• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1091-1102
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• 2014

The power analysis attack is a cryptanalytic technique to retrieve an user's secret key using the side-channel power leakage occurred during the execution of cryptographic algorithm embedded on a physical device. Especially, many power analysis attacks have targeted on an exponentiation algorithm which is composed of hundreds of squarings and multiplications and adopted in public key cryptosystem such as RSA. Recently, a new correlation power attack, which is tried when two modular multiplications have a same input, is proposed in order to recover secret key. In this paper, after reviewing the principle of side-channel attack based on input collisions in modular multiplications, we analyze the vulnerability of some exponentiation algorithms having regularity property. Furthermore, we present an improved exponentiation countermeasure to resist against the input collision-based CPA(Correlation Power Analysis) attack and existing side channel attacks and compare its security with other countermeasures.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.181-186
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• 2017

In recent years, robots have been actively used for children's creativity learning and play, but most robots have a stereotyped form and have a high dependency on the program, making it difficult to learn creativity and play. In order to compensate for these drawbacks, We have created a robot that can easily and reliably combine each other. The robot can memorize the desired operation and execute the memorized operation by using one button. Also, in case multiple modules are combined, pressing the button once on any module makes it possible to easily adjust the operation of all the combined modules. In order to verify the actual operation, two, three, and five modules are combined to demonstrate the usefulness of the proposed structure and algorithm by implementing a gobbling motion and a walking robot. It is required to study intelligent modular robots that can control over the Internet by supplementing the wireless connection method.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.661-666
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• 2009

Modular snake-like robots, which consist of series of modules, are robust for failure and have flexible locomotions for environment. However, they are difficult to control and few efficient and various locomotions are introduced yet. In this paper, GA based phase generation and trajectory generation approaches are implemented and compared for locomotion of snake-like robots and extended for analysis for selections of partial modules. In addition, modeling and simulation environments are implemented in Webots simulator and above GA based experiments for locomotion are executed for KMC snake-like robot.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.1
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• pp.136-142
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• 2001

In this paper, a method of canceling and suppressing differential mode crosstalk noise signals caused by non-uniform coupling between two transmission lines in UTP (unshielded twisted pair) modular jacks is discussed. Differential mode crosstalk noise signals in balanced transmission lines with UTP modular jacks were suppressed, by applying field cancellation techniques to this modular jack. To verify an effectiveness of the field cancellation techniques, 8 pin modular jacks were made, and the NEXT (Near End Crosstalk) losses were measured to prove its applicability by the network analyzer(HP8720C) at 100 Mb/s.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.6-11
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• 2003

Recently, the modular network was proposed in a way to keep the size of the neural network small. The modular network solves the problem by splitting it into sub-problems. In this aspect, fuzzy systems act in a similar way. However, in a fuzzy system, there must be an expert rule which separates the input space. To overcome this, fuzzy-neural network has been used. However, the number of fuzzy rules grows exponentially as the number of input variables grow. In this paper, we would like to solve the size problem of neural networks using modular network with the hierarchic structure. In the hierarchic structure, the output of precedent module affects only the THEN part of the rule. Finally, the rules become shorter being compared to the rule of fuzzy-neural system. Also, the relations between input and output could be understood more easily in the Proposed modular network and that makes design easier.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.769-771
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• 2002

임계 암호시스템은 현대 암호학에서 중요한 한 축을 이루는 암호학의 한 분야이다. 본 논문에서는 임계 암호시스템의 근간이 되는 비밀 분산(Secret Sharing)의 한 분야인 (k, n) threshold scheme에서 능동적 비밀 분산 (Proactive Secret Sharing)을 위한 공유(Share)갱신 방법을 개선한 새로운 공유 갱신 방법을 제안한다. 이전 방법은 각 참여자당 O(n$^2$)의 모듈라 멱승 연산을 수행하는데 비하여 제안 방법은 O(n)의 모듈라 멱승 연산만으로 공유갱신이 가능하다. 이와 함께 본 논문에서는 k ＜(1/2)n-1인 경우에 대하여 제안 방법의 안전함을 증명한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.796-798
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• 2004

기존의 번호판 인식 시스템에서는 대부분 카메라가 고정 상태에서 차량의 전면부를 찍어 영상을 획득하고, 이로부터 번호판을 추출하고 인식한다 그러나 본 연구에서는 기존 연구들과 달리 이동 중인 자동차에 카메라를 설치하여 움직이는 자동차의 영상을 획득하여 번호판을 추출하고 인식한다. 인식하고자 하는 영상이 잡음이나 왜곡 없이 깨끗하다면 인식 과정은 간단하게 수행될 것이다. 그러나, 실제로 얻어진 영상은 간단한 방법으로 인식하기에는 어려올 정도로 왜곡이나 변형이 심한 경우가 많다. 따라서 본 논문에서는 SVM 전단에 모듈라 신경망을 결합하여 인식하는 방법을 사용함으로써 잡음과 같은 변형에 덜 민감하도록 하고자 하였다. 실험결과, 제안하는 분류기를 이용한 방법이 번호판 인식에 우수한 성능을 보임을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.371-374
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• 2001

본 논문에서는 셀룰라 오토마타(Cellular Automata : CA)를 이용한 다항식 모듈라 멱승 알고리즘을 제안한다. 또한 이를 이용하여 공개키 암호 알고리즘인 EiGamal 알고리즘을 구현한다. 기존의 모듈라 멱승 알고리즘은 대부분 선형 귀환 시프트 레지스트(Linear Feedback Shift Register : LFSR)를 이용하여 구현하였다. 그러나 LFSR을 이용한 구조는 기저가 자주 변경되는 연산에 대하여 구현하기에 곤란한 단점을 가지고 있다. 본 논문에서 제안된 알고리즘은 CA의 병렬성과 높은 적응성을 이용함으로써 기저가 자주 변경되는 멱승 연산 알고리즘에 쉽게 적용할 수 있는 장점이 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.32-43
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• 2004

In this paper, a system which can effectively recognize the plate image extracted from camera set on a moving car is proposed. To extract car licence plate from moving vehicles, multiple candidates are maintained based on the strong vertical edges which are found in the region of car licence plate. A candidate region is selected among them based on the ratio of background and characters. We also make a comparative study of recognition performance between support vector machines and modular neural networks. The experimental results lead us to the conclusion that the former is superior to the latter. For a better recognition rate, a simple method combining the support vector machine with modular neural network where the output of the latter is used as the input of the former is suggested and evaluated. As we expected, the hybrid one shows the best result among those three methods we have mentioned.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.9-14
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• 2004

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.