• 응용통계연구
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• 제28권2호
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• pp.343-351
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• 2015

본 논문에서는 SRC-Stat 통계패키지를 이용하여 변량효과를 적합하는 방법에 대해서 소개하고자 한다. 본 패키지를 통하여 단변량 평균 뿐만 아나리 산포 및 분산에도 변량효과를 고려하는 이중 다단계 일반화 선형모형을 적합할 수 있다. 고정효과 및 변량효과의 추정치는 다단계 우도 방법을 이용하고 있으며, 실제 자료 적합을 통해 패키지의 사용법에 대해서 설명하고자 한다.

• 한국통신학회논문지
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• 제40권5호
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• pp.833-840
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• 2015

본 논문에서는 미리 알려진 임의의 다항식과 암호화된 다항식의 곱셈을 수행한 후, 해당 곱셈이 정당하게 수행되었음을 보이기 위해 증명자 (Prover)와 검증자 (Verifier)간의 다항식 상등성 영지식증명 (Zero-knowledge Proof) 프로토콜을 일반화할 수 있는 방법을 다룬다. 이를 위하여 다항식의 상등성을 증명하는 일반화된 프로토콜을 제시하고 랜덤오라클 (Random Oracle) 모델에서 안전성을 증명한다. 이러한 기법은 안전한 집합연산 기법을 포함하여 다항식에 기반한 다자간 연산기법 (Secure Multi-party Computation)에 적용될 수 있다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.448-462
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• 2021

온라인 커뮤니티에서 교환구조가 커뮤니티 결속력에 미치는 영향에 관한 연구는 매우 드물다. 더욱이 이 연구들은 서로 상충된 연구결과를 보고하고 있다. 본 연구는 이러한 연구결과를 재조명하기 위해 사회적 교환이론에 근거하여 커뮤니티 내 교환구조를 직접적 및 일반적 교환구조로 분류하고, 일반적 교환구조의 결정요인으로 지식의 분산도를 포함하였다. 그리고 상충된 연구결과를 설명하기 위해 교환혜택을 조절변수로 도입하였다. 본 연구는 네이버에서 활동하는 380개의 온라인 커뮤니티에서 데이터를 수집하여 분석하였다. 그 결과 지식의 분산도는 일반적 교환구조를 촉진하는 것으로 나타났다. 그리고 직접적, 일반적 교환구조 모두 커뮤니티 결속력에 긍정적 영향을 미치나, 이 관계는 교환혜택의 수준에 따라 조절되는 것으로 나타났다. 즉, 교환혜택이 높은 그룹에서는 일반적 교환구조가 직접적 교환구조보다 커뮤니티 결속력에 미치는 영향이 더 큰 반면, 교환혜택이 낮은 그룹에서는 두 교환구조의 영향력 사이에 통계적으로 유의미한 차이가 없는 것으로 나타났다.

• 지능정보연구
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• 제9권2호
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Journal of Information Processing Systems
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• 제11권2호
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• pp.184-204
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• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.7-12
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• 1993

This paper presents a generalized truncated least, squares adaptive algorithm and a two-stage design method. The proposed algorithm is directly derived from the normal equation of the generalized truncated least squares method (GTLSM). The special case of the GTLSM, the truncated least squares (TLS) adaptive algorithm, has a distinct features which includes the case of minimum steps estimator. This algorithm seemed to be best in the deterministic case. For real applications in the presence of disturbances, the GTLS adaptive algorithm is more effective. The two-stage design method proposed here combines the adaptive control system design with a conventional control design method and each can be treated independently. Using this method, the validity of the presented algorithms are examined by the simulation studies of an indirect adaptive control.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• 대한전기학회논문지:전력기술부문A
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• 제55권9호
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• pp.351-358
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• 2006

In this paper, DSPOCS(Digital Switchgear-Panel Operation and Control Solution) is designed, which is the intelligent inference based operation and control solution to obtain the safety and reliability of electric power supply in substation based on IED. DSPOCS is designed as a scheduled monitoring and control task and a real-time alarm inference task, and is interlinked with BRES(Bus Reconfiguration Expert System) in the required case. The intelligent alarm inference task consists of the alarm knowledge generation part and the real-time pattern matching part. The alarm knowledge generation part generates automatically alarm knowledge from DB saves it in alarm knowledge base. On the other hand, the pattern matching part inferences the real-time event by comparing the real-time event information furnished from IEDs of substation with the patterns of the saved alarm knowledge base.; Especially, alarm knowledge base includes the knowledge patterns related with fault alarm, the overload alarm and the diagnosis alarm. In order to design the database independently in substation structure, busbar is represented as a connectivity node which makes the more generalized graph theory possible. Finally, DSPOCS is implemented in MS Visual $C^{++}$, MFC, the effectiveness and accuracy of the design is verified by simulation study to the typical distribution substation.