• Wind and Structures
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• v.9 no.1
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• pp.37-58
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• 2006

The paper describes a study about effects of upstream hills on design wind loads using two mathematical approaches: Computational Fluid Dynamics (CFD) and Artificial Neural Network (NN for short). For this purpose CFD and NN tools have been developed using an object-oriented approach and C++ programming language. The CFD tool consists of solving the Reynolds time-averaged Navier-Stokes equations and $k-{\varepsilon}$ turbulence model using body-fitted nearly-orthogonal coordinate system. Subsequently, design wind load parameters such as speed-up ratio values have been generated for a wide spectrum of two-dimensional hill geometries that includes isolated and multiple steep and shallow hills. Ground roughness effect has also been considered. Such CFD solutions, however, normally require among other things ample computational time, background knowledge and high-capacity hardware. To assist the enduser, an easier, faster and more inexpensive NN model trained with the CFD-generated data is proposed in this paper. Prior to using the CFD data for training purposes, extensive validation work has been carried out by comparing with boundary layer wind tunnel (BLWT) data. The CFD trained NN (CFD-NN) has produced speed-up ratio values for cases such as multiple hills that are not covered by wind design standards such as the Commentaries of the National Building Code of Canada (1995). The CFD-NN results compare well with BLWT data available in literature and the proposed approach requires fewer resources compared to running BLWT experiments.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.2
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• pp.16-21
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• 1993

A newly advanced method of characterizing large signal S-parameters of TR using the overall gain of normally operating TR is proposed based on the load pull method which gives the matching network only. Large signal S-parameters of TR are characterized from the circuit which consists of TR and 3dB Coupler Tuners at the input and output ports, and which is B class biased with 0dBminput signal. Amplifier can be designed to have 8.5dB gain at 770MHz using the calculated large signal S-parameters with the resulting gain of 8.786dB.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1706-1710
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• 2005

In a case of computer simulation used for the verification of pneumatic system performance one of the main problems is that various parameters can be used to describe flow characteristics of the system components. The Standard ISO 6358 offers two parameters: the sonic conductance C and the critical static pressure ratio b, but the parameters can not be directly utilised in an analysis of a pneumatic system. In the standard analysis there is applied the airflow coefficient ${\mu}$, but it is not presented in the vendors' catalogues. In the paper the numerical algorithm for calculation of the airflow coefficient ${\mu}$. (which is required for computer simulation) as a function of sonic conductance C and a critical pressure ratio b (recommended by the standard) is presented. Additionally, because of the iterative character of the described algorithm, an artificial neural network approach to solve the problem is proposed.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.1-6
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• 2010

Simulation analysis may be the essential means to either evaluate performance of systems or optimize system parameters for new design. Including many variations for design and implementation, MANET (Mobile Ad-hoc NETwork) is one target area of such an analysis. Since every node, however, included in the network has mobility, one MANET could be overlapped or merged with another one which use a different transport protocol. In order to communicate among nodes in this case, the new merged network should configure paths and addresses in advance. Configuring paths and addresses generates much overheads which ultimately cause delay in communicating data. Performance analysis is required to improve the data transport performance by minimizing overheads. This paper proposes a sound address auto-configuration method which is based on an on-demand manner and then presents modeling and performance analysis of the method. NS-2 simulation results verify that the proposed method can not only alleviate overheads, which are inevitably generated for address auto-configuration processes, and but also decentralize them in time.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.78-87
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• 2008

Received signal strength indicator (RSSI) is used as one of the ranging techniques to locate dynamic sensor nodes in wireless sensor network. Before it can be used for position estimation, RSSI values must be converted to distances using path loss model. These distances among sensor nodes are combined using trilateration method to find position. This paper presents an idea which attempts to integrate both path loss model and trilateration as one algorithm without going through RSSI-distance conversion. This means it is not simply formulas combination but a whole new model was developed. Several advantages were found after integration: it is able to reduce processing load, and ensure that all values do not exceed the maximum range of 16-bit signed or unsigned numbers due to antilog operation in path loss model. The results also show that this method is able to reduce estimation error while inaccurate environmental parameters are used for RSSI-distance conversion.

• IE interfaces
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• v.17 no.1
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• pp.121-127
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• 2004

In this paper, we present the base station(BS) processing capacity to provide the multimedia services in IMT-2000 wireless communication network. The IMT-2000 services are classified into two types, circuit and packet services, and user‘s environments such as indoor, pedestrian and vehicle are considered. Using the parameters of service economics and wireless technology, the user's reference traffic and BS‘s traffic are computed. The traffic is measured by throughput(kbps). Based on the measured traffic, the accommodating users in BS are also evaluated. In addition to, the BS design capacity is presented for varying the number of users and traffic distribution in different user's environments.

• Computers and Concrete
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• v.8 no.5
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• pp.491-510
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• 2011

Before deciding if structures exposed to high temperature are to be repaired or demolished, their final state should be carefully examined. Destructive and non-destructive testing methods are generally applied for this purpose. Compressive strength and color change in mortars are observed as a result of the effects of high temperature. In this study, ordinary and pozzolan-added mortar samples were produced using different aggregates, and exposed to 100, 200, 300, 600, 900 and $1200^{\circ}C$. The samples were divided into two groups and cooled to room temperature in water and air separately. Compression tests were carried out on these samples, and the color change was evaluated by the Munsell Color System. The relationships between the change in compressive strength and color of mortars were determined by using a multi-layered feed-forward Neural Network model trained with the back-propagation algorithm. The results showed that providing accurate estimates of compressive strength by using the color components and ultrasonic pulse velocity design parameters were possible using the approach adopted in this study.

• Steel and Composite Structures
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• v.9 no.5
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• pp.445-455
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• 2009

This study proposes a system identification technique for a fiber-reinforced polymer deck with neural networks. Neural networks are trained for system identification and the identified structure gives training data in return. This process is repeated until the identified parameters converge. Hence, the proposed algorithm is called an iterative neural network scheme. The proposed algorithm also relies on recent developments in the experimental design of the response surface method. The proposed strategy is verified with known systems and applied to a fiber-reinforced polymer bridge deck with experimental data.