• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.329-336
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• 2007

This paper considers a fault accommodation problem for inertial navigation systems (INS) that have redundant inertial sensors such as gyroscopes and accelerometers. It is wellknown that the more sensors are used, the smaller the navigation error of INS is, which means that the error covariance of the position estimate becomes less. Thus, when it is decided that double faults occur in the inertial sensors due to fault detection and isolation (FDI), it is necessary to decide whether the faulty sensors should be excluded or not. A new accommodation rule for double faults is proposed based on the error covariance of triad-solution of redundant inertial sensors, which is related to the navigation accuracy of INS. The proposed accommodation rule provides decision rules to determine which sensors should be excluded among faulty sensors. Monte Carlo simulation is performed for dodecahedron configuration, in which case the proposed accommodation rule can be drawn in the decision space of the two-dimensional Cartesian coordinate system.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.15
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• pp.119-136
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• 2001

The purpose of this paper is to consider how to decide the cause of loss or damage to the transport goods when maritime accident occurs. In marine insurance, the underwriter is liable for any loss or damage proximately caused by a risk insured(MIA Art.55). So it is very important to determine the proximate cause of loss or damage to ascertain whether it is to be recoverable under the policy. But there is no definite conception or rule what is the proximate cause. It was left to the tribunal as a question of fact. In this paper, I will suggest the general rules to determine the proximate cause of loss or damage of the transport goods in marine insurance. First, in MIA 1906, there is the rule of proximate causation and it has been established the effective causation by cases since 1918. Second, in Institute Cargo Clauses(B) & (C), there are rules of considerably relaxed standards to determine the causation of loss of or damage using the "attributable to" and "caused by" basis. Third, it is noted, under the complex causation situation, there are difference basises to decide the liability of underwriters between the case of successive occurrence of single risk and the case of concurrent occurrence of several risks. Forth, in practice, it couldn't be ascertained the underwrier's liability by a definite rule and it should be fully considered the circumstances and conditions of the loss.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.552-555
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• 1998

In dealing with representing knowledge under uncertainty there is a sustain tendency to increase flexibility in order to avoid problems of inconsistency in the knowledge. Many knowledge systems(information retrieval systems, expert system) include hybrid representation models. Funny retrieval systems appear as a complement or as an enrichment of this models. In this paper, we describe dynamic rule modification through situation assessment for uncertainty management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.115-126
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• 2000

In General, data mining is defined as the knowledge discovery or extracting hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important works is to find association rules in data mining. Association Rule is mainly being used in basket analysis. In addition, it has been used in the analysis of web-log and user-pattern. This paper provides the application method in the field of marketing through the analysis of data using association rule as a technique of data mining.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.231-243
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• 1997

Objective of the study was to investigate characteristics and meanings of the agricultural extension work during the period of Japanese $rule(1906{\sim}1945)$ through historical review on the work. The agricultural extension work during the period of Japanese rule was categorized into six patterns such as agricultural extension work by (1) government agricultural administration system, (2) agricultural experiment/research institute, (3) public school, (4) through the movement of rural development(Nong-chon-Jinheung Undong), (5) pro-government private organization, and (6) pure private organization. In conclusion, the characteristics of the extension work were defined; 1) a tool and/or method for colonial rule, 2) non-democratic and top-down extension work by administration, 3) authoritative and coercive work initiated by governmental system, 4) nation wide work of cooperation with public and private organization, 5) extension work focused on production increasing food and industrial materials, and 6) extension work initiated by government system without participation of pure private organization. The meanings of the extension work were reviewed in two ways. Firstly, in macro point of view, the work has negative meaning that was utilized as a tool and/or method for Japanese colonial rule, since the extension work was implemented under the direction of agricultural policy and was also directly related to the policy of Japanese colonial government. Secondly, in micro point of view, the work has significant meaning either negative or positive such as 1) beginning of modernized extension work based on experiment and research, 2) introduction of forcible agricultural extension work, 3) inducement of distrust tendency to agricultural policy and agricultural extension work, 4) establishment of basis in hardware of agricultural extension work, 5) production of experience in software of agricultural extension work, and 6) improvement of recognition toward scientific farming and improvement of level in agricultural techniques.

• Korean Management Science Review
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• v.13 no.2
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• pp.25-48
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• 1996

Mixed Model Assembly Lines (MMALs) are increasingly used to produce differentiated products on a single assembly line without work-in-process storage, Usually, a typical MMAL consists of a number of (1) stations doing exactly the same operation on every job, (2) stations involving operations with different choices, and (3) stations offering operations that are not performed on every job, or that are performed on every job but with many options. For stations of the first type there is no sequencing problem at all. However, for the second type a set-up cost is incurred each time the operation switches from one choice to another. At the third type of stations, different models, requring different amounts and choices of assembly work, creates an uneven flow of work along the line and variations in the work load at these stations. When a subsequence of jobs requires more work load than the station can handle, it is necessary to help the operations at the station or to complete the work elsewhere. Therefore, a schedule which minimize the sum of set-up cost and utility work cost is desired. So this study has developed Fixed Random Ordering Rule (FROR), Fixed Ascending Ordering Rule (FAOR), Fixed Descending Ordering Rule, and Extended NHR (ENHR). ENHR is to choose optimal color ordering of each batch with NHR, and to decide job sequence of the batch with it, too. As the result of experiments, ENHR was the best heuristic algorithm. NHR is a new heuristic rule in which only the minimum addition of violations from both partial sequence and unassigned sequence at every branch could be considered. And this is a heuristic sequencing rule for the third type of stations at MMAL. This study developed one more heuristic algorithm to test the performance of NHR, which is named as Practical Heuristic Rule (PHR).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1284-1290
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• 2004

In market-basket analysis, weighted association rule(WAR) discovery can mine the rules which include more beneficial information by reflecting item importance for special products. However, when items are divided into more than one group and item importance for each group must be measured by different measurement or separately, we cannot directly apply traditional weighted association rule discovery. To solve this problem, we propose a novel methodology to discovery the weighted association rule in this paper In this methodology, the items should be first divided into sub-groups according to the properties of the items, and the item importance is defined or calculated only with the items enclosed to the sub-group. Our algorithm makes qualitative evaluation for network risk assessment possible by generating risk rule set for risk factor using network sorority data, and quantitative evaluation possible by calculating risk value using statistical factors such as weight applied in rule generation. And, It can be widely used for new model of more delicate analysis in market-basket database in which the data items are distinctly separated.