• 대한기계학회논문집B
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• 제25권10호
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• pp.1337-1345
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• 2001

Numerical investigation is made to study the effects of thermosolutal convection on the formation of macrosegregation in a Pb-Sn alloy solidification process in a two dimensional confined rectangluar mold. The basic equations are sovled using the Contrinum Model theory with the SIMPE algorithm during the solidification process. In addition, to track the liquid-solid interface with time variations, the moving boundary condition was adopted and moving irregular interface shapes were treated with the time-dependent, boundary-fitted coordinate system. As the temperature reduces from the liquidus to the solidus, the liquid concentration of Sn, the lighter constituent, increases. Then the buoyancy-driven flow due to temperature and liquid composition gradients occurs in the mushy region and forms the complicated macrosegregation maps. belated to this phenomena, effects on the macrosegregation formation depending on the cooling condition and gravity values are examined.

• 한국자동차공학회논문집
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• 제5권1호
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• pp.79-86
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• 1997

The need for an active exhaust moise controller has been growing as an reinforcement of exhaust noise regulation, high power output and improvement of average fuel economy. In this study, for development of this active exhaust noise controller, the implementation of a composition of realtime control algorithm for active exhaust noise control and the construction of simulator for realtime control algorithm for active exhaust noise control has been investigated. Also, in order to implement active exhaust noise control with this simulator, the feasibility model for control of vehicle exhaust noise control is suggested.

• 대한전기학회논문지
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• 제36권1호
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• pp.43-51
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• 1987

In this paper, a state-space model of the burnthrough point control system of an industrial sintering process is derived. The model is then used in designing a self-tuning controller which consists of the receding horizon control law and a least-squares prediction algorithm with covariance resetting. By applying this controller to POSCO IV sintering process, satisfactory experimental results have been obtained. This paper presents some of these real-time experimental results and analyzes the control performance through productivity, operation indices, quality, sintered material composition, etc. From these experimental results and simulation results, the validity of the model can be observed. Moreover, the properties of the controller, e.g. stability, steady-state error, are shown based on the model.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1998년도 추계학술대회 및 정기총회
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• pp.204-208
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• 1998

The fundamental issues to evaluate machine tool's performance through simulation pertain to the physical models of the machine tool itself and of process while the practical problems are related to the development of the modular software structure. It allows the composition of arbitrary machine/process models along with the development of programs to evaluate each state of machining process. Surface roughness is one of the fundamental factors to evaluate machining process and performance of machine tool, but it is not easy to evaluate surface roughness due to its tribological complexity. This paper presents an algorithm to calculate surface roughness considering cutting geometry, cutting parameters, and contact dynamics of cutting between tool and workpiece as well as tool wear in turning process. The designed virtual machining system can be used to evaluate the surface integrity of a turned surface during the design and process planning phase for the design for manufacturability analysis of the concurrent engineering.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.61.3-61
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• 2001

Field screening for pollutants has been a target of instrumental development for number of years. An electronic nose can be of substantial benefit to rapidly localize the spatial extent of a pollution or to find pollutant source. Although an electronic nose cannot separate complex composition into the whole spectrum of present volatile organic compound(VOC) in pollutants such as Gas chromatography(GC), qualitative and semi-quantitative chemical characterizations are rapidly available on site at a low cost using the optimum sensory system and pattern recognition algorithm based on neural network.

• 한국인터넷방송통신학회논문지
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• 제9권1호
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• pp.89-94
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• 2009

본 논문에서는 경로수 ${\zeta}$로 주어진 DCG(Directed Cyclic Graph)의 입출력간의 연관관계를 고효율디지털논리회로로 설계하는 알로리즘과 DCG의 각 노드들에 코드를 할당하는 알고리즘을 제안하였다. 본 논문에서는 기존 알고리즘의 문제점을 도출한 후, 다른 접근방법으로써 DCG의 경로수로 부터 행렬방정식을 유도한 후 이를 통해 DCG의 경로수에 따른 회로설계 알리즘을 제안하였으며, 설계된 회로와 함께 DCG의 특성을 만족하도록 노드들에 대한 코드를 할당하는 알고리즘을 제안하였다. 본 논문에서 제안한 고효율디지털논리회로설계 알고리즘은 기존의 알고리즘으로는 가능하지 않았던 경로수의 DCG에 대하여 회로설계가 가능하게 되었고, 보다 최적화된 디지털논리회로를 구현할 수 있음을 확인하였다. 본 논문에서 제안한 회로설계 알고리즘을 통해 임의의 자연수를 경로수로 갖는 DCG에 대한 설계가 가능하며, 입출력단자 수의 감소. 회로구성의 간략화, 연산속도의 향상과 비용감소 등의 잇점이 있고, 예제를 통해 본 논문에서 제안한 알고리즘의 적합성과 타당성을 검증하였다.

