• Journal of Information Technology Applications and Management
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• v.10 no.3
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• pp.75-92
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• 2003

In order to investigate IS(Information Systems) outsourcing cost estimation model preference of firms, this study reviews previous literatures on outsourcing and firm characteristics. The relationships between firm characteristics and IS outsourcing cost estimation model preference are analysed. Four major factors of firms characteristics are found and classified. IS outsourcing cost estimation model with SLA are found to have a strong relationship with organizational culture. Future research will be needed to verify the result of this exploratory study.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.407-416
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• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• Journal of Korean Society for Quality Management
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• v.9 no.1
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• pp.51-60
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• 1981

This paper deals with the estimation of the Weibull parameters, which have a close relation with product reliability characteristics. Among the many kinds of estimation methods, Ishikawa's Weibull Probability Paper (WPP) is commonly used. The WPP is very convenient, but it has a great disadvantage in estimation accuracy by plotting method. It is very difficult to get the same results even if one use the same data several times. A computer program for the regression method is used for the parameter estimation to reduce these errors.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.1-16
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• 2007

In the microcell- or picocell-based system the frequent movements of the mobile bring about excessive traffics into the networks. A mobile location estimation mechanism can facilitate both efficient resource allocation and better QoS provisioning through handoff optimization. Existing location estimation schemes consider only LOS model and have poor performance in presence of multi-path and shadowing. In this paper we study a novel scheme which can increase estimation accuracy by considering NLOS environment and other multiple decision parameters than the received signal strength.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.913-924
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• 1991

In this paper, we propose a motion estimation algorithm using hierarchical structure. The algorithm uses the image pyramids from the repetitive application of Gaussian filtering and decimation, and performs an inter-level displacement propagation in its motion estimation process. The motion estimation algorithm based on the hierarchical structure is shown to be very effective since this scheme utilizes the local imformation as well as the global imformation. The experimental results on the various data imdicate that compared to the Horn and Schunck's method, the proposed algorithm yields an accurate motion estimation with a fast convergence behaviour.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.218-230
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• 2007

Measured values of solar radiation data have a characteristic that they change almost by the minute, so original data can be massive. Therefore, we need to construct a database which stores and manages history data of solar radiation data systematically. A study of an estimation method of the future change trend is also required. In this paper, we present a data structure in order to store history data of solar radiation data and propose an estimation method for the change trend of solar radiation that applies to a time-series decomposition method. Also, we present the results of experiments based on measured data from 20 domestic cities in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.471-489
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• 2013

Data compression at the transport layer could both reduce transmitted bytes over network links and increase the transmitted application data (TCP PDU) in one RTT at the same network conditions. Therefore, it is able to improve transmission efficiency on Internet, especially on the networks with limited bandwidth or long delay links. In this paper, we propose an on-the-fly TCP data compression scheme, i.e., the TCPComp, to enhance TCP performance. This scheme is primarily composed of the compression decision mechanism and the compression ratio estimation algorithm. When the application data arrives at the transport layer, the compression decision mechanism is applied to determine which data block could be compressed. The compression ratio estimation algorithm is employed to predict compression ratios of upcoming application data for determining the proper size of the next data block so as to maximize compression efficiency. Furthermore, the assessment criteria for TCP data compression scheme are systematically developed. Experimental results show that the scheme can effectively reduce transmitted TCP segments and bytes, leading to greater transmission efficiency compared with the standard TCP and other TCP compression schemes.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.115-125
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• 2016

PURPOSES : This study proposes a methodology to collect data necessary for microlevel emission estimation, such as second-by-second speeds and road grades, and to accordingly estimate emissions. METHODS : To ease data collection for microlevel emission estimation, a vehicle equipped with speed- and location-recording instruments as well as equipment for measuring road geometry was used. As a case study, this vehicle and the proposed methodology were used on a 10-km-long highway in Yongin City, Korea. Emissions from the vehicle during driving were estimated in various microscale driving conditions. RESULTS : Differences in the estimated emission under different microscale driving conditions cannot be ignored. Compared with the estimations obtained when second-by-second data were not considered, CO and NOx emissions were more than threefold higher when considering second-by-second speed; similarly, CO and NOx emission estimations were higher by approximately 10% and 3%, respectively, when considering second-by-second road grade. CONCLUSIONS : The proposed method can estimate vehicle emissions under real-world driving conditions in such applications as road design and traffic policy assessments