• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1814-1820
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• 2018

State estimation of power systems has become vital in recent days of power operation and control. SCADA and EMS are intended for the state estimation and to communicate and monitor the systems which are operated at specified time. Although various methods are used we can achieve the better results by using PMU technique. On placing the PMU, operating time is reduced and making the performance reliable. In this paper, PMU placement is done in two ways. Those are 'optimal technique with pruning operation' and 'depth of unobservability' considering incomplete and complete observability of a network. By Depth of Unobservability Number of PMUs are reduced to attain Observability of the network. Proposed methods are tested on IEEE 14, 30, 57, SR-system and Sub systems (1, 2) with bus size of 270 and 444 buses. Along with achieving complete observability analysis, single PMU loss condition is also achieved.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.343-346
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• 2011

대부분의 퍼즐 게임에서 발생 할 수 있는 중요한 문제 중 하나는 교착상태 문제이다. 본 논문에서는 이러한 교착상태를 해결하기 위해 퍼즐 게임을 그래프 기법으로 나타낸 뒤, 이를 이용한 새로운 교착상태 발견 기법인 Cycle Detection을 제안한다. 기존의 기법들은 알고리즘을 수행하는데 너무 많은 시간이 걸리거나, 패턴에 대한 데이터베이스가 구축되어 있어야 하기 때문에 실시간으로 교착상태를 발견하기엔 문제가 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 탐색해야 하는 노드의 개수를 최대한 줄이는 Local search 기법과 Pruning 기법을 적용하여 퍼즐 게임을 플레이하는 동안 실시간으로 교착상태를 발견할 수 있는 기법을 제안한다. 본 기법은 성능을 평가하기 위해 실제 퍼즐게임 환경에서 알고리즘을 수행하고, 그 결과로 검색하는 노드의 개수와 검색 시간을 기존의 기법과 비교하여 성능향상을 확인하였다.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2096-2098
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• 2002

In this paper, the optimal evasive maneuver strategies for typical subsonic ASM(anti-ship missile) to reach its target ship with high survivability against CIWS(close in weapon system) are studied. The optimal evasive maneuver input is defined by the homing command optimizing the cost function which takes aiming errors of CIWS into account. The optimization problem for the effective evasive maneuver is formulated based on a simple missile dynamics model and a CIWS model. By means of solving the problem, a multiple hypotheses testing method is proposed. Since this method requires generation of too many hypotheses, the hypothesis-pruning technique is adopted. The solution shows that the optimal evasive maneuver is a bang-bane shaped command whose frequency is varied by the aimpoint determination strategy in CIWS.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1141-1151
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• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.160-166
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• 2007

When a mail was given to users, each user's response could be different according to his or her preference. This paper presents a solution for this situation by constructing a user preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or ron-spam in a meaningful way. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.298-302
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• 2008

주어진 작은 크기의 칩 내부에 많은 기능 (예: 멀티미디어, 음성/영상 등)을 작동시키기 위해서는 고집적(high-integration)의 회로가 구현되게 된다. 이러한 고집적 회로는 작동할 때 상당한 양의 전력 소모를 유발하게 되어 결국 배더리 수명을 단축시키는 상황을 가지게 한다. 더욱 심각한 상황은 고 밀도의 칩 안에서의 많은 전력 소모는 열의 발생을 더욱 가속화 시키게 되며, 결국 칩 작동의 신뢰성(reliability)을 상당히 잃게 만든다. 본 연구에서는 칩의 작동에 따른 열 발생으로 유발되는 칩의 온도 상승을 감지하는 센서회로 구현에 관한 것이다. FPGA 칩은 주 목적의 기능을 수행하는 회로들을 구현함과 동시에 추가적으로 열 감지 센서 회로를 구현할 자원을 FPGA가 제공을 해 주어야 하는데, 주목적의 회로 공간(즉, 자원) 사용으로 인해 열 센서 회로 구현 자원이 충분하지 않을 경우나 여러 지역에 사용 가능한 자원이 소규모로 흩어진 경우 등 센서 구현을 위한 자원 탐색 및 구현 가능성에 대해 점검하는 알고리즘이 필요하다. 본 연구는 이러한 알고리즘을 개발하여 그 효용성을 실험을 통해 보이고 있다. 제안한 알고리즘의 특징은 Branch-and-Bound에 기반을 두고 있으며, 알고리즘의 수행 시간 단축을 위한 효과적인 search tree pruning 기법을 제안하고 있다.

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.153-157
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• 1996

본 논문은 연속 분포 HMM을 사용한 핵심어 추출기법(Keyword Spotting)과 화자 인식에 기반한 음성 다이얼링 및 부서 안내에 관한 것이다. 개발된 시스템은 상대방의 이름, 직책, 존칭 등에 감탄사나 명령어 등이 혼합된 형태의 자연스런 음성 문장으로부터 다이얼링과 안내에 필요한 핵심어를 자동 추출하고 있다. 핵심 단어의 사용에는 자연성을 고려하여 문법적 제약을 최소한으로 두었으며, 각 단어 모델에 대해서는 음소의 갯수 더하기 $3{\sim}4$개의 상태 수와 3개 정도의 mixture component로써 좌우향 모델을, 묵음모델에 대해서는 2개 상태의 ergodic형 모델을 구성하였다. 인식에 있어서는 프레임 동기 One-Pass 비터비 알고리즘과 beam pruning을 채택하였으며, 인식에 사용된 어휘는 36개의 성명, 8개의 직위 및 존칭, 5개 정도의 호출어, 부탁을 나타내는 동사 및 그 활용이 10개 정도이다. 약 $3{\sim}6$개 정도의 단어로 구성된 문장을 실시간($1{\sim}3$초이내)에 인식하고, 약 98% 정도의 핵심어 인식 성능을 나타내고 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.