• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.421-441
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• 2017

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.17-22
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• 2024

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

• Journal of Information Technology Applications and Management
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• 제19권2호
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• pp.181-196
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• 2012

The cost of software maintenance occupies about two thirds in the software lifecycle. However, it is not easy to estimate the cost of software maintenance because of various viewpoints about software maintenance, unclear estimation methods, and complex procedures. Until now, the cost estimation model has used compensation factors for software characteristic and environment on the basis of program size. Especially, most of existing models use maintenance rate of total software cost as a main variable. This paper suggests the software maintenance cost estimation model that uses the result of calculating real maintenance efforts. In this paper, we classify functional maintenance and non-functional maintenance as software maintenance activity type. For functional maintenance, present function point of target software is needed to evaluate. The suggested maintenance cost evaluation model is applied to a software case in public sector. This paper discusses some differences between our model and other modes.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.637-645
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• 2003

Small area estimation had been studied using data-based methods such as Direct, Indirect, Synthetic methods. However recently, model-based such as based on regression or time series estimation methods are applied to the study. In this paper we investigate a model-data based small area estimation which takes into account the spatial relation among the areas. The Economic Active Population Survey in 2001 are used for analysis and the results from the model based and model-data based estimation are compared with using MSE(Mean squared error), MAE(Mean absolute error) and MB(Mean bias).

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.565-570
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• 2001

Vehicle dynamics control (VDC) system requires more information on driving conditions compared with ABS and/or TCS. In order to develop the VDC system, tire slip angles, vehicle side-slip angle, and vehicle lateral velocity as well as road friction coefficient are needed. Since there are not any cheap and reliable sensors, recent researches on parameter estimation have given rise to a number of parameter estimation techniques. This paper presents new vehicle model to estimate vehicle's yaw rate. This model is improved from the conventional 2 degrees of freedom vehicle model, so-called bicycle model, taking nonlinear effects into account. These nonlinear effects are: (i) tyre nonlinearity; (ii) lateral load transfer during cornering; (iii) variable gear ratio with respect to vehicle velocity. Estimation results are validated with the experimental results.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2007년도 추계학술대회 발표논문집
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• pp.213-230
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• 2007

Although the response model has been frequently applied to nonresponse weighting adjustment or imputation, the estimation under callbacks has been relatively underdeveloped in the response model. The estimation method using the response probability is developed under callbacks. A replication method for the estimation of the variance of the proposed estimation is also developed. Since the true response probability is usually unknown, we study the estimation of the response probability. Finally, we propose an estimator under callbacks using the ratio imputation as well as the response probability. The simulation study illustrates our techniques.

• 한국농공학회지
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• 제32권4호
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• pp.27-43
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• 1990

This study aimed at developing a generalized model on the estimation of the long - term run - off volume for practical purpose. During the research period of last 3 years( 1986-1988), 3 types of estimation model on the long - term run - off volume(Effective rainfall model, unit hydrograph model and barne's model for dry season) had been developed by the author. In this study, through regressional analysis between determinant factors (bi of effective rainfall model, ai of unit hydrograph model and Wi of barne's model) and catchment characteris- tics(catchment area, distance round the catchment area, massing degree coefficient, river - exte- nsion, river - slope, river - density, infiltration of Watershed) of 11 test case areas by multiple regressional method, a new methodology on the derivation of determinant factors from catchment characteristics in the watershed areas having no hydrological station was developed. Therefore, in the resulting step, estimation equations on run - off volume for practical purpose of which input facor is only rainfall were developed. In the next stage, the derived equations were applied on the Kang - and Namgye - river catchment areas for checking of their goodness. The test results were as follows ; 1. In Kang - river area, average relative estimation errors of 72 hydrographs and of continuous daily run - off volume for 245 days( 1/5/1982 - 31/12) were calculated as 6.09%, 9.58% respectively. 2. In Namgye - river area, average relative estimation errors of 65 hydrographs and of conti- nuous daily run - off volume for 2fl days(5/4/1980-31/12) were 5.68%, 10.5% respectively. In both cases, relative estimation error was averaged as 7.96%, and so, the methodology in this study might be hetter organized than Kaziyama's formula when comparing with the relative error of the latter, 24~54%. However, two case studies cannot be the base materials enough for the full generalization of the model. So, in the future studies, many test case studies of this model should he carries out in the various catchment areas for making its generalization.

• 한국CDE학회논문집
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• 제19권1호
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• pp.1-11
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• 2014

In the initial design stage, the technology for estimating and managing the weight of a floating offshore structure, such as a FPSO (Floating, Production, Storage, and Off-loading unit) and an offshore wind turbine, has a close relationship with the basic performance and the price of the structure. In this study, using the genetic programming (GP), being used a lot in the approximate estimating model and etc., the weight estimation model of the floating offshore structure was studied. For this purpose, various data for estimating the weight of the floating offshore structure were collected through the literature survey, and then the genetic programming method for developing the weight estimation model was studied and implemented. Finally, to examine the applicability of the developed model, it was applied to examples of the weight estimation of a FPSO topsides and an offshore wind turbine. As a result, it was shown that the developed model can be applied the weight estimation process of the floating offshore structure at the early design stage.

• 한국산업융합학회 논문집
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• 제26권1호
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• 대한간호학회지
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• 제41권6호
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• pp.750-757
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• 2011

Purpose: Cost-benefit analysis is one of the most commonly used economic evaluation methods, which helps to inform the economic value of a program to decision makers. However, the selection of a correct benefit estimation method remains critical for accurate cost-benefit analysis. This paper compared benefit estimations among three different benefit estimation models. Methods: Data from community-based chronic hypertension management programs in a city in South Korea were used. Three different benefit estimation methods were compared. The first was a standard deterministic estimation model; second, a repeated-measures deterministic estimation model; and third, a transitional probability estimation model. Results: The estimated net benefit of the three different methods were $1,273.01, $-3,749.42, and $-5,122.55 respectively. Conclusion: The transitional probability estimation model showed the most correct and realistic benefit estimation, as it traced possible paths of changing status between time points and it accounted for both positive and negative benefits.