• 전자공학회논문지C
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• 제35C권6호
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• pp.20-29
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• 1998

본 논문에서는 경로수 1로 주어진 DCG(Directed Cyclic Graph)의 입출력간의 연관관계를 고속병렬다치논리회로로 설계하는 알고리즘들과 DCG의 각 노드들에 코드를 할당하는 알고리즘을 제안하였다. 본 논문에서는 기존의 Nakajima에 의해 제안된 알고리즘의 문제점을 도출한 후, 그의 기법과는 다른 접근방법으로써 DCG의 경로수로부터 행렬방정식을 유도한 후 이를 통해 DCG의 경로수에 따른 회로설계 알고리즘을 제안하였으며, 설계된 회로와 함께 DCG의 특성을 만족하도록 노드들에 대한 코드를 할당하는 알고리즘을 제안하였다. 본 논문에서 제안한 회로설계 알고리즘을 통해 Nakajima 등의 알고리즘으로는 회로설계가 가능하지 않았던 경로수의 DCG에 대하여 회로설계가 가능하게 되었고, 또한 Nakajima 등의 알고리즘을 통해 설계된 회로와 본 논문에서 제안한 알고리즘을 통해 설계한 회로를 비교하여 본 논문의 알고리즘이 보다 최적화된 회로를 구현할 수 있음을 증명하였다. 본 논문에서 제안한 회로설계 알고리즘을 통해 임의의 자연수를 경로수로 갖는 DCG에 대한 설계가 가능하며, 입출력단자 수의 감소, 회로구성의 간략화, 연산속도의 향상과 비용감소 등의 잇점이 있고, 예제를 통해 본 논문에서 제안한 알고리즘의 적합성과 타당성을 검증하였다.

• Spatial Information Research
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• 제18권5호
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• pp.107-117
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• 2010

본 연구는 최근 다수 도시개발사업들이 활발히 진행되고 있는 화성시 동부권을 사례대상지로하여 행정구역 단위 인구데이터를 격자형 인구분포자료로 변환한 후 인구유인을 유발할 것으로 예상되는 주요 도시계획관련 공간변수들을 GIS로 측정 대입하여 제네틱 알고리즘기법과 회귀분석기법 두 가지 방법으로 일종의 도시인구분포모형을 구축하였다. 두 가지 모형의 분석결과를 통해 도시환경 해석에 있어서의 두 기법의 성능상 특장점을 비교해 보았으며, 분석결과 GA기법은 변수 설명력에 관한 변별력에 있어 일반회귀분석보다 우월한 특징이 있음을 알 수 있었고 따라서 회귀분석과 병행할 경우 매우 직관적이며 보완적인 도시분석기법이 될 수 있음을 확인할 수 있었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.25-28
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• 2001

RBFN has some problem that because the basis function isn't orthogonal to each others the number of used basis function goes to big. In this reason, the Wavelet Neural Network which uses the orthogonal basis function in the hidden node appears. In this paper, we propose the composition method of the actual function in hidden layer with the scaling function which can represent the region by which the several wavelet can be represented. In this method, we can decrease the size of the network with the pure several wavelet function. In addition to, when we determine the parameters of the scaling function we can process rough approximation and then the network becomes more stable. The other wavelets can be determined by the global solutions which is suitable for the suggested problem using the genetic algorithm and also, we use the back-propagation algorithm in the learning of the weights. In this step, we approximate the target function with fine tuning level. The complex neural network suggested In this paper is a new structure and important simultaneously in the point of handling the determination problem in the wavelet initialization.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.