• Management & Information Systems Review
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• v.26
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• pp.181-204
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• 2008

This study is to categorize five types of conflict handling styles that employees can take when conflict occurs. The five types are integrating, avoiding, dominating, obliging, and compromising. I found these factors that explain conflicts handling styles divided them into organizational structure, task group functioning and need styles and how certain factors explain different kinds of conflict handling styles without other factors. To measure conflict handling styles, this study used the scale of conflict style devised Rahim. Data were collected by the survey method from employees engaged in the service industry located Seoul, the Province of Gyeonggi, and the Province of Gangwon. In addition, in order to prove my hypothesis, I used hierarchical regression analysis method to find the pure explanation that each factors have without multicollinearity. According to the study's result, in a person's type of needs, if the need for achievement is high, they prefer integrating style. In contrast, if the need for achievement is low, they prefer avoiding style. Also, if the need for affiliation is high, the employees prefer compromising style. But if the need for affiliation is low and the need for dominance is high, the employees favor dominating style. However, in task group functioning, group homogeneity, group cohesiveness, and group goal clarity are high, or the confidence in peers and management is high, the employees prefer obliging style to other conflicts handling styles. As well as if group homogeneity, group cohesiveness, and group goal clarity are high, it was found that they prefer compromising style. Also, if the role conflict that is related to organizational structure is serious, employees prefer obliging style, but they have weakenss in explanation. To sum up these results, if the employees have obliging style that shows lack of concerns over themselves and at the same time, have high concerns to others, is affected by task group or organization. And we can infer that the other conflicts handling styles are effected by personal characteristic.

• Journal of Korean Society for Quality Management
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• v.14 no.1
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• pp.53-60
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• 1986

The purpose of this study has been finding where GMDH (Group Method of Data Handling) lies in accordance with comparing other methods and ascertaining the effectiveness of GMDH at the systems of forecasting method. Other methods used for the comparison are: multiple regression model, Brown's third exponential smoothing model. Also the study has reviewed how the expected value and equatior are changed by GMDH. At the same time, the study has also reviewed various characteristics made with comparatively a few data. In conclusion, GMDH is better than the other method in point of view fitness, high effectiveness in self-selection and self-construction of the variables.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Management & Information Systems Review
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• v.24
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• pp.125-154
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• 2008

The purpose of this study is to analyze how the organizational structure, task group functioning and need styles have influence on the conflicts handling styles such as integrating, avoiding, dominating, obliging and compromising as the employee's personality engaged in the service industry. This study uses the hierarchical regression analysis method. Data were collected by the survey method from employees engaged in the service industry located Seoul, the Province of Gyeonggi, and the Province of Gangwon. The study result shows that to the employees with the type A style, need styles are significantly related to the conflicts handling styles such as avoiding. But to the employees with the type B style, need styles are significantly related to the conflicts handling styles such as integrating and avoiding. On the other hand the result shows that the higher the need for achievement and the lower the need for autonomy, employees with the type B style prefer integrating styles to other conflicts handling styles. Or the higher the need for dominance, employees with the type B style prefer dominating styles to other conflicts handling styles. And the higher the need for dominance, employees with the type A style prefer dominating styles to other conflicts handling styles. The study result shows that to the employees with the type A and type X style, task group functioning are significantly related to the conflicts handling styles such as obliging and compromising. But to the employees with the type B style, task group functioning are significantly related to the conflicts handling styles such as obliging and dominating. On the other hand the result shows that the lower faith in peers and management and the higher confidence in peers and management, employees with the type B style prefer obliging style to other conflicts handling styles. But the higher group homogeneity and group cohesiveness, the lower faith in peers and management and the higher confidence in peers and management, employees with the type X style prefer obliging style to other conflicts handling styles. And the higher confidence in peers and management, employees with the type A style prefer compromising style to other conflicts handling styles. The study result shows that to the employees with the type A, organizational structure functioning are significantly related to the conflicts handling styles such as avoiding, obliging and compromising. But to the employees with the type X style, organizational structure functioning are significantly related to the conflicts handling styles such as dominating. On the other hand the result shows that the higher role conflict, the lower role ambiguity and the higher communication system, employees with the type A style prefer avoiding style to other conflicts handling styles. But the lower role ambiguity, employees with the type X style prefer compromising style to other conflicts handling styles. To conclude from these results, employees with the type A style have influence on the organizational structure functioning to other factors on the preference of the conflicts handling styles. And employees with the type B style have influence on the needs styles to other factors on the preference of the conflicts handling styles. Or employees with the type X style have influence on the task group functioning to other factors on the preference of the conflicts handling styles. Although this study provides several managerial implications, this study has some limitations. Specifically data were collected from only the service industry in Seoul, the Province of Gyeonggi, and the Province of Gangwon. In spite of the limitations, the study results could be used valuably in case of the personnel managers which manage the employees under the conflict situations.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.231-242
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• 2023

This paper represents a comparative study in which Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time-dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, Data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization; therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

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

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.50-55
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• 1998

The GMDH(Group Method of Data Handling) is a useful data analysis technique for identification of nonlinear complex systems. Therefore, in this paper the application method of GMDH algorithm for modeling nonlinear dynamic systems is proposed. The identification of dynamic systems by using GMDH consists of applying a set of input/output data and computing the necessary coefficient set dynamically. Also, in this paper, by reducing sequentially the criterion which can adopt or reject the data, a method to prevent excessive computation that is a disadvantage of GMDH is proposed.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).

• Steel and Composite Structures
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• v.42 no.1
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• pp.123-137
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• 2022

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.