• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.273-289
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• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.84-92
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• 2002

In this Paper, We propose new performance measure model in Logistic Industry. New model has been learned by key points of PZB model and advanced structure of MBNQA which has cause measure points and effect measure points. The Structure of new performance measure model is Cubic Model which is reflected with time. We try to verify this model apply advance logistic company.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.111-119
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• 1996

The purpose of this study is to construct the main system of simulator for the environment control of agricultural production facilities. The model describing the system was based on the energy and mass balance in an unsteady - state situation. The model consist of the three major parts : the main model, the light model, and the environmental control model, and each part was separated to be developed individually. The main model which is the core of this system includes the thermal model, the soil model, the ventilation model, the cultivation model, and the carbon dioxide model. And also the environmental control model includes the thermal curtain model, the heater/cooler model and the underground heat exchanger model. The equations used in this model were written in analog programming methods using PCSMP The simulator was evaluated through comparison between simulated and measured temperatures controlled during daytime and night. The results showed good agreements between the predicted and measured temperatures.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.591-595
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• 2010

Conventional methods of model evaluation usually rely only on model performance based on a comparison of simulated variables to corresponding observations. However, this type of model evaluation has been criticized because of its insufficient consideration of the various uncertainty sources involved in modeling processes. This study aims to propose an extended model evaluation method using multiple assesment indices (MAIs) that consider not only the model performance but also the model structure and parameter uncertainties in rainfall-runoff modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250m, 500m, and 1km digital elevation models) were developed and assessed by three MAIs for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. In addition, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. Numerous parameter sets could lead to indistinguishable hydrographs. This result supports that while making a model complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty. The proposed model evaluation process can provide an effective guideline for identifying a reliable hydrologic model.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.155-159
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• 2020

In this study, the structural analyses were performed on the number of leaf springs in large truck. The deformations were small for all four models. The maximum stress of model A was found to be the largest, and that of model D was the smallest. Model A was seen about 1.87 times larger than model D and about 1.52 times larger than model B. The maximum stresses of models C and D were seen to be less. In terms of the effect to reinforce one more overlapping spring, The effect of the enhancement of the strength of model D was shown to be small by comparing with model C. Therefore, model C with three overlapping springs is thought to be efficient in design and good in strength. The structural strength of leaf spring can be evaluated by applying this study result to the leaf spring at large truck. And it is seen that the result can be the design of the leaf spring with durability at large truck and the aesthetic convergence.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.103-114
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• 2021

A conceptual model can be used to manage complexity in both the design and implementation phases of the system development life cycle. Such a model requires a firm grasp of the abstract principles on which a system is based, as well as an understanding of the high-level nature of the representation of entities and processes. In this context, models can have distinct architectural characteristics. This paper discusses model multiplicity (e.g., unified modeling language [UML]), model singularity (e.g., object-process methodology [OPM], thinging machine [TM]), and a heterogeneous model that involves multiplicity and singularity. The basic idea of model multiplicity is that it is not possible to present all views in a single representation, so a number of models are used, with each model representing a different view. The model singularity approach uses only a single unified model that assimilates its subsystems into one system. This paper is concerned with current approaches, especially in software engineering texts, where multimodal UML is introduced as the general-purpose modeling language (i.e., UML is modeling). In such a situation, we suggest raising the issue of multiplicity versus singularity in modeling. This would foster a basic appreciation of the UML advantages and difficulties that may be faced during modeling, especially in the educational setting. Furthermore, we advocate the claim that a multiplicity of views does not necessitate a multiplicity of models. The model singularity approach can represent multiple views (static, behavior) without resorting to a collection of multiple models with various notations. We present an example of such a model where the static representation is developed first. Then, the dynamic view and behavioral representations are built by incorporating a decomposition strategy interleaved with the notion of time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.277-283
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• 2005

Kriging model is widely used as design analysis and computer experiment (DACE) model in the field of engineering design to accomplish computationally feasible design optimization. In general, kriging model has been applied to many engineering applications as an interpolation model because it is usually constructed from deterministic simulation responses. However, when the responses include not only global nonlinearity but also numerical error, it is not suitable to use Kriging model that can distort global behavior. In this research, generalized kriging model that can represent both interpolation and regression is proposed. The performances of generalized kriging model are compared with those of interpolating kriging model for numerical function with error of normal distribution type and trigonometric function type. As an application of the proposed approach, the response of a simple dynamic model with numerical integration error is predicted based on sampling data. It is verified that the generalized kriging model can predict a noisy response without distortion of its global behavior. In addition, the influences of maximum likelihood estimation to prediction performance are discussed for the dynamic model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.10
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• pp.515-529
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• 2017

EnergyPlus, which is widely used in various fields, provides Simple Window Model, a window model that can be used practically. However, the results of building load using the model are different from those of the standard model. The main cause of the deviation by Simple Window Model was analyzed to be due to the assumption that all windows were considered as single layer. The purpose of this study is to propose a window model that improves the cause of deviation by Simple Window Model and can be easily calculated from the algebraic relations. The proposed window model solved the heat balance equation algebraically by using seven window characteristic coefficients. The coefficient relationships consisted of the heat transmission coefficient and solar heat gain coefficient as input parameters make practical use and calculation possible. As a result of comparing the deviation between each window model by implementing the dynamic analysis method, the proposed window model showed that the deviation of the total heating/cooling energy consumption was reduced to 1/3 compared to Simple Window Model for one year. Although the maximum energy consumption did not show any significant improvement, the indoor temperature evaluation showed significantly reduced deviation.

• ETRI Journal
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• v.36 no.3
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• pp.479-489
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• 2014

This paper presents a model-based image steganography method based on Watson's visual model. Model-based steganography assumes a model for cover image statistics. This approach, however, has some weaknesses, including perceptual detectability. We propose to use Watson's visual model to improve perceptual undetectability of model-based steganography. The proposed method prevents visually perceptible changes during embedding. First, the maximum acceptable change in each discrete cosine transform coefficient is extracted based on Watson's visual model. Then, a model is fitted to a low-precision histogram of such coefficients and the message bits are encoded to this model. Finally, the encoded message bits are embedded in those coefficients whose maximum possible changes are visually imperceptible. Experimental results show that changes resulting from the proposed method are perceptually undetectable, whereas model-based steganography retains perceptually detectable changes. This perceptual undetectability is achieved while the perceptual quality - based on the structural similarity measure - and the security - based on two steganalysis methods - do not show any significant changes.

• School Mathematics
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• v.9 no.4
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• pp.467-486
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• 2007

In mathematical modeling activity modeling process is usually an iterative process. When model can not be solved, the model needs to be simplified by treating some variables as constants, or by ignoring some variables. On the other hand, when the results from the model are not precise enough, the model needs to be refined by considering additional conditions. In this study we investigate the role of spreadsheet model in model refinement and modeling process. In detail, we observed that by using spreadsheet model students can solve model which can not be solved in paper-pencil environment. And so they need not go back to model simplification process but continue model refinement. By transforming mathematical model to spreadsheet model, the students can predict or explain the real word situations directly without passing the mathematical conclusions step in modeling process.