• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.47-73
• /
• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Food Science of Animal Resources
• /
• v.30 no.6
• /
• pp.896-904
• /
• 2010

HACCP is a scientific and systematic program that identifies specific hazards and gives measurements in order to control them and ensure the safety of foods. Transportation of livestock and its products is one of the vulnerable sectors regarding food safety in Korea, as meats are transported by truck in the form of a carcass or packaged meat in a box. HACCP application and its acceleration of distribution, in particular transportation, are regarded as important to providing consumers with ultimately safe livestock products. To achieve this goal, practical tools for HACCP application should be developed. Supply chain management (SCM) is a holistic and strategic approach to demand, operations, procurement, and logistics process management. SCM has been beneficially applied to several industries, notably in vehicle manufacture and the retail trade. HACCP-based real-time visibility system using wireless application (WAP) of the livestock distribution is centralized management system that enables control of temperature and HACCP management in real-time for livestock transportation. Therefore, the application of HACCP to livestock distribution (transportation, storage, and sale) can be activated. Using this system, HACCP management can be made easier, and distribution of safe livestock products can be achieved.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.329-339
• /
• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• Journal of agricultural medicine and community health
• /
• v.20 no.2
• /
• pp.157-168
• /
• 1995

This study was carried out, through analyzing the annual reports(year of 1973-1993) on health status of Su Dong-Myun, and specific survey data of 332 households(Su Dong-Myun 209, Byul Nae-Myun 123), located in Nam Yang Ju-Si, Kyung Gi-Do, from July 20 to July 31, 1995, to find out more effective means for primary medical care in a rural area. The results were as fellows : 1. Number of population in Su Dong-Myun was 5,419 in 1973, 4,591(the lowest) in 1987 and 5,707 in 1995. In the composition rate of population, "0-14" of age group showed markedly decreasing tendency from 43.1% in 1975, to 19.1% in 1995, however "65 and over" markedly in creasing tendency form 5.3% in 1975 to 9.8% in 1995. 2. Annual utilization rate per 1,000 inhabitants in Su Dong-Myun showed markedly increasing tendency from 1973 to 1977 such as 343 in 1973, 540 in 1975, 900 in 1977. However, since 1979, the rate showed rapidly decreasing tendency, such as 846 in 1979, 519 in 1985, 190 in 1991 and 1993. 3. The morbid household rate per year was 53.6% of respondents and the rate per 15 days was 48.2%. In disease classification rate of morbid household per year, Arthralgia & Neuralgia was the highest rate(33.9%) and gastro-intestinal disorder(19.3%), Cough(11,9%), Hypertension(7.8%), Accident(3.2%) in next order. 4. In the utilizing facilities for Primary Medical Care, Medical facilities was showed the highest rate(58.1% of respondents) and Pharmacy and Drug Shp(33.1%), Tradition Method(4.0%) in next order. In the Medical facilities, General private clinic was showed the highest rate(34.3%) and specific private Clinic(22.3%), Hospital(19.0%), Health (Sub)center(16.3%), Nurse practitioner (3.3%), Oriental hospital and clinic(2.7%) in next order. 5. Experience rate, utilizing health subcenter was 51.8% of the respondents, and it was 55.0% in Su Dong-Myun and 46.3% in Byul Nae-Myun. In utilization times of health subcenter, times-rate showed next orders such as 1-2 times/6months(31.6%), 1-2 times/year (22.1%), 1-2 times/months(19.2%), 1-2 times/3months(15.6%). 6. In objectives, visiting Health Subcenter, Medical Care was the highest rate(59.8% of the respondents) and health control(23.3%) was in next order. In Medical Care, Primary Care by general physician was higher rate(51.1%) almost all. In the Health control, Immunization too was high rate(18.0%) in health control activities. 7. The reasons rate, utilizing health subcenter showed next order, such as distance to Medical facilities(33.0% of the respondents), Medical Cost(28.1%), Simple process of consultation (10.8%), Effectiveness of cure(7.6%), Function of primary medical care(7.0%) and Attitude of physician(6.5%). 8. In the affecting factors to utilization of primary medical facilities, medical needs was showed the highest rate(29.5% of the respondents) and medical cost(15.4%), distance to medical facilities(14.2%), traffic vehicle(14.2%) and farm work(6.9%) in next order. 9. In the priority between 'daily farm work,' and 'primary medical care', only 46.4% of respondents answered that primary health care is more important than the daily farm work The 22.6% of respondents answered 'daily farm work', and the 12.3% answered 'the equal of the both'. 10. In the criterion of medical facilities choice, medical knowledge and technical quality was showed the highest rate(56.3%), distance or time to medical facilities(10.9%), sincerity and kindness of physician(9.4%), medical cost(8.7%) and traffic vehicle(6.5%) in next order 11. In the advise for improvement of health subcenter function, the 36.1% of respondents answered that 'enforcement of medical personnel and equipment' was required, and then 'improved medical technology'(25.5%), 'good attitude of physician'(14.9%), 'improved medical system'(13.3%), 'enforced drug'(6.7%) in next order. 12. The study on affecting factors to utilization of primary medical facilities was very difficult subject to systematize the analyzed results, due to a prejudice of protocol planner, surveyer and respondent, and variety and overlapping of subject matter.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.1-16
• /
• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.135-155
• /
• 2018

