• IE interfaces
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• v.19 no.1
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• pp.34-42
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• 2006

This paper presents a scheme for a hierarchical production scheduling and control system for a refrigerator factory with mixed model assembly lines. The setting of the factory is as follows. There are three mixed-model assembly lines called main line A, B and C and two batch lines that supply parts to the main lines. For each of the main lines, three work-centers are dedicated to them. The sub-lines and work-centers produce parts in batch type. An incoming production order from the master planner is characterized by its product type, amount, and due date. Under this situation, the proposed scheme has several features to schedule and control the above mentioned factory; 1) select the starting time and the place (assembly line) for an order processing, 2) devise a way to control orders to be processed as scheduled, and 3) reschedule orders when something unexpected happen. Finally, this paper provides a case study where the proposed scheme is applied to.

• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.251-260
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• 1998

This paper deals with a simulated annealing neural network model for determining sequences of models inputted into a mixed model assembly line. We first present a energy function fitting to our problem, next determine the value of the parameters of the energy function using convergence ratio and the number of searched feasible solution. Finally we compare our model NMS with the modified Thomopoulos model. The result of the comparison shows that NMS and Thomopoulos offer a similar output in the problems having good smoothness.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1358-1368
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• 2021

A research on agricultural automation is a main issues to overcome the shortage of labor in Korea. A sweet persimmon farmers need much time and labors for classifying profitable sweet persimmon and ill profitable products. In this paper, we propose a mixed two-step synthetic neural network model for efficiently classifying sweet persimmon images. In this model, we suggested a surface direction classification model and a quality screening model which constructed from image data sets. Also we studied Class Activation Mapping(CAM) for visualization to easily inspect the quality of the classified products. The proposed mixed two-step model showed high performance compared to the simple binary classification model and the multi-class classification model, and it was possible to easily identify the weak parts of the classification in a dataset.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.59-65
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• 2010

This paper proposes a modified heuristic mixed model assembly line (MMAL) balancing algorithm that provides consistent station assignments on a model by model basis as well as on a station by station. Basically, some of single model line balancing techniques are modified and incorporated to be fit into the MMAL. The proposed algorithm is based on N.T. Thomopoulos' [8] method and supplemented with several well proven single model line balancing techniques proposed in the literature until recently. Hoffman's precedence matrix [2] is used to indicate the ordering relations among tasks. Arcus' Rule IX [1] is applied to generate rapidly a fairly large number of feasible solutions. Consequently, this proposed algorithm reduces the fluctuations in operation times among the models as well as the stations and the balance delays. A numerical example shows that the proposed algorithm can provide a good feasible solution in a relatively short time and generate relatively better solutions comparing to other three existing methods.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.125-134
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• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• KIEAE Journal
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• v.15 no.5
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• pp.21-33
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• 2015

Purpose: Domestically, a recent controversial part on Mixed-Use Development is its case that utilizes railroad sites among urban infrastructure. but most of all, a concern is being concentrated on the Mixed-Use Development that uses railroad depot. It has the advantage, which can give diversity and publicness to urban environment by using and planing the upper and bottom of railroad depot, a proximity site as Development Available Land. However, there are few cases except for only Yang-cheon APT in SinJeong railway depot as a domestic case even though a domestic concern about Mixed-Use Development is rising more than ever. Method: Accordingly, this study has something in common with a domestic case, dealing with a case of development in Hongkong that enhances efficiency for city space through Mixed-Use Development and solves various urban problems. Result: A purpose of this study, based on overall comprehension about Rail and Property Model of MTRC in Hongkong, is to figure out how it solve various problems in Mixed-Use Development using Railway Depot or stations in the base of Rail and Property Model of MTRC.

• Food Science and Biotechnology
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• v.16 no.2
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• pp.183-186
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• 2007

The type of mixed gel network of $\kappa$-carrageenan/agar was determined by applying rheological principles. Apparent Young's modulus of the mixed gels was mathematically analyzed with (a) simply adding the moduli of two component gels, (b) phase-separated type's upper and lower bound models, (c) interpenetrating type's logarithmic model. The experimental data fitted the estimates from the operation (a). Whereas, as for the models (b), the experimental values in the agar-rich region fitted the estimates of the upper bound model, but in the $\kappa$-carrageenan-rich region slightly deviated from those of the lower bound model. It reflected an evidence of a phase-separated type, although it was not typical, that there must be data good-fit in the agar-rich and $\kappa$-carrageenan-rich regions with the upper and lower bound models, respectively. Experimental values disagreed with estimates of the model (c). Gel time was analyzed to evince the phase-separated type. As agar concentrations increased at a fixed amount of $\kappa$-carrageenan, gel time gradually decreased and then sharply increased and decreased again. The pattern of such change in gel time also represented a typical behavior of phase-separated type's mixed gels.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.178-182
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• 2009

A new mixed-effects model was developed that predicts individual-tree total height for Pinus densiflora trees in Gangwon province as a function of individual-tree diameter (cm). The mixed-effects model contains two random-effects parameters. Maximum likelihood estimation was used to fit the model to 560 height-diameter observations of individual trees measured throughout Gwangwon province in 2007 as part of the National Forest Inventory Program in Korea. The new model is an improvement over fixed-effects models because it can be calibrated to a local area, such as an inventory plot or individual stand. The new model also appears to be an improvement over the Forest Resources Evaluation and Prediction Program for the ten calibration trees used in this study. An example is provided that describes how to estimate the random-effects parameters using ten calibration trees.

• ETRI Journal
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• v.32 no.5
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• pp.687-694
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• 2010

Breaks in presence (BIP) are those moments during virtual environment (VE) exposure in which participants become aware of their real world setting and their sense of presence in the VE becomes disrupted. In this study, we investigate participants' experience when they encounter technical anomalies during game play. We induced four technical anomalies and compared the BIP responses of a navigation mode game to that of a combat mode game. In our analysis, we applied a linear mixed model (LMM) and compared the results with those of a conventional regression model. Results indicate that participants felt varied levels of impact and recovery when experiencing the various technical anomalies. The impact of BIPs was clearly affected by the game mode, whereas recovery appears to be independent of game mode. The results obtained using the LMM did not differ significantly from those obtained using the general regression model; however, it was shown that treatment effects could be improved by consideration of random effects in the regression model.