• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.149-151
• /
• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.3
• /
• pp.309-330
• /
• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• Proceedings of the SAREK Conference
• /
• 2008.06a
• /
• pp.357-362
• /
• 2008

The present study has been conducted variable refrigerant flow system applied building. Multi air-conditioning system has some benefits : easier building management and maintenance and energy saving. Recently, the system heat pump has been employed in medium-sized and tall buildings. However, the performance data and design method for system heat pump are limited in literature due to complicated system parameters and operating conditions. In the present study, case study of a system heat pump applied various building. The aim of this paper is to application multi air-conditioners and to inform the benefits of multi air-conditioners.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.609-620
• /
• 2008

There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.7
• /
• pp.157-166
• /
• 1999

This paper describes the launching characteristics of a passenger car using a Push-Belt type Continuously Variable Transmission(CVT) which equipped a wet type multi-plate clutch asa starting device and a solid flywheel with a torsional damper for a torsional coupling device. To reduce the torsional vibration of the drive-line , some torsional coupling devices were used for the passenger car equipped CVT having the clutch as a starting device especially . In this study, we developed the computer simulation program to investigate the launching characteristics of a passenger car equipped CVT using the mathematical models of this system. For the mathematical models of the vehicle, the CVT, the we type multi-plate clutch and the torsional damper, we obtained the specification and the necessary data through the reverse engineering of those. For the verification of our analysis, we performed the test of prototype car with different throttle positions at road and dynamometer. The launching characteristics of a passenger car considered here an acceleration performance and an ascending performance.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.1
• /
• pp.33-38
• /
• 2012

As underwater channel is very complex and time-varying, don't supports good-quality for communication service. In this paper, a multi-carrier CDMA(MC-CDMA) system for the reliability and robust service in the underwater acoustic channel is proposed and analyzed for its performance. Applied variable rate algorithm to the proposed system gets a channel state information from relationship between SINR and user data-rate. Using channel state information make spectrum usage more efficient and overall system performance improved. In this paper, the performance of proposed system analyzed by simulation. And Pseudo-Random spread codes used in the system are discussed.

• The Journal of the Korea Contents Association
• /
• v.14 no.11
• /
• pp.722-731
• /
• 2014

The purpose of this study was to identify the relationship between multi-cultural family husbands' stress related to their wife and their psychological abuse based on the Stress theory of Lazarus et al. Data from 271 subjects that answered to all questions regarding the subjects' social demographic characteristics, and the husbands' stress related to their wife and their psychological abuse of their wife in statistical analyses. To analyze the data, a study model was set using multi-cultural family husbands' stress related to their wife as an independent variable and their psychological abuse of their wife as a dependent variable and the goodness-of-fit of the model for the data and the relationship between the variables were reviewed using a structural equation model. According to the results, the goodness-of-fit index of the model satisfied the statistical acceptable standard with CFI=0.909 and RMSEA=0.057. The relationship between the variables indicated that multi-cultural family husbands' stress related to their wife had significant effects on their psychological abuse of their wife. This result indicates the necessity of intervention for management of the husbands' stress in order to prevent multi-cultural family husbands' psychological abuse of their wife.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.671-678
• /
• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• Korean Chemical Engineering Research
• /
• v.45 no.1
• /
• pp.32-38
• /
• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.897-911
• /
• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.