• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.104-112
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• 2008

In modern war information system development makes battlefield materialize, and combat factors can maximize combat power exhibition as that apply synchronization. Information system is the core of combat power operation under NCW(Network centric Warfare). This paper proposed a new MOE(Measure of Effectiveness) that can assess the combat power synergistic effect of information system at the theater joint fire operation in NCW environment. This methodology applied the rule of Newton's second law $F=(m{\Delta}{\upsilon})/t{\Rightarrow}(M{\upsilon}I)/T$) Details factor in combat power evaluation is as following. (1) M : Network power; (2) v : Movement velocity; (3) I : Information superiority; (4) T : C2(command and control) time. We applied this methodology to the "JFOS-K(Joint Fire Operating System-Korea) in Joint Chief of Staff" in the real military affair section.

• 조명전기설비학회논문지
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• 제20권7호
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• pp.53-57
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• 2006

PID 제어기는 산업자동화 설비에 널리 쓰이고 있다. 하지만 시스템 특성이 간헐 또는 연속적으로 변화할 때에 정밀제어를 위한 새로운 매개변수 결정이 쉽지 않다. 이를 해결하기 위한 방법으로 본 논문에서는 PID 제어기와 같은 기능을 갖는 신경망이득 자동조절기를 제안 하였다. 시스템의 적절한 궤환제어 이득은 델타 학습규칙에 의해서 결정된다. 제안된 신경망이득 자동조절기의 기능은 유도 전동기의 속도제어 실험에 의해 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.46-49
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• 1998

In this paper, we introduce the notion of intelligent communication by introducing a novel intelligent receiver model. This receiver is continually evolving and learns and improves in performance as it compiles its experience over time. In digital communication context, in a typical training mode, it jearns the concept of "1" as is deteriorated by arbitrary (not necessarily additive as is typically assumed) disturbance and /or modulation. After learning "1", in test mode, it classifies the received signal "1" and "0" almost completely. The intelligent receiver as implemented is grounded on the recently introduced Stochastic Morphological Sampling Theorem(SMST), a distribution-free result which gives theoretical bounds on the sample complexity(training size) needed for the required performance parameters such as accuracy($\varepsilon$) and confidence($\delta$). Based on this theorem, we demonstrate --almost irrespective of channel and modulation model-- the number of samples needed to learn the concept of "1" is not too "large" and the resulting universal receiver structure, that corresponding to classical Nearest Neighbor rule in Pattern Recognition Theory, is trivial. We check the surprising efficiency and validity of this model through some simple simulations. and validity of this model through some simple simulations.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.579-582
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• 2011

가변 메카니즘은 키오스크를 이용하는 사용자의 키와 위치를 초음파 센서를 통하여 감지하고, 이를 통하여 적정한 높이와 키오스크의 방향을 조절한다. 본 논문에서는 가변형 키오스크의 높이와 방향 제어를 위하여 ADALINE-퍼지 논리 시스템 제어 방식을 제안한다. 제안한 방식의 유용성을 확인하기 위하여 가변형 키오스크 시스템이 사용자의 방향과 높이에 따른 제어 동작을 제대로 수행하는지 대하여 Widrow-Hoff 델타 규칙과 실험을 통하여 성능을 비교 검토 하였다.

• 조명전기설비학회논문지
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• 제12권3호
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• pp.124-131
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• 1998

본 논문에서는 건축물의 수배전설비에서 발생하는 경보를 신경 회로망과 고장 진단 지식베이스를 적용하여 감시의 효율을 향상시킬 수 있는 방안을 제시하였다. 제안된 경보 처리시스템은 역전파 알고리즘의 누가 역전파 알고리즘을 이용하여 다중 경보 발생에서 최소 경보요소를 추출하는데 사용하고 추출된 경보는 사전에 정의된 고장진단 지식베이스를 이용하여 셜비 유지보수 정보를 화면에 전개하도록 구성하였다. 제안된 감시기법의 유용 성을 확인하기 위하여 5가지의 가상 시나리오를 통해서 신경 회로망의 적용 가능성을 확인할 수 있었으며, 비전문가라도 설비의 유지관리 업무가 가능하도록 개발되었다.

• 한국농공학회지
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• 제36권2호
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• pp.88-95
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• 1994

This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration. Two neural networks were developed to forecast daily evapotranspiration of the rice crop with back-propagation and counter-propagation algorithm. The neural network trained by back-propagation algorithm with delta learning rule is a three-layer network with input, hidden, and output layers. The other network with counter-propagation algorithm is a four-layer network with input, normalizing, competitive, and output layers. Training neural networks was conducted using daily actual evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity, and pan evaporation. During the training, neural network parameters were calibrated. The trained networks were applied to a set of field data not used in the training. The created response of the back-propagation network was in good agreement with desired values and showed better performances than the counter-propagation network did. Evaluating the neural network performance indicates that the back-propagation neural network may be applied to the estimation of evapotranspiration of the rice crop. This study does not provide with a conclusive statement as to the ability of a neural network to evapotranspiration estimating. More detailed study is required for better understanding and evaluating the behavior of neural networks.

• 조명전기설비학회논문지
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• 제23권2호
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• pp.103-107
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• 2009

PI제어기는 발전기의 전압제어 시스템에 널리 쓰이고 있다. 하지만 발전 시스템의 특성이 연속적으로 변화할 때, 새로운 PI매개변수를 결정하는 것이 쉽지 않다. 이를 해결하기 위하여 본 논문에서는 발전기의 전압제어에 신경망자율 적응 제어를 이용하는 제어 방법을 제안하였다. 전압제어 시스템의 적절한 연속적인 궤환 제어 이득은 델타학습 규칙에 의해서 결정된다. 제안된 제어 방법의 기능은 직류 발전기 전압제어 실험에 의해 확인하였다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.390-390
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• 2008

In this paper, we have designed and fabricated optical waveguides based on the Mach-Zehnder Interferometer (MZI) for application to sensor. The evanecent-wave sensor based on the MZI principle has sufficiently high sensitivity to measure the change of the refractive index on surface of a waveguide. The waveguides were optimized at a wavelength of 1550 nm and fabricated according to the design rule of 0.45 delta%, which is the difference of refractive index between the core and clad. The fabrication of MZI optical waveguides was performed by a conventional Planar Lightwave Circuit (PLC) fabrication process. The fabricated MZI optical waveguide device was measured. According to the measurement result, the insertion loss of MZI optical waveguide device was below 3.5 dB and the polarization dependent loss (PDL) was within 0.1dB. In addition, we analyzed optical properties of MZI sensor according to the refractive index change of the sensor arm.

• 한국컴퓨터정보학회논문지
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• 제7권1호
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• pp.174-183
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• 2002

본 연구에서는 자동화된 데이터의 수집과 자동화된 제조환경하에서 수행될 수 있는 공정관리도의 패턴양상에 대하여 인공지능의 대표적인 기법인 인공신경망을 이용하여 각 패턴의 인식과 이의 검증, 그리고 이상패턴의 발생상황을 모니터링할 수 있는 지능형 공정관리 시스템을 개발하는데 중점을 두었다 개발된 패턴인식시스템을 이용하여 공정의 상태를 관리하는 작업자의 부담을 한층 덜어줄 수 있으며 작업자는 공정에 이상패턴이 발생하는 경우에 패턴인식시스템을 통하여 공정상태에 대한 정보를 전달받을 수 있어서 지속적인 품질개선활동을 수행할 수 있게 된다.

• International Journal of Korean Welding Society
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• 제1권1호
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.