• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of Preventive Medicine and Public Health
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• v.26 no.4 s.44
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• pp.628-649
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• 1993

This study was conducted to investigate the factors related to the poor school performance of the elementary school children. Two schools in Taegu, one in the affluent area and the other in the poor area, were selected and a total of 175 children whose school performance was within low 10 percentile (poor performers) and 97 children whose school performance were within high 5 percentile (good performers) in each class of 2nd, 4th and 6th grades were tested for the physical health, behavioral problem and family background. Each child had gone through a battery of tests including visual and hearing acuity, anthropometry (body weight, height, head circumference), intelligence (Kodae Stanford-Binet test), test anxiety (TAI-K), neurologic examination by a developmental pediatrician and heavy metal content (Pb, Cd, Zn) in hair by atomic absorption spectrophotometry. A questionnaire was administered to the mothers for prenatal and prenatal courses of the child, family environment, child's developmental history, and child's behavioral and learning problems. Another questionnaire was administered to the teachers of the children for the child's family background, arithmatic & language abilities and behavioral problem. The poor school performance had a significant correlation with male gender, high birth order, broken home, low educational and occupational levels of parents, visual problem, high test anxiety score, attention deficit hyperactivity disorder (ADHD), poor physical growth (weight, height, head circumference) and low I.Q. score. The factors that had a significant correlation with the poor school performance in multiple logistic regression analysis were child's birth order (odds ratio=2.06), male gender(odds ratio=5.91), broken home(odds ratio=9.29), test anxiety score(odds ratio=1.07), ADHD (odds ratio=9.67), I.Q. score (odds ratio=0.85) and height less than Korean standard mean-1S.D.(odds ratio=11.12). The heavy metal contents in hair did not show any significant correlation with poor school performance. However the lead and cadmium contents were high in males than in females. The lead content was negatively correlated with child's grade(P<0.05) and zinc was positively correlated with grade (P<0.05). among the factors that showed a significant correlation with the poor school performance, high birth order, short stature and ADHD may be modified by a good family planning, good feeding practice for infant and child, and early detection and treatment of ADHD. Also, teacher and parents should restrain themselves from inducing excessive test anxiety by forcing the child to study and over-expecting beyond the child's intellectual capability.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.57-76
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• 2017

Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of the Korean Society of School Health
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• v.1 no.2
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• pp.118-135
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• 1988

This study is designed to analyze the problems of health education in schools and explore the ways of enhancing health education from a historical perspective. It also shed light on the managerial aspect of health education (including medical-check-up for students disease management. school feeding and the health education law and its organization) as well as its educational aspect (including curriculum, teaching & learning, and wishes of teachers). At the same time it attempted to present the ways of resolving the problems in health education as identified her. Its major findings are as follows; I. Colculsion and Summary 1. Despite the importance of health education, the area remains relatively undeveloped. Students spend a greater part of their time in schools. Hence the government should develop a keener awareness of the importance of health education and invest more in it to ensure a healthy, comfortable life for students. 2. At the moment the outcomes of medical-check-up for students, which constitutes the mainstay of health education, are used only as statistical data to report to the relevant authorities. Needless to say they should be used to help improve the wellbeing of students. Specifically, nurse-teachers and home-room teachers should share the outcomes of medical-check-up to help the students wit shortcomings in growth or development or other physical handicaps more clearly recognize their problems and correct them if possible. 3. In the area of disease management, 62.6, 30.3 and 23.0 percent of primary, middle, and highschool students, respectively, were found to suffer from dental ailments. By contrast 2.2, 7.8, and 11.5 percent of primary, middle and highschool students suffered from visual disorders. The incidence of dental ailments decreases while that of visual impairments increases as students grow up. This signifies that students are under tremendous physical strain in their efforts to be admitted by schools of higher grade. Accordingly the relevant authorities should revise the current admission system as well as improve lighting system in classrooms. 4. Budget restraints have often been cited as a major bottleneck to the expansion of school feeding. Nevertheless it should be extended at least, to all primary schools even at the expense of parents to ensure the sound growth of children by improving their diet. 5. The existing health education law should be revised in such a way as to better meet the needs of schools. Also the manpower for health education should be strengthened. 6. Proper curriculum is essential to the effective implementation of health education. Hence it is necessary to remove those parts in the current health education curriculum that overlaps with other subjects. It is also necessary to make health education a compulsory course in teachers' college at the same time the teachers in charge of health education should be given an in-service training. 7. Currently health education is being taught as part of physical education, science, home economics or other courses. However these subjects tend to be overshadowed by English, mathematics, and other subjects which carry heavier weight in admission test. It is necessary among other things, to develop an educational plan specifying the course hours and teaching materials. 8. Health education is carried out by nurse-teachers or home-room teachers. In connection with health education, they expressed the hope that health education will be normalized with newly-developed teaching material, expanded opportunity for in-service training and increased budget, facilities and supply of manpower. These are the mainpoints that the decision-makers should take into account in the formation of future policy for health education. II. Recommendations for the Improvement of Health Education 1. Regular medical check-up for students, which now is the mainstay of health education, should be used as educational data in an appropriate manner. For instance the records of medical check-up could be transferred between schools. 2. School feeding should be expanded at least in primary schools at the expense of the government or even parents. It will help improve the physical wellbeing of youths and the diet for the people. 3. At the moment the health education law is only nominal. Hence the law should be revised in such a way as to ensure the physical wellbeing of students and faculty. 4. Health education should be made a compulsory course in teachers' college. Also the teachers in service should be offered training in health education. 5. The curriculum of health education should be revised. Also the course hours should be extended or readjusted to better meet the needs of students. 6. In the meantime the course hours should be strictly observed, while educational materials should be revised in no time. 7. The government should expand its investment in facilities, budget and personnel for health education in schools at all levels.

