• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.454-457
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• 2005

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. And calendar based on temporal association rules proposes the discovery of association rules along with their temporal patterns in terms of calendar schemas, but this approach is also adopt an Apriori-like candidate set generation. In this paper, we propose an efficient temporal frequent pattern mining using TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (1) this method separates many partitions by according to maximum size domain and only scans the transaction once for reducing the I/O cost. (2) This method maintains all of transactions using FP-trees. (3) We only have the FP-trees of I-star pattern and other star pattern nodes only link them step by step for efficient mining and the saving memory. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the Apriori algorithm and also faster than calendar based on temporal frequent pattern mining methods.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.52-53
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• 2006

현재 국내 배전자동화 시스템의 고장구간을 최소한으로 하는 불필요 정전구간 복구시스템은 고장발생 시 중앙에서 고장구간을 판단하고 Fuzzy Rule을 이용한 복구해를 통하여 계통의 Device들을 운영자가 직접 제어하는 방식이다. 이러한 방식은 원격으로 제어가 가능하며 운영자가 직접 상황에 따라 적절한 판단을 하여 신뢰성이 크다는 장점이 있지만, 운영자의 실수에 의해서 계통에 문제가 발생할 가능성도 있고, 정전복구시간 면에서 볼 때 오퍼레이터의 실수에 의해 생기는 복구지연시간은 소비자들에게는 경제적 손실 및 불편을 초래할 수 있다. 따라서 이러한 운영자의 실수를 미연에 방지하고 정전복구 시간 또한 가급적 줄일 수 있는 방안을 마련하는 것이 필요하다. 본 연구에서 제안하는 Multi-Agent 기반의 분산형 정전복구 시스템은 중앙의 제어를 통하지 않고 계통 내의 Device들이 Agent 개념의 지능을 가지고 상호 정보교환을 통하여 고장구간을 판단하고 정전을 복구하는 시스템으로 현재 배전자동화 시스템의 복구시간을 1분 이내로 단축시킬 수 있는 시스템을 제안하였다. 간단한 배전계통을 구성하고 전력계통의 현재 통신망중 유/무선의 대표적인 LAN 및 CDMA 통신을 이용한 사례연구를 통해 제안한 시스템에서의 정전구간 1분 이내의 복구시간을 입증하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.35-36
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• 2012

The generation of fuzzy rules is inevitable in order to construct fuzzy modeling and in general, has the problem that the number of rules increases exponentially with increasing dimension. To solve this problem, we introduce the system that generate the fuzzy rules and make a inference based on FCM clustering algorithm that partition the input space in the scatter form. The parameters in the premise part of the fuzzy rules is determined as membership matrix by the FCM clustering algorithm and the consequence part of the fuzzy rules is are expressed as a polynomial function. Proposed model evaluated using the numerical data.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.951-957
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• 2010

An optimal path generation method considering the safety of UGV(Unmanned Ground Vehicle) is proposed and demonstrated through examples. Among various functions of UGV, real-time obstacle avoidance is a key issue to realize realistic scenario in FCS(Future Combat Systems). A two-dimensional narrow corridor environment is considered as a test field. For each step of UGV movement, two objectives are considered: One is to minimize the distance to the target and the other to maximize the distance to the nearest point of an obstacle. A weighted objective function is used in the optimization problem. Equality and inequality constraints are taken to secure the UGV's dynamics and safety. The weighting factors are controlled by a fuzzy controller which is constructed by a fuzzy rule set and membership functions. Simulations are performed for two cases. First the weighting factors are considered as constant values to understand the characteristics of the corresponding solutions and then as variables that are adjusted by the fuzzy controller. The results are satisfactory for realistic situations considered. The proposed optimal path generation with the fuzzy control is expected to be well applicable to real environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1284-1297
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• 2019

The network environment is presently becoming very increased. Accordingly, the study of traffic classification for network management is becoming difficult. Automatic signature extraction system is a hot topic in the field of traffic classification research. However, existing automatic payload signature generation systems suffer problems such as semi-automatic system, generating of disposable signatures, generating of false-positive signatures and signatures are not kept up to date. Therefore, we provide a fully automatic signature update system that automatically performs all the processes, such as traffic collection, signature generation, signature management and signature verification. The step of traffic collection automatically collects ground-truth traffic through the traffic measurement agent (TMA) and traffic management server (TMS). The step of signature management removes unnecessary signatures. The step of signature generation generates new signatures. Finally, the step of signature verification removes the false-positive signatures. The proposed system can solve the problems of existing systems. The result of this system to a campus network showed that, in the case of four applications, high recall values and low false-positive rates can be maintained.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.921-934
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• 2014

The purpose of the study is to derive a long term reservoir operation method that is easy to understand and apply to practical use for dam operators. The zone based operation rule is a simple method to make operation decisions by criteria corresponding to storage zones. The reservoir storage levels dividing a reservoir, however, must be determined by some methods. We developed a reservoir operation model based on the zone based operation rule and the shuffled complex evolution algorithm (SCE-UA) was used to determine storage levels for zone division. The model was applied to Angat Dam in the Philippines that has trouble in water supply due to imbalance between supply and demand. We derived a zone based operation rule for Angat Dam and applied it to the reservoir simulation of Angat Dam using the historical inflow. The simulation results showed water supply deficit and power generation were improved by 34.5% and 21.2%, respectively, when compared with the historical records. The current study results may be used to derive a long term reservoir operation rule.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1095-1105
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• 2013

This paper proposes the RDMS(Rough Set Theory based Disease Monitoring System) which efficiently manages diseases in Healthcare System. The RDMS is made up of DCM(Data Collection Module), RDRGM(RST based Disease Rules Generation Module), and HMM(Healthcare Monitoring Module). The DCM collects bio-metric informations from bio sensor of patient and stores it in RDMS DB according to the processing procedure of data. The RDRGM generates disease rules using the core of RST and the support of attributes. The HMM predicts a patient's disease by analyzing not only the risk quotient but also that of complications on the patient's disease by using the collected patient's information by DCM and transfers a visualized patient's information to a patient, a family doctor, etc according to a patient's risk quotient. Also the HMM predicts the patient's disease by comparing and analyzing a patient's medical information, a current patient's health condition, and a patient's family history according to the rules generated by RDRGM and can provide the Patient-Customized Medical Service and the medical information with the prediction result rapidly and reliably.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.179-185
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• 2012

This paper proposes a design of RSIDS(RST and SVM based Intrusion Detection System) using RST(Rough Set Theory) and SVM(Support Vector Machine) algorithm. The RSIDS consists of PrePro(PreProcessing) module, RRG(RST based Rule Generation) module, and SAD(SVM based Attack Detection) module. The PrePro module changes the collected information to the data format of RSIDS. The RRG module analyzes attack data, generates the rules of attacks, extracts attack information from the massive data by using these rules, and transfers the extracted attack information to the SAD module. The SAD module detects the attacks by using it, which the SAD module notifies to a manager. Therefore, compared to the existing SVM, the RSIDS improved average ADR(Attack Detection Ratio) from 77.71% to 85.28%, and reduced average FPR(False Positive ratio) from 13.25% to 9.87%. Thus, the RSIDS is estimated to have been improved, compared to the existing SVM.