• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.5
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• pp.3-11
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• 2018

Since the tensegrity structure is flexible and variable, the study on the mobility to the tensegrity has been conducted. However, it is difficult to apply the tensegrity to the architecture field due to several limits. This paper describes the methodology for the analysis of the shape change process of single prism tensegrity structure as an initial study. To apply the tensegrity structure to the architectural field, the assemblage and mathematical formulation procedures of the single prism tensegrity structures are carried out. And single prism tensegrity are presented to the computational strategies for simulate the shape change of those structures. Next, the investigation of structural behaviors through various cases of target displacements is described. Also, the summary of these methods in algorithms is illustrated. As a result it is confirmed that the single prism tensegrity structure model converges 99% on average to a given target node by using the proposed algorithm. Therefore, it is confirmed that the proposed algorithm and program are suitable for shape change analysis of single prism tensegrity structure model.

• Steel and Composite Structures
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• v.43 no.1
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• pp.1-17
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• 2022

The free and forced nonlinear dynamic behaviors of Porous Functionally Graded Material (PFGM) plates are examined by means of a High-Order Implicit Algorithm (HOIA). The formulation is developed using the Third-order Shear Deformation Theory (TSDT). Unlike previous works, the formulation is written without resorting to any homogenization technique neither rule of mixture nor considering FGM as a laminated composite, and the distribution of the porosity is assumed to be gradually variable through the thickness of the PFGM plates. Using the Hamilton principle, we establish the governing equations of motion. The Finite Element Method (FEM) is used to compute approximations of the resulting equations; FEM is adopted using a four-node quadrilateral finite element with seven Degrees Of Freedom (DOF) per node. Nonlinear equations are solved by a HOIA. The accuracy and the performance of the proposed approach are verified by presenting comparisons with literature results for vibration natural frequencies and dynamic response of PFGM plates under external loading. The influences of porosity volume fraction, porosity distribution, slenderness ratio and other parameters on the vibrations of PFGM plate are explored. The results demonstrate the significant impact of different physical and geometrical parameters on the vibration behavior of the PFGM plate.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.717-729
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• 2014

Projection pursuit classification tree uses a 1-dimensional projection with the view of the most separating classes in each node. These projection coefficients contain information distinguishing two groups of classes from each other and can be used to calculate the importance measure of classification in each variable. This paper reviews the variable importance measure with increasing interest in line with growing data size. We compared the performances of projection pursuit classification tree with those of classification and regression tree(CART) and random forest. Projection pursuit classification tree are found to produce better performance in most cases, particularly with highly correlated variables. The importance measure of projection pursuit classification tree performs slightly better than the importance measure of random forest.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.21-33
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• 2006

The reliability analysis can be conducted more effectively by formulating the stochastic finite element method suitable for the reliability theory about the cable stayed bridge. After conducting the initial equilibrium analysis of the cable stayed bridge, the program which can conduct the linear and nonlinear stochastic finite element analysis using the perturbation method and the reliability analysis considered to the correlation of the random variable is developed. Using the results of this program about the cable stayed bridge, the characteristic of the node displacement, element force and cable tension according to the correlation of the random variable is investigated quantitatively. Also the reliability index and the failure probability are examined by the compounding the correlation of the random variable.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.235-241
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• 2008

If the target volume cannot be included with one field at head and neck cancer, we commonly used two or more field. It is very important to irradiate uniform dose at junction area of the fields. However, according to body shape of patient or general condition of patient, skin junction area can be matched incorrect, So overdose area or underdose area can be appeared in the junction area. This study researched therapy technique which can give uniform dose at skin junction owing to applying the edge block of lateral field at head and neck cancer. We measured the changed distance and rotational angle between central line of anterior supraclavicle lymph node and low margin of right lateral field on simulation process using the shielding block of variable rotation. As a result, the changed distance between central line of anterior supraclavicle lymph node and low margin of right lateral field was below 2mm to ${\pm}$10cm distance at central line of Y axis, changed angle was average 1.28 degree. But by using it the shielding block of variable rotation, the incorrect match at junction can be minimized. We think that this technique is very efficient one to apply this technique at head and neck cancered by the movement of organs can be not included, Therefore we have to pay attention on the process to imput MLC layer

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.78-89
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• 2013

This paper proposes a high-throughput and multi-mode quasi-cyclic (QC) low-density parity-check (LDPC) decoder based on a fully parallel structure. The proposed QC-LDPC decoder employs the fully parallel structure to provide very high throughput. The high interconnection complexity, which is the general problem in the fully parallel structure, is solved by using a broadcasting-based sum-product algorithm and proposing a low-complexity cyclic shift network. The high complexity problem, which is caused by using a large amount of check node processors and variable node processors, is solved by proposing a combined check and variable node processor (CCVP). The proposed QC-LDPC decoder can support the multi-mode decoding by proposing a routing-based interconnection network, the flexible CCVP and the flexible cyclic shift network. The proposed QC-LDPC decoder is operated at 100 MHz clock frequency. The proposed QC-LDPC decoder supports multi-mode decoding and provides 8.1 Gbps throughput for a (1944, 1620) QC-LDPC code.