• Proceedings of the Korean DIstribution Association Conference
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• 2002.05a
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• pp.31-59
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• 2002

Recently there are many changes of logistics environment Such as integrated logistics information system, the rapid growth of the domestic and international small package express service and third party logistics with Electronic Commerce. At this time it is very important to deliver to customers the goods sold through EC speedy, accurately and safely. That is to say, the role of small package express service is very important at EC times. The bottlenecks of small package express service in the circumstances of EC are the weakness of EC operating company and small package express service provider the shortage of distribution centre and cargo terminal, the shortage of skilled man with related small package express service etc. So, I suggested that for activation of EC it is necessary to strengthen the strategic alliances, introduce GPS and use the third party logistics positively in the side of small package express service provider. And it is necessary to prepare for the settlements of traffic problems, support the introduction of integrated logistics service, logistics information system, deregulate restriction such as weight limit of vehicles in the side of the government. And to government support throughout extending nation's SOC, deregulation, support to small package express service terminal, permit to stopping & parking in downtown, abolishing a no passing zone, permit to being employed foreigner. Also this service involves ensuring that the product will arrive when wanted, and in an undamaged condition.

• 대한공업교육학회지
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• v.33 no.2
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• pp.192-217
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• 2008

This paper investigated the perceived criticalities and patterns of Korean Professional Engineer's competency regarding the working activities of automative product development, manufacturing, etc by using questionnaires responded to the survey which were applied to the automotive professors, experts and professional engineers (vocational parties) by e/mail, etc. This research investigated the following questions: First, what are the characteristic patterns, relevancy and perceived criticalities of Korean Professional Engineer's competencies? Second, What are the ranked priority of the Korean Professional Engineers' competencies? Are there any differency for each item, sub group of job, intelectual criterior of the competencies between relevancy and perceived criticalities according to the types of vocational parties, etc.? Accoring to the results; first, Professor group showed highest points among 3 groups per each item of the competencies by vocational parties Second, Chassis design group ranked top position among the 8 sub groups by vocational parties and, third, Problem Solving Knowledge ranked highest points than any others. Korean Professional Engineers are found to be positioned as key members, leaders and managers on surveying market, product planning, designing product & components, developing component parts, establishing shop with production equipment, managing quality control & material handling, organizing relevant meetings, developing human resources by training and learning, to back up finance with law matters, cooperating with concerned parties to achieve organizational goals, and to coordinate projects. etc, identifying ethical issues and business skills in order to survive and win to be competitive in various kinds of the automotive industry battle fields.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.12
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• pp.1856-1864
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• 2015

The purpose of this study was to investigate the nutrient intake status of university students in Chuncheon area (175 males and 131 females). This study was conducted by employing a self-administered questionnaire. Dietary assessment was measured by a 24-h recall method. The average height and weight of male students were $175.2{\pm}6.2cm$ and $68.2{\pm}9.9kg$, respectively. For female students, average values were $161.7{\pm}5.2cm$ and $55.1{\pm}6.5kg$, respectively. The mean BMIs for both male and female students were 22.2 and 21.1, respectively. In both male and female students, the rate of skipping breakfast was high. Daily averages for energy, carbohydrates, protein, and fat intakes in male students were significantly higher than those of female students (P<0.001). For male students, protein, vitamin B1, P, Fe, and Na were above recommended nutrient intake and adequate intake, whereas for female students, they were protein, vitamin A, P, and Na. For male students, nutrient intakes for Ca, vitamin $B_2$, vitamin C, and vitamin $B_6$ were below the estimated average requirement (EAR) by at least 50% or more, whereas for female students, they were vitamin C, Fe, vitamin $B_6$, vitamin $B_2$, niacin, folate, and Ca. Ca was alarmingly low, with more than 75% of both male and female students showing levels below the EAR. Therefore, it is important that nutritional education be facilitated for college students to take responsibility of their own health through learning about nutrient intake as well as developing positive eating habits.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.