• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.33-39
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• 1995

One dimensional optimization problem is considered, we propose a method to find the global minimum of one-dimensional function with on gradient information but only the finite number of input-output samples. We construct a learning network which has a good learning capability and of which global maximum(or minimum) can be calculated with simple calculation. By teaching this network to approximate the given function with minimal samples, we can get the global minimum of the function. We verify this method using some typical esamples.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1568-1569
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• 2005

This paper presents a method to optimize train radio communication network for train control(CBTC) or multimedia services. To determine the optimized distance between wayside radio stations in a radio communication network constructed along railway, radio frequency allocation and hand-over capability is studied in terms of radio communication cell coverage and roaming feasibility.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.569-571
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• 2003

As a definition of NERC, Available Transfer Capability(ATC) is a measure of the transfer capability remaining in the physical transmission network for the future commercial activity. To calculate ATC, accurate and defensible TTC, CBM and TRM should be calculated in advance. In this paper, we propose a method to quantify TRM using probabilistic load flow based on the method of moment. Generation output, bus voltages, loads, and line outages are considered as complex random variables (CRV) to take into account for uncertainties related to the transmission network conditions. Probability Density Function (PDF) of line flow at the most limiting line is used to quantify TRM with the desired probabilistic margin. Suggested method is compared with the results from conventional CPF method and verified using 24 bus MRTS, and the suggested method based on PLF shows efficiency and flexibility for the quantification of TRM compared with the conventional method.

• 대한전기학회논문지
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• 제41권5호
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.

• Advances in biomechanics and applications
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• 제1권2호
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• pp.111-126
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• 2014

The microarchitecture of trabecular bone plays a significant role in mechanical strength due to its load-bearing capability. However, the complexity of trabecular microarchitecture hinders the evaluation of its morphological characteristics. We therefore propose a new classification method based on static multiscale theory and dynamic finite element method (FEM) analysis to visualize a three-dimensional (3D) trabecular network for investigating the influence of trabecular microarchitecture on load-bearing capability. This method is applied to human vertebral trabecular bone images obtained by micro-computed tomography (micro-CT) through which primary trabecular bone is successfully visualized and extracted from a highly complicated microarchitecture. The morphological features were then analyzed by viewing the percolation of load pathways in the primary trabecular bone by using the stress wave propagation method analyzed under impact loading. We demonstrate that the present method is effective for describing the morphology of trabecular bone and has the potential for morphometric measurement applications.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.646-648
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• 2010

A Sensor Node in Wireless Sensor Network has very limited resources such as processing capability, memory capacity, battery power, and communication capability. When the communication between any two sensor nodes are required to be secured, the symmetric key cryptography technique is used for its advantage over public key cryptography in terms of requirement of less resources. Keys are pre-distributed to each sensor node from a set of keys called key pool before deployment of sensors nodes. Combinatorial design helps in a great way to determine the way keys are drawn from the key pool for distributing to individual sensor nodes. We study various deterministic key predistribution techniques that are based on combinatorial design.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.865-870
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• 2003

A motion controller has been used variously in industry such as semiconductor manufacture equipment, industrial robot, assembly/conveyor line applications and CNC equipment. There are several types of controller in motion control. One of these is a PC-based motion controller such as PCI or ISA, and another is stand-alone motion controller. The PC bus-based motion controller is popular because of improving bus architectures and GUI (Graphic User Interface) that offer convenience of use to user. There are some problems in this. The PC bus-based solution allows for only one of the form factors, so it has a poor flexibility. The overall system package size is bigger than other motion control system. And also, additional axes of control require additional slot, however the number of slots is limited. Furthermore, unwieldy and many wirings come to connect plants or I/O. The stand-alone motion controller has also this limit of axes of control and wiring problems. To resolve these problems, controller must have capability of operating as stand-alone devices that resides outside the computer and it needs network capability to communicate to each motion device. In this paper, a network-based stand-alone motion system is proposed. This system integrates PC and motion controller into one stand-alone motion system, and uses CAN (Controller Area Network) as network protocol. Single board computer that is type of 3.5" FDD form factor is used to reduce the system size and cost. It works with Windows XP Embedded as operating system. This motion system operates by itself or serves as master motion controller that communicates to slave motion controller. The Slave motion controllers can easily connect to master motion system through CAN-network.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권3호
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• pp.127-132
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• 2016

인공신경망을 압축에 적용하여 더 높은 압축 성능을 보이기 위한 알고리즘들이 몇 가지 연구되어 있다. 그러나 그동안 이러한 알고리즘들은 한정된 계산 능력의 하드웨어를 가지고 있기에 작은 크기의 신경망을 사용할 수밖에 없었으며 적용하는 대상 역시 실제로 사용하기에는 너무 작은 크기의 파일들이었다. 본 논문에서는 GPGPU의 계산능력을 신경망 학습에 이용하여 만든 텍스트 문맥 기반 문자 등장 확률 예측기와 함께 허프만 부호화의 성능을 높일 수 있는 변환 방법을 제시한다. 앞먹임 신경망과 GRU 회귀 신경망에 대해 실험을 수행하였으며, 회귀 신경망 모델은 앞먹임 신경망에 비해 뛰어난 예측 성공률과 압축률을 보였다.

• 정보보호학회논문지
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• 제17권3호
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• pp.17-25
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• 2007

서명권한의 위임은 다양한 환경에서 요구되는 암호학적 서비스이다. Mambo 등은 서명권한 위임에 대한 해결방법으로 프록시 서명기법을 제안하였다. 프록시 서명기법이 제안된 후, 한 명의 서명자가 자신의 권한을 한 사람의 프록시 서명자에게 위임하는 기본적인 형태의 위임이 아니라 보다 일반적인 위임 구조를 제공하기 위한 프록시 서명기법들이 제안되었다. 모든 구성 가능한 위임 구조를 포괄할 수 있는 개념으로 위임 네트워크가 Aura에 의해 제안되었고, 이후 Chow 등은 ID기반의 위임 네트워크를 제안하였다. E는 위임 네트워크에서 발생하는 위임의 개수라고 하고 N은 사용자의 개수라고 하자. 계산 복잡도의 관점에서 Chow 등의 위임 네트워크는 E번의 페어링 연산과 N번의 스칼라 곱셈 연산이 수행된다. 본 논문에서는 E번의 페어링 연산만으로 Chow 등이 제안한 것과 동일하게 동작하는 위임 네트워크를 제안한다. 또한 제안하는 위임 네트워크의 구성을 변형함으로써 N번의 페어링 연산이 요구되는 위임 네트워크를 구성한다.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.49-57
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• 2022

최근 인공지능의 다양한 활용은 기계학습의 딥 인공신경망 구조를 통해 가능해졌으며 인간과 같은 능력을 보여주고 있다. 불행하게도 딥 구조의 인공신경망은 아직 정확한 해석이 이루어지고 있지 못하고 있다. 이러한 부분은 인공지능에 대한 불안감과 거부감으로 작용하고 있다. 우리는 이러한 문제 중에서 인공신경망의 능력 부분을 해결한다. 인공신경망 구조의 크기를 계산하고, 그 인공신경망이 처리할 수 있는 데이터의 크기를 계산해 본다. 계산의 방법은 수학에서 쓰이는 군의 방법을 사용하여 데이터와 인공신경망의 크기를 군의 구조와 크기를 알 수 있는 Order를 이용하여 계산한다. 이를 통하여 인공신경망의 능력을 알 수 있으며, 인공지능에 대한 불안감을 해소할 수 있다. 수치적 실험을 통하여 데이터의 크기와 딥 인공신경망을 계산하고 이를 검증한다.