• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.79-84
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the autoregressive and moving average model with auxiliary stochastic input (ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story building model subject to ground exitations.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.611-620
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• 2014

This research analyzed the operation efficiency and influence factors of housing facilities for the severely disabled in Jeolla region by using the DEA model. First, the analysis of efficiency for 2012 was carried out using the CCR model. As a result, 12 DMUs were proved to be efficient, and the average efficiency of CCR was 0.85, confirming that the efficiency of all DMUs were satisfactory. Second, regression analysis was carried out to analyze the factors affecting the efficiency of the DEA model by using the Tobit model. In this case, the inputs and calculated variables were set as independent variables whereas the efficiency as the dependent variable. As a result, the detailed variables had a low significance; the overall input variables showed a negative influence while the calculated variables tended to be a positive influence. In terms of operation efficiency, there was no meaningful result in input variables besides the number of workers. Instead of expanding the input variables, the following should be made for housing facilities of the severely disabled; more efforts should be put in to improve welfare service delivery system and operating environment and structure, and the program must be supplemented as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.114-130
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• 2020

Balinese calendar is defined as a unique calendar system for combining solar-based and lunar-based system and assuming local system. It is considered as guidance of Balinese societies' activities management, starting from meeting arrangement, wedding ceremony, to religious ceremonies. Practically, it has developed in the form of printed Balinese calendar and electronic Balinese calendar, either web or mobile application. The core of the function is to find out the day with its various characteristics in the Balinese Calendar. In general, society usually asks the religious leader to find out the day in detail. The technology of NLP combined with models of pattern discoveries supports the arrangement of the interaction model in searching the good day in Balinese Calendar to equip the conventional searching system in the previous applications. This study will design a dialog model with AIML method in multi-parameter basis; therefore, the users will be dynamically able to use the searching content in various ways by chatting in similar with consulting to a religious leader. This model will be applied in a chatbot basis service in telegram machine. The addition of the context recognition section into 4 paterns has been successfully improve the ability of AIML to recognize input patterns with many criteria. Based on the testing with 50 random input patterns obtained a success rate of 92.5%.

• Computers and Concrete
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• v.16 no.4
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• STI Policy Review
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• v.1 no.2
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• pp.51-68
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• 2010

This paper compares OECD nations by developing a comprehensive evaluation index that examines the efforts and achievements of countries toward Low-Carbon Green Growth. The input-process-output of a Low-Carbon Society system is in dynamic competition with that of a High-Carbon Society system. The model used in this study of the comprehensive evaluation index for Low-Carbon Green Growth was comprised of Large indices such as Input, Process, and Output. The Input and Output consisted of 'Social-economic' and 'Physical-ecological' Middle indices while the Process was made up of 'Stimulation mechanisms' and 'Participation of stakeholders and Knowledge flow' Middle indices. In order to calculate the comprehensive evaluation index, our model gave a weight to each indicator/index and applied a weighted arithmetic mean. Korea ranked $15^{th}$ out of 30 OECD nations in the comprehensive evaluation that analyzed Input ($14^{th}$), Process ($18^{th}$), and Output ($17^{th}$). The top five nations were Switzerland, Sweden, Denmark, Germany, and France; while Japan was $8^{th}$ and the USA $26^{th}$.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.31-38
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• 1997

The main objective of this research is to develop a model to select the optimal input service level for a distribution center - multi branch inventory distribution system. With the continuous review policy, the distribution center places an order for specific order quantity to an outside supplier, and the order quantity is replenished after a certain lead time. Also, each branch places an order for particular order quantity to the distribution center to satisfy the customer demands, and receives the replenishment after a lead time. When an out of stock condition occurs during an order cycle, a backorder is placed to the upper level to fill the unfilled demands. With these situation, variable demand and variable lead time are used for better industrial practice. Further, actual lead times with a generic lead time distribution are used in developing the control model. Under the actual lead time model, the customer service measures actually attained for the distribution center and each branch are explained as the effective customer service measures. Thus, throughout the optimal control (using computer search procedures), we can select the optimal input service levels for the distribution center and each branch to attain the effective service level for each branch which is consistent with the goal level of service for each branch. At the same time, the entire distribution system keeps minimum inventories.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.81-91
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• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.244-255
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• 2011

In this study, we try to evaluate the efficiency of the photonics industry using a data envelopment analysis(DEA) model. We first develope four stage procedures for selecting proper input and output variables which consist of selecting the first candidate variables from literature survey, selecting the second candidate variables through experts' discussion, measuring the partial efficiency of the selected variables based on Tofallis' profiling, and clustering some variables through the rank correlation analysis of partial efficiency proposed by Min and Kim(l998). With this procedure, we select 4 input variables(capital, number of employee, R&D cost, operating cost) and 2 output variables(sales, growth of sales) and then utilize CCR and BCC model to measure efficiencies of 26 photonics companies in Gwangju. Moreover, we perform the reference group analysis to figure out what causes inefficiencies and to provide the desirable values for input and output variables at which inefficient photonics companies become efficient. Finally, we classify 26 photonics companies into three groups such as optical communications, optical applications, and optical sources, and perform the Kruskal-Wallis test to check if there exist some differences between efficiencies of three groups.