The purpose of this study is to analyze the difference of interpretation level and intention to use message framing when autonomous vehicle, which is emerging as the product of 4th industrial revolution, is used as taxi, Interpretation level refers to the interpretation of a product or service, assuming that it will happen in the near future or in the distant future. Message framing refers to the formation of positive or negative expressions or messages at the extremes of benefits and losses. In other words, previous studies interpret the value of a product or service differently according to these two concepts. The purpose of this study is to investigate whether there are differences in intention to use when two concepts are applied when an autonomous vehicle is launched as a taxi. The results are summarized as follows: First, the message format explaining the gain and why should be used when using the autonomous taxi in the message framing configuration, and the loss and how when the autonomous taxi is not used. Messages were constructed and compared. The two message framing differed (t = 3.063), and the message type describing the benefits and reasons showed a higher intention to use. In addition, the results according to interpretation level are summarized as follows. There was a difference in intentions to use when assuming that it would occur in the near future and in the near future with respect to the gain and loss, Respectively. In summary, in order to increase the intention of using autonomous taxis, it is concluded that messages should be given to people assuming positive messages (Gain) and what can happen in the distant future. In addition, this study will be able to utilize the research method in studying intention to use new technology. However, this study has the following limitations. First, it assumes message framing and time without user experience of autonomous taxi. This will be different from the actual experience of using an autonomous taxi in the future. Second, self-driving cars should technical progress is continuing, but laws and institutions must be established in order to commercialize it and build the infrastructure to operate the autonomous car. Considering this fact, the results of this study can not reflect a more realistic aspect. However, there is a practical limit to search for users with sufficient experience in new technologies such as autonomous vehicles. In fact, although the autonomous car to take advantage of the public transportation by taxi is now ready for the road infrastructure, and technical and legal public may not be willing to choose to not have enough knowledge to use the Autonomous cab. Therefore, the main purpose of this study is that by assuming that autonomous cars will be commercialized by taxi you can do to take advantage of the autonomous car, it is necessary to frame the message, why can most effectively be used to find how to deliver. In addition, the research methodology should be improved and future research should be done as follows. First, most students responded in this study. It is also true that it is difficult to generalize the hypotheses to be tested in this study. Therefore, in future studies, it would be reasonable to investigate the population of various distribution considering the age, area, occupation, education level, etc. Where autonomous taxi can be used rather than those who can drive. Second, it is desirable to construct various message framing of the questionnaire, but it is necessary to learn various message framing in advance and to prevent errors in response to the next message framing. Therefore, it is desirable to measure the message framing with a certain amount of time when the questionnaire is designed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2018.11a
• /
• pp.225-227
• /
• 2018

This research paper examines the history and present of 'temporary passengers' prescribed in Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations and suggests improvement plans referring to the examples of legislation of other countries. In 2015, Ministry of Ocean and Fisheries made authoritative interpretation that Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations, which prescribes special cargo drivers such as agricultural, marine or livestock vehicles as temporary passengers, is applied only to passenger ships and not to cargo ships such including Ro-Ro cargo ships. As the authoritative interpretation of the Ministry does not agree with not only the traditional interpretational methodology but also the interpretational methodology that are commonly used today, it lacks logical basis and looks unpersuasive. Paragraph 9 of Article 5 of the Ships Safety Act Enforcement Regulations can be applied not only on passenger ships but also on cargo ships. Also in case of Ro-Ro cargo ships, it is logically contradictory and against fairness not to acknowledge special cargo vehicle drivers as temporary passengers when there is no problem with safe navigation and safety of passengers on board even when the sailor, the sailor's family and the ship owner may be acknowledged as temporary passengers. To avoid unnecessary disputes and lawsuits, improvement plans using theory of legislation through statutory reform is more desirable. Therefore, the P aragraph should be amended to "Supercargo who deals with cargo that requires special care due to the characteristics of the cargo, such as transportation vehicles for agricultural products, marine products, livestock, explosives or flammable materials (drivers can serve as supercargos)" to reflect the distinct characteristics of cargo and ship navigation in Korea including the current distribution system, while setting an objective standard based on common sense of ordinary people and not on arbitrary interpretation.

