• Steel and Composite Structures
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• v.29 no.1
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• pp.67-75
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• 2018

High performance concrete (HPC) depends on various parameters such as the type of cement, aggregate and water reducer amount. Generally, the ready concrete company in various regions according to the requirements and costs, mix design of concrete as well as type of cement, aggregates, and, amount of other components will vary as a result of moment decisions or dynamic optimization, though the ideal conditions will be more applicable for the design of mix proportion of concrete. This study aimed to apply dynamic optimization for mix design of HPC; consequently, the objective function, decision variables, input and output variables and constraints are defined and also the proposed dynamic optimization model is validated by experimental results. Results indicate that dynamic optimization objective function can be defined in such a way that the compressive strength or performance of all constraints is simultaneously examined, so changing any of the variables at each step of the process input and output data changes the dynamic of the process which makes concrete mix design formidable.

• Design & Manufacturing
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• v.16 no.3
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• pp.50-57
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• 2022

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• Korean Journal of Oriental Medicine
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• v.12 no.2 s.17
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• pp.85-92
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• 2006

Pulse is one of the basic diagnostic information of TKM(Traditional Korean Medicine). To quantify and standardize pulse diagnosis, we had collected an amount of clinical data from May 2005 by using newly developed pulse analyzer. But there were many noises in pulse wave according to measuring method, environment, operator and condition of patient. So some data can’t be included for analyzing diagnosis. To reduce noises from measuring pulse and to collect reliable pulse wave data, we made the process map of measuring method and applied six sigma project. With this we can improved the method of measuring pulse wave in collecting clinical data. The project follows a disciplined process of five macro phases: define, measure, analyze, improve and control (DMAIC). A process map and C-E diagram are used to identify process input and output variables. The major input variables are selected by using C&E matrix, and process map is developed by analyzing input variables. And the optimum process conditions are going to be controled to avoid in increasing loss of collecting pulse wave data.

• Design & Manufacturing
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• v.15 no.1
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• Journal of Korean Academy of Nursing
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• v.24 no.4
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• pp.584-596
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• 1994

The objective of the study is to explore the relationships between the variables of nursing productivity on the framework of system del in the tertiary university based care hospital in Korea. Productivity is basically defined as the relation-ship between inputs and outputs. Under the proposition that the nursing unit is a system that produces nursing care output using personal and material resources through the nursing intervention and nursing care management. And this major conception of nursing productivity system comproises input, process and output and feed-back. These categorized variables are essential parts to produce desirable and meaningful out-put. While nursing personnel from head nurse to staff nurses cooperate with each other, the head nurse directs her subordinates to achieve the goal of nursing care unit. In this procedure, the head nurse uses the leadership of authority and benevolence. Meantime nursing productivity will be greatly influenced by environment and surrounding organizational structures, and by also the operational objectives, the policy and standards of procedures. For the study of nursing productivity one sample hospital with 15 general nursing care units was selected. Research data were collected for 3 weeks from May 31 to June 20 in 1993. Input variables were measured in terms of both the served and the server. And patient classification scores were measured drily by degree of nursing care needs that indicated patent case-mix. And also nurses' educational period for profession and clinical experience and the score of nurses' personality were measured as producer input variables by the questionnaires. The process varialbes act necessarily on leading input resources and result in desirable nursing outputs. Thus the head nurse's leadership perceived by her followers is defined as process variable. The output variables were defined as length of stay, average nursing care hours per patient a day the score of quality of nursing care, the score of patient satisfaction, the score of nurse's job satis-faction. The nursing unit was the basis of analysis, and various statistical analyses were used : Reliability analysis(Cronbach's alpha) for 5 measurement tools and Pearson-correlation analysis, multiple regression analysis, and canonical correlation analysis for the test of the relationship among the variables. The results were as follows : 1. Significant positive relationship between the score of patient classification and length of stay was found(r=.6095, p.008). 2. Regression coefficient between the score of patient classification and length of stay was significant (β=.6245, p=.0128), and variance explained was 39%. 3. Significant positive relationship between nurses’ educational period and length of stay was found(r=-.4546, p=.044). 5. Regression coefficient between nurses' educational period and the score of quality of nursing care was significant (β=.5600, p=.029), and variance explained was 31.4%. 6. Significant positive relationship between the score of head nurse's leadership of authoritic characteristics and the length of stay was found (r=.5869, p=.011). 7. Significant negative relationship between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was found(r=-.4578, p=.043). 8. Regression coefficient between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was significant(β=-.6912, p=.0043), variance explained was 47.8%. 9. Significant positive relationship between the score of the head nurse's leadership of benevolent characteristics and the score of nurses' job satis-faction was found(r=.4499, p=050). 10. A significant canonical correlation was found between the group of the independent variables consisted of the score of the nurses' personality, the score of the head nurse's leadership of authoritic characteristics and the group of the dependent variables consisted of the length of stay, average nursing care hours(Rc²=.4771, p=.041). Through these results, the assumed relationships between input variables, process variable, output variables were partly supported. In addition it is also considered necessary that-further study on the relationships between nurses' personality and nurses' educational period, between nurses' clinical experience including skill level and output variables in many research samples should be made.