• 한국관개배수논문집
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• 제18권2호
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• pp.87-100
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• 2011

This study is to delineate the watershed hydrological parameters such as area, slope, rain gauge weight, NRCS-CN and time of concentration (Tc) by using the Geographic Information Sytem (GIS) technique, and estimation of design flood for an ungauged watershed. Especially, we attempted to determine the Tc of ungauged watershed and develop simple program using the cell-based algorithm to calculates upstream or downstream flow time along a flow path for each cell. For a $19km^2$ watershed of tributary of Nakdong river (Seupmoon), the parameters including flow direction, flow accumulation, watershed boundary, stream network and Tc map were extracted from 30m Agreeburn DEM (Digital Elevation Model) and landcover map. And NRCS-CN was extracted from 30m landcover map and soil map. Design rainfall estimation for two rainfall gauge which are Sunsan and Jangcheon using FARD2006 that developed by National Institute for Disaster Prevention (NIDP). Using the parameters as input data of HEC-l model, the design flood was estimated by applying Clark unit hydrograph method. The results showed that the design flood of 50 year frequency of this study was $8m^3/sec$ less than that of the previous fundamental plan in 1994. The value difference came from the different application of watershed parameter, different rainfall distribution (Huff quartile vs. Mononobe) and critical durations. We could infer that the GIS-based parameter preparation is more reasonable than the previous hand-made extraction of watershed parameters.

• 한국통신학회논문지
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• 제24권10B호
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• pp.1832-1840
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• 1999

논문에서는 WDM 기술을 사용하는 완전 광전달망에서 장애를 복구할 수 있는 양 방향 BLSR/4 WDM/SHR 구조의 네트워크를 설계하였다. 제안한 네트워크는 전기적 기능이 없는 완전 광소자를 사용하여 높은 수준의 투명성을 제공하고, BLSR/4 SHR 구조를 사용함으로써 장애 복구를 효율적으로 수행할 수 있다. 또한, 제안한 BLSR/4 WDM/SHR의 여러 장애에 대한 생존성을 분석하기 위한 모델을 제시하였으며, 전파 시간, 처리 시간, 절체 시간의 파라미터를 사용하여 복구 성능을 분석하였다.

• 한국전기전자재료학회논문지
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• 제21권1호
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• pp.73-78
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• 2008

A resistor network model for the horizontal AlInGaN LED was investigated, The parameters of the proposed model are extracted from the test dies and $350{\mu}m$ LED, The center of the P-area is the optimal position of a P-electrode by the simulation using the model. Also the optimal chip size of the LED for the new target current was investigated, Comparing the simulation and fabrication result, the errors for the forward voltage and the light power are average 0,02 V, 8 % respectively, So the proposed resistor network model with the linear forward voltage approximation and the exponential light power model are useful in the simulation for the horizontal AlInGaN LED.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• 한국통신학회논문지
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• 제41권12호
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• pp.1925-1932
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• 2016

One of the major challenges in the design of wireless sensor network (WSN) is to reduce the energy consumption of sensor nodes for prolonging the network lifetime. In the sensor network, communication is the most energy consuming event. Therefore, most of the energy saving techniques conserve energy by adjusting different parameters of the trans-receiver. Among them, one of the promising methods is the transmission power control (TPC). In this paper, we investigated the effects of the link quality based TPC scheme employed to the IEEE 802.15.4 standard for energy saving. The simulation results demonstrated that the link quality based TPC scheme works effectively in conserving energy as compared to the conventional IEEE 802.15.4.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 Asia Navigation Conference
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• pp.176-183
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• 2006

A novel intelligent anti-collision decision-making supporting system is addressed in this paper. To obtain precise anti-collision information capability, an innovative neurofuzzy network is proposed and applied. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to train the parameters of the Fuzzy Inference System (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data are obtained by trial manoeuvre. This neurofuzzy network can be considered to be a self-learning system with the ability to learn new information adaptively without forgetting old knowledge. This supporting system can decrease ship operators' burden to deal with bridge data and help them to make a precise anti-collision decision.

• 대한기계학회논문집A
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• 제29권1호
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• pp.88-95
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• 2005

This paper presents a design scheme of model reference adaptive control incorporating a Neural Network for a pneumatic servo system. The parameters of discrete-time model of plant are estimated by using the recursive least square method. Neural Network is utilized in order to compensate the nonlinear nature of plant such as compressibility of air and frictions present in cylinder. The experiment of a trajectory tracking control using the proposed control scheme has been performed and its effectiveness has been proved by comparing with the results of a model reference adaptive control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• 산업기술연구
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• 제41권1호
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• pp.1-6
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• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.