research
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• v.20 no.1
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• pp.76-88
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• 2014

This research has been conducted to provide the fundamental data for foodservice & culinary research by analyzing 1,054 papers in 'The Korean Journal of Culinary Research' from 1995 through 2012 regarding year, theme, material, and subject. The released papers are 58.6 a year and almost 50 percent of related papers have been published, considering that 511 papers have been released for the recent five years. The research theme was first classified into four following areas such as foodservice, culinary, food and so forth. Then it was subdivided under 65 items on 19 subject matters. Most of the studies are related to foodservice area(595 papers, 56.4%), followed by culinary area (250 papers, 23.7%), food area(105 papers, 10.0%) and other areas (104 papers, 9.9%). For the topics used in foodservice research, most papers investigated the marketing area(170 paper, 28.6%). In culinary and food-related research, papers dealt with confectionary and bread (67 papers, 23.3%), sauce dressing (38 papers, 13.2%), vegetable fruit (36 papers, 12.5%), fermented food (35 papers, 12.5). In regard to the analysis of research subjects, institutions, and companies, 140 papers(38.2%) conducted hotel-related research, followed by 106 restaurant-related papers (29%) and forty-three papers(11.8%) on foodservice enterprises and franchises. Based on this analysis, most of the theses in 'The Korean Journal of Culinary Research' have been published in the area of foodservice (56.4%). In comparison, the theses in the area of culinary and food are associated with the name of the journal and only 33.7 percent of papers have been released. For these reasons, the name of the journal would rather be renamed 'The Korean Journal of Foodservice and Culinary Research'.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.577-588
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• 2013

Main purpose of this study is to suggest improvement of environmental information disclosure system and to find out counterplan for hospitals based on literal review and 41 hospitals/clinic's environmental information data(2011) released by the Korean ENV-INFO SYSTEM. The research methodology used to analyze 18 items divided between 7 for compulsory and 11 for voluntary was primarily quantitative. Research subjects was comprised of 33 general hospitals, 6 hospitals and 1 clinic. Environmental information disclosure system needs to be improved as follows: (1) enlargement of citizen participation, (2) upgrade of 'Company Overview', (3) clear definition of items, (4) unifying measure unit, (5) close verification, (6) creating standards for additional informations. The following activities are critical for hospitals: (1) reliability enhancement of hospital's data, (2) reorganization strategy & green management system, (3) water/energy reduction data accumulation, (4) greenhouse gas emission reduction planning, (5) introduction of green purchase guidelines, (6) digital publication of environmental(sustainable) report.

• Journal of Educational Research in Mathematics
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• v.22 no.1
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• pp.69-99
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• 2012

This study aims to develop a highly-reliable inventory for measuring and analyzing the positive psychological experience in math learning. A measurement result provides meaningful information on the strength of students. Elements of positive psychological experience in math learning were extracted by the extensive research on the literature. The result was verified through the open survey. Through this process, the first preliminary questionnaire of 106 items was released, and was revised into the second one of 66 items through a continuous investigation on reliability and validity. Finally, a measurement inventory with 65 questions for testing 'positive psychological experience in math learning' was established.

• Journal of The Korean Association of Information Education
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• v.21 no.3
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• pp.361-370
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• 2017

It is in the vortex of the Fourth Industrial Revolution in all areas of our society. Education is no exception. We all know that software is at the center of the education in the 4th industry revolution. It is very important for students to survey the ICT background in order to be good at software education. The OECD PISA has surveyed ICT background of students in a three-year cycle from 2003. This study analyzes PISA ICT statistical data based on the data released in April 2017. We compare and analyze the availability and usability items among the 81 items of the ICT background of the students. The results of linear regression analysis showed that Korea was the lowest among OECD countries. ICT availability is ranked 28 out of 30 countries and ICT usability is ranked 31 out of 31 countries. This reflects the fact that ICT education is not implemented in the country since 2008. For the students who will be the leaders of the future society, ICT education that had implemented in 2000 should be carried out quickly.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.156-163
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• 2006

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.