• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.96-99
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• 2000

We presents a BGA(Ball Grid Array) package for RF circuit modules and extracted its electrical parameters. We constructed a BGA package of ITS(Intelligent Transportation System) RF module and examined electrical parameters with a HP5475A TDR(Time Domain Reflectometry) equipment and compared its electrical parasitic parameters with PCB RF circuits. With a BGA substrate of 3 $\times$ 3 input and output terminals, we have found that self capacitance of BGA solder ball is 68.6fF, self inductance 146pH, mutual capacitance 10.9fF and mutual inductance 16.9pH. S parameter measurement with a HP4396B Network Analyzer showed the resonance frequency of 1.55㎓ and the loss of 0.26dB. Thus, we may improve electrical performance when we use BGA package structures in the design of RF circuit modules.

• Transactions of Materials Processing
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• v.24 no.3
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• pp.167-175
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• 2015

In the current study, the design methodology has been developed to produce shoe mold for a suitable walking shoes of the general Korean using ANN (Artificial Neural Network). To design the suitable and comfortable shoes for the Korean, the shapes of foots were measured for 513 people. In this research, the foot length, breadth and ankle were considered as design parameters. In order to find the optimal foot shape for the average value of design parameters, the average value of design parameters and the other measurements were used as input and output to the ANN. After training, the various foot measurements were predicted by ANN. Base on the ANN results, the walking shoes were manufactured by considering these measurements and designing a shoe mold. From the results, the proposed method could give a more systematic and feasible means for manufacturing walking shoes with greater usefulness and better generality.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.790-803
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• 2019

The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1483-1503
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• 2011

To advance the effectiveness of the current Hanwoo improvement system, we developed a general simulation that compared a series of breeding schemes under realistic user circumstances. We call this system the Integrated General Model (IGM) and it allows users to control the breeding schemes and selection methods by manipulating the input parameters. The Current Hanwoo Performance and Progeny Test (CHPPT) scheme was simulated with a Modified Hanwoo Performance and Progeny Test (MHPPT) scheme using a Hanwoo Breeding Farm cow population of the Livestock Improvement Main Center (LOMC) of the National Agricultural Cooperatives Federation (NACF). To compare the two schemes, a new method, the Simple Hanwoo Performance Test (SHPT), which uses ultrasound technology for measuring the carcass traits of live animals, was developed. These three models, including the CHPPT, incorporated three types of selection criteria: phenotype (PH), true breeding value (TBV), and estimated breeding value (EBV). The simulation was scheduled to mimic an actual Hanwoo breeding program; thus, the simulation was run to include the years 1983-2020 for each breeding method and was replicated 10 times. The parameters for simulation were derived from the literature. Approximately 642,000 animals were simulated per replication for the CHPPT scheme; 129,000 animals were simulated for the MHPPT scheme and 112,000 animals for the SHPT scheme. Throughout the 38-year simulation, all estimated parameters of each simulated population, regardless of population size, showed results similar to the input parameters. The deviations between input and output values for the parameters in the large populations were statistically acceptable. In this study, we integrated three simulated models, including the CHPPT, in an attempt to achieve the greatest genetic gains within major economic traits including body weight at 12 months of age (BW12), body weight at 24 months of age (BW24), average daily gain from 6 to 12 months (ADG), carcass weight (CWT), carcass longissimus muscle area (CLMA), carcass marbling score (CMS), ultrasound scanned longissimus muscle area (ULMA), and ultrasound scanned marbling score (UMS).

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.

• KIEAE Journal
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• v.7 no.4
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• pp.147-152
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• 2007

Numerous non-destructive tests(NDT) to assess the safety of real structures have been developed. System identification(SI) techniques using dynamic responses and behaviors of structural systems become an outstanding issue of researchers. However the conventional SI techniques are identified to be non-practical to the complex and tall buildings, due to limitation of the availability of an accurate data that is magnitude or location of external loads. In most SI approaches, the information on input loading and output responses must be known. In many cases, measuring the input information may take most of the resources, and it is very difficult to accurately measure the input information during actual vibrations of practical importance, e.g., earthquakes, winds, micro seismic tremors, and mechanical vibration. However, the desirability and application potential of SI to real structures could be highly improved if an algorithm is available that can estimate structural parameters based on the response data alone without the input information. Thus a technique to estimate structural properties of building without input measurement data and using limited response is essential in structural health monitoring. In this study, shaking table tests on three-story plane frame steel structures were performed. Out-put only model analysis on the measured data was performed, and the dynamic properties were inverse analyzed using least square method in time domain. In results damage detection was performed in each member level, which was performed at story level in conventional SI techniques of frequency domain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.181-189
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• 2013

In this paper, we introduce the fuzzy neural network based on the individual input space to design the pattern recognizer. The proposed networks configure the network by individually dividing each input space. The premise part of the networks is independently composed of the fuzzy partition of individual input spaces and the consequence part of the networks is represented by polynomial functions. The learning of fuzzy neural networks is realized by adjusting connection weights of the neurons in the consequent part of the fuzzy rules and it follows a back-propagation algorithm. In addition, in order to optimize the parameters of the proposed network, we use real-coded genetic algorithms. Finally, we design the optimized pattern recognizer using the experimental data for pattern recognition.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.634-640
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• 2015

This paper deals with the design of a microstrip line feed toppled T shaped microstrip patch antenna that gives dualband characteristics at 4 GHz and 6.73 GHz respectively. The simulation of proposed antenna geometry has been performed using method of moment based IE3D simulation software. A radial basis function neural network (RBFNN) is used for the estimation of bandwidth for dualband at 4 GHz and 6.73 GHz respectively. In RBFNN model, antenna parameters such as dielectric constant, height of substrate, and width are used as input and bandwidth of first and second band is considered as output of the network. To validate the RBFNN output, an antenna has been physically fabricated on glass epoxy substrate. The fabricated antenna can be utilized in S and C bands applications. RBFNN results are found in close agreement with simulated and experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Animal cells and systems
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• v.16 no.2
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• pp.121-126
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• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.