• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.267-269
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• 2009

Incomplete knowledge instances may be registered from misidentifications of sensors, such as vision sensor. In previous study, we proposed robust object instance registration method to robot centered knowledge framework to guarantee the consistency of the registered knowledge. In real environment, a persistent update is necessary due to the objects can be moved dynamically. In this paper, we propose the way to update robot knowledge continually using the registration method. Our experiment in this paper shows that sound and complete knowledge can be registered and updated by the proposed method, even under imperfect sensing data.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.349-350
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• 2008

지능 로봇은 표현 가능한 사물, 공간을 모델링하기 위해 주변 환경을 인지하고, 자신이 수행할 수 있는 행동을 결합하여 임무를 수행하게 된다. 이를 위해 온톨로지를 사용하여 사물, 공간, 상황 및 행동을 표현하고 특정 임무 수행을 위한 자바 기반 Rule을 통해 다양한 추론 방법을 제공하는 로봇 지식 체계를 사용하였다. 사용된 로봇 지식 체계는 생성되는 인스턴스가 자료의 클래스와 속성 값이 일관성 있고 다른 자료와 모순되지 않음을 보장해 준다. 이러한 로봇 지식 체계를 효율적으로 사용하기 위해서는 완전한 온톨로지 인스턴스의 생성이 밑받침 되어야 한다. 하지만 실제 환경에서 로봇이 Vision Sensor를 통해 사물을 인식할 때 False Positive False Negative와 같은 인식 오류를 발생시키는 문제점이 있다. 이를 보완 하기 위해 본 논문에서는 물체와 물체간의 Spatial Relation, Temporal Relation과 각 물체마다의 인식률 및 속성을 고려하여 물체 인식 오류에서도 안정적으로 인스턴스 관리를 가능하게 하는 Rule 기반의 일식오류 검출 필터를 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.591-599
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• 2023

Due to the recent attack by Castryck-Decru, the private key of SIDH can be recovered in polynomial time so several methods have been proposed to prevent the attack. Among them, M-SIDH proposed by Fouotsa et al, counteracts the attack by masking the torsion point information during the key exchange. In this paper, we implement M-SIDH and evaluate its performance. To the best of our knowledge, this is the first implementation of M-SIDH in C language. Toward that end, we propose a method to select parameters for M-SIDH instantiation and propose a 1024-bit prime for implementation. We implemented the square-root Velu formula over the extension field for further optimization. As a result, 1129 ms is required for a key exchange in the case of MSIDH-1024, providing the classic 64-bit security level.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.17-35
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• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.