• 한국통신학회논문지
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• 제15권5호
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• pp.434-445
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• 1990

OSI 모델에 입각한 기능확인 위주의 종래의 단순한 프로토콜 시험기법은 성능시험, 적합성시험, 상호접속시험으로 세분화되고 대상 프로토콜도 일대일, 일대다수(Point-to -multipoint)로 확장되었다. 본 논문에서는 시험항목 생성부분과 시험결과 분석기능을 공통 플랫폼으로 하고 시험 대상 프로토콜의 성격과 시험목적에 따라서 모듈화된 시험수행기부분을 선택적으로 변경하는 다기능 프로토콜 시험시스템을 제시하였다. 본 논문은 다기능 시험시스템 설계에 입각한 예로서 망루팅 프로토콜 시험시스템, 트랜스포트 프로토롤 시험시스템의 구조를 보인다.

• 반도체디스플레이기술학회지
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• 제18권3호
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• pp.42-46
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• 2019

In this paper, we introduce the novel idea to improve parallel test efficiency of semiconductor test. The idea includes the test interface card consisting of NoC structure able to transmitting test data regardless of ATE speed. We called the scheme "Multi-Clock" mode. In the proposed mode, because NoC can spread over the test data in various rates, many semiconductors are tested in the same time. We confirm the proposed idea will be promising through a FPGA board test and it is important to find a saturation point of the Multi-Clock mode due to the number of test chips and ATE channels.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.82-87
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• 2020

In this paper, we introduce the novel idea to improve the B/W usage efficiency of on-chip networks used for TAM to test multiple DRAMs. In order to avoid the local bottleneck of test packets caused by an ATE, we make test patterns using microcode-based instructions within ATE and adopt a test bus to transmit test responses from DRAM DFT (Design for Testability) called Test Generator (TG) to ATE. The proposed test platform will contribute to increasing the test economics of memory IC industry.

• ETRI Journal
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• 제29권3호
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• pp.371-380
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• 2007

This paper proposes a new automatic compensation network (ACN) for a system-on-chip (SoC) transceiver. We built a 5 GHz low noise amplifier (LNA) with an on-chip ACN using 0.18 ${\mu}m$ SiGe technology. This network is extremely useful for today's radio frequency (RF) integrated circuit devices in a complete RF transceiver environment. The network comprises an RF design-for-testability (DFT) circuit, capacitor mirror banks, and a digital signal processor. The RF DFT circuit consists of a test amplifier and RF peak detectors. The RF DFT circuit helps the network to provide DC output voltages, which makes the compensation network automatic. The proposed technique utilizes output DC voltage measurements and these measured values are translated into the LNA specifications such as input impedance, gain, and noise figure using the developed mathematical equations. The ACN automatically adjusts the performance of the 5 GHz LNA with the processor in the SoC transceiver when the LNA goes out of the normal range of operation. The ACN compensates abnormal operation due to unusual thermal variation or unusual process variation. The ACN is simple, inexpensive and suitable for a complete RF transceiver environment.

• Computers and Concrete
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• 제25권6호
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• pp.551-563
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• 2020

This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.

• 정보처리학회논문지D
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• 제14D권6호
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• pp.649-656
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• 2007

오늘날 차량네트워크(CAN)는 전기적 강인, 저가격과 접근지연 때문에 분산 임베디드 시스템에서 널리 사용되는 버스형 필드이다. 그러나 버스토폴로지에서 발생하는 의존적인 제한 때문에 차량네트워크가 어플리케이션상에서 안전적으로 사용되는지는 논쟁되어왔다. 그래서 차량네트워크(CAN) 디자인 단계 동안 데이터 버스의 부하와 최대 지연, 경쟁 우선순위와 같은 네트워크의 성능을 분석하는 것이 중요하게 되었다. 이 논문은 차량네트워크의 성능을 평가하기 위해 사용된 시뮬레이션 알고리즘과 고장 기법 기술을 적용을 소개한다. 이는 차량네트워크의 어떤 산만한 구현의 줄임과 시스템의 신뢰성을 향상 시켜 줄 것이다.

• International Journal of Concrete Structures and Materials
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• 제1권1호
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• Journal of Positioning, Navigation, and Timing
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• 제7권1호
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• pp.43-52
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• 2018

In recent years, the need of high accuracy navigation for vehicles has increased due to the development of autonomous driving vehicles and increase in land transportation convenience. This study is performed for vehicle users to achieve a performance of centimeter-level positioning accuracy by utilizing Compact Network Real-time Kinematic (RTK) that is applicable as a national-level infrastructure. To this end, medium-baseline RTK was implemented in real time to estimate accurate integer ambiguities between reference stations for reliable generation of Network RTK correction using the linear combination of carrier-phase observations and L1/L2 pseudo-range measurements. The residual tropospheric error was estimated in real time to improve the accuracy of double-differenced integer ambiguity resolution between network configuration reference stations that have at least 30 km or longer baseline distance. In addition, C++ based software was developed to enable real-time generation and broadcasting of Compact Network RTK correction information by utilizing an accurately estimated double-differenced integer ambiguity values. As a result, the horizontal and vertical 95% accuracy was 2.5cm and 5.2cm, respectively, without performance degradation due to user's position change within the network.

• 한국항해항만학회지
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• 제28권5호
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• pp.435-441
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• 2004

본 논문은 비선형성을 많이 내포하고 있어 수학적으로 모델링 하기 어려운 선박용 안정화 위성 안테나 시스템을 모델링하기 위해서, 신경 회로망의 오차 및 응답시간을 최소로 하는 최적 구조 신경 회로망 모델을 도출하고 이를 적용하고자 한다. 오차와 응답시간을 최소화하기 위해 유전알고리즘을 이용하여 신경 회로망 구조를 설계하였다. 안테나 시스템으로부터 얻어진 입출력 데이터에 거하여 본 논문에서 제안한 식별기를 이용하여 안테나 시스템을 식별하였으며, 실제 선박의 운동 성분에 대해서도 시스템을 잘 표현할 수 있는 최적 구조 신경 회로 기반 시스템 식별기를 얻을 수 있었다. 실제 실험을 통해서, 최적 신경회로망 구조가 안테나 시스템 식별에 효과적인 것을 알 수 있었다.

• 융합보안논문지
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• 제5권2호
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• pp.1-8
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• 2005

최근 새로운 공격기법에 대한 대응방법의 하나로 네트워크 상황 즉, 네트워크 사용량을 분석을 통한 외부 공격 예방기법이 연구되고 있다. 이를 위한 네트워크 분석을 데이터 마이닝 기법을 통하여 네트워크 이벤트에 대한 연관 규칙을 주어 외부뿐만 아니라 내부 네트워크를 분석할 수 있는 기법이 제안되었다. 대표적인 데이터 마이닝 알고리즘인 Apriori 알고리즘을 이용한 네트워크 트래픽 분석은 과도한 CPU 사용시간과 메모리 요구로 인하여 효율성이 떨어진다. 본 논문에서는 이를 해결하기 위해서 새로운 연관 규칙 알고리즘을 제시하고 이를 이용하여 네트워크 이벤트 감사 모듈을 개발하였다. 새로운 알고리즘을 적용한 결과, Apriori 알고리즘을 적용한 시스템에 비해 CPU 사용시간과 메모리의 사용량에 있어 큰 향상을 보였다.