• Land and Housing Review
• /
• v.13 no.4
• /
• pp.55-71
• /
• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• The Korean Journal of Air & Space Law and Policy
• /
• v.33 no.2
• /
• pp.61-114
• /
• 2018

Recently airspace became a hot issue considering today's international relations. However, there was no data that could be fully explained about a legal system of korean airspace, so I looked at law and practice about korean airspace together. The nation's aviation law sector is comletely separate from those related to civil and military aircraft, at least in legal terms. The Minister of Land, Infrastructure and Transport shall carry out his/her duties with various authority granted by the "Aviation Safety Act". The nation's aviation-related content is being regulated too much by the Ministry of Land, Infrastructure and Transport's notice or regulation, and there are many things that are not well known about which clauses of the upper law are associated with. The notice should be clearly described only in detail on delegated matters. As for the airspace system, the airspace system is too complex for the public to understand, and there seems to be a gap between law and practice. Therefore, I think it would be good to reestablish a simple and practical airspace system. Airspace and aviation related tasks in the military need to be clearly understood by distinguishing between those entrusted by the Minister of Land, Infrastructure and Transport and those inherent in the military. Regarding matters entrusted by the Minister of Land, Infrastructure and Transpor, it is necessary to work closely with the Minister of Land, Infrastructure and Transport when preparing related work guidelines, and to clarify who should prepare the guidelines. Regarding airspace control as a military operation, policies or guidelines that are faithful to military doctrine on airspace control are needed.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.199-230
• /
• 2021

Innovative new products and services are being launched through the convergence between heterogeneous industries, and social interest and investment in convergence industries such as AI, big data-based future cars, and robots are continuously increasing. However, in the process of commercialization of convergence new products and services, there are many cases where they do not conform to the existing regulatory and legal system, which causes many difficulties in companies launching their products and services into the market. In response to these industrial changes, the current government is promoting the improvement of existing regulatory mechanisms applied to the relevant industry along with the expansion of investment in new industries. This study, in these convergence industry trends, aimed to analysis the existing regulatory system that is an obstacle to market entry of innovative new products and services in order to preemptively predict regulatory issues that will arise in emerging industries. In addition, it was intended to establish a regulatory impact analysis system to evaluate adequacy and prepare improvement measures. The flow of this study is divided into three parts. In the first part, previous studies on regulatory impact analysis and evaluation systems are investigated. This was used as basic data for the development direction of the regulatory impact framework, indicators and items. In the second regulatory impact analysis framework development part, indicators and items are developed based on the previously investigated data, and these are applied to each stage of the framework. In the last part, a case study was presented to solve the regulatory issues faced by actual companies by applying the developed regulatory impact analysis framework. The case study included the autonomous/electric vehicle industry and the Internet of Things (IoT) industry, because it is one of the emerging industries that the Korean government is most interested in recently, and is judged to be most relevant to the realization of an intelligent information society. Specifically, the regulatory impact analysis framework proposed in this study consists of a total of five steps. The first step is to identify the industrial size of the target products and services, related policies, and regulatory issues. In the second stage, regulatory issues are discovered through review of regulatory improvement items for each stage of commercialization (planning, production, commercialization). In the next step, factors related to regulatory compliance costs are derived and costs incurred for existing regulatory compliance are calculated. In the fourth stage, an alternative is prepared by gathering opinions of the relevant industry and experts in the field, and the necessity, validity, and adequacy of the alternative are reviewed. Finally, in the final stage, the adopted alternatives are formulated so that they can be applied to the legislation, and the alternatives are reviewed by legal experts. The implications of this study are summarized as follows. From a theoretical point of view, it is meaningful in that it clearly presents a series of procedures for regulatory impact analysis as a framework. Although previous studies mainly discussed the importance and necessity of regulatory impact analysis, this study presented a systematic framework in consideration of the various factors required for regulatory impact analysis suggested by prior studies. From a practical point of view, this study has significance in that it was applied to actual regulatory issues based on the regulatory impact analysis framework proposed above. The results of this study show that proposals related to regulatory issues were submitted to government departments and finally the current law was revised, suggesting that the framework proposed in this study can be an effective way to resolve regulatory issues. It is expected that the regulatory impact analysis framework proposed in this study will be a meaningful guideline for technology policy researchers and policy makers in the future.