• 무역상무연구
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• 제51권
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• pp.3-54
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• 2011

The important and main purpose of revision of Incoterms rules are to adapt it to contemporary commercial practice in world trade, and to contribute to great service to foreign trade. Incoterms$^{(R)}$ 2010 revision basically focuses on trade terms to be used for any mode or modes of transport at compared Incoterms 2000 gouping in four basically different categories, and presents trade terms to be used for traditionally seaborn trade. Therefore the former is for trading in manufactured goods involved in containerization, the latter is for trading in community. This study focuses on a motive of revisions and main features of the Incoterms$^{(R)}$ 2010 rules, an outline, the problems and alternative of them. In conclusion, I would like present as follows; (1) Although they only concerned the models of delivery and critical point, they only say a few of the many factors of a sale contract, that is to say for the devision of fuctions, costs and risks between the contracting parties as supplement law, but the guestion of the legal position of Incoterms rules is various in different countries and scholars. in spite of that, it must focus on the practical application and the wide acceptance of Incoterms rules in world trade. (2) Although they present more simple and clear, unfricative, than Incoterms 2000 rules, in order to help users, the need to unify consistently and fully explanate in contents and expression. (3) Introduction and guidance note of Incoterms$^{(R)}$ 2010 rules basically focuses on the understanding of a motive of revisions as compared Incoterms 2000 rules. But it is well advised to do this on the assumption of understanding the various basic meaning of terms. (4) finally, trade concerned regulations take account of the reflection for the application to domestic and international trade according to formally reconization of availability for both trade.

• 한국과학교육학회지
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• 제37권4호
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• pp.705-717
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• 2017

본 연구에서는 중등 과학교사의 감정노동의 실태를 파악하고자 감정표현규칙, 감정노동 유형, 감정노동 사례, 과학 특정 감정표현규칙에 대해서 탐색하고자 하였다. 이를 위해 중등 과학교사를 대상으로 '과학교사 감정노동 설문지(TELSTS)'를 개발하여 145명의 설문응답 결과를 분석하였다. 분석 결과는 다음과 같다. 첫째, 중등 과학교사들은 학교현장에서 과학교사로서 요구되거나 기대되는 감정표현규칙이 있다는 것에 동의하였으며, 특히 긍정적 감정표현규칙을 더 잘 인식하고 있었다. 둘째, 중등과학교사들은 자신들이 인식한 감정표현규칙을 지키기 위해 감정노동을 수행하고 있는 것으로 나타났으며 특히 내면행위에 대한 응답 평균값이 높았다. 집단별 차이를 보면 정교사 여부와 교직경력에 따라서 감정노동에 유의미한 차이를 보였다. 셋째, 과학교사의 특수성을 분석한 결과, 주로 실험지도와 과학의 객관적, 논리적 이미지와 관련되었으며, 74%의 응답이 부정적이거나 중립적인 감정표현규칙에 해당하였다. 마지막으로 본 연구결과가 과학교육에 주는 시사점을 논의하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제6권3호
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• pp.507-528
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• 2014

In the ship and offshore structure design, age-related problems such as corrosion damage, local denting, and fatigue damage are important factors to be considered in building a reliable structure as they have a significant influence on the residual structural capacity. In shipping, corrosion addition methods are widely adopted in structural design to prevent structural capacity degradation. The present study focuses on the historical trend of corrosion addition rules for ship structural design and investigates their effects on the ultimate strength performance such as hull girder and stiffened panel of double hull oil tankers. Three types of rules based on corrosion addition models, namely historic corrosion rules (pre-CSR), Common Structural Rules (CSR), and harmonised Common Structural Rules (CSR-H) are considered and compared with two other corrosion models namely UGS model, suggested by the Union of Greek Shipowners (UGS), and Time-Dependent Corrosion Wastage Model (TDCWM). To identify the general trend in the effects of corrosion damage on the ultimate longitudinal strength performance, the corrosion addition rules are applied to four representative sizes of double hull oil tankers namely Panamax, Aframax, Suezmax, and VLCC. The results are helpful in understanding the trend of corrosion additions for tanker structures.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.877-880
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• 2000

In this paper, we develop an algorithm of soccer image sequences mosaicing using reverse affine transform. The continuous mosaic images of soccer ground field allows the user/viewer to view a “wide picture” of the player’s actions The first step of our algorithm is to automatic detection and tracking player, ball and some lines such as center circle, sideline, penalty line and so on. For this purpose, we use the ground field extraction algorithm using color information and player and line detection algorithm using four P-rules and two L-rules. The second step is Affine transform to map the points from image to model coordinate using predefined and pre-detected four points. General Affine transformation has many holes in target image. In order to delete these holes, we use reverse Affine transform. We tested our method in real image sequence and the experimental results are given.

• 전기학회논문지
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• 제59권5호
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• International Journal of Precision Engineering and Manufacturing
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• 제2권1호
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• pp.26-32
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• 2001

An ecpert system for rotationally symmetric deep drawing products has been developed. The application for non-axisymmetric components, however, has not been reported yet. This study construsctus and expert system for non-axisymmetric motor frame which shape is classified into ellipse in deep draqing process and investigates process sequence design with elliptical shape. The developed system consists of four modules. The first is recognition of calculate surface area for non-axisymmetric products. The third is blank design module the creates an oval-shaped blank with the same surface area. The fourth is a processplanning module based on production rules that play the best important roles in an expert system for manufacturing .The production rules are generated and upgraded by interviewing field engineers. Especially, drawing coefficient, punch and die radii for elliptical shape products are considered as main design parameters. The constructed system for elliptical deep drawing product would be very useful to reduce lead time and improve accuracy for products.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• 산업공학
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• 제11권1호
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• pp.97-103
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• 1998

In these days many people have studied on the berthing problem. The operation rules differ from port so port and the problem is highly dependent on the environment of port. The purpose of this study is to develop a decision support system decision makers of the berthing problem for Inchon Port. The system is developed with graphic user interface(GUI) using user-interactive approach and some general and specific rules for Inchon Prot are considered. The system is composed of the following four parts ; the input/output part, the automatic berthing part by the system using rules, the manual berthing part by user, and the part for modifying results or handling exceptional events. The system is designed to assign ship to berths by matching the characteristics with environmental and operational constraints of Inchon Port. We expect that this system can provide decision makers with an efficient and fast way to berthing and can reduce wastes of time, space, and manpower in port operations.

• 대한전기학회논문지
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• 제39권5호
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• pp.527-537
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• 1990

This paper describes an intelligent control-based job scheduler, named ESIJOBS, for flexible manufacturing system. In order to construct rulebase of this system, traditional rules of job scheduling in FMS are examined and evaluated. This result and the repetitional simulations with graphic monitoring system are used to form the rulebase of ESIJOBS, which is composed of three parts:six part selection rules, four machine center selection rules, and twenty-one metarules. Appropriate scheduling rule sets are selected by this rulebase and manufacturing system status. The performances of all simulations are affected by random breakdowns of major FMS components during each simulation. Six criteria are used to evaluate the performance of each scheduling. The two modes of ESIJOBS are simulated and compared with combinational 24 rule-set simulations. In this comparison ESIJOBS dominated the other rule-set simulations and showed the most excellent performance particularly in three criteria.

• 한국지능시스템학회논문지
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• 제14권6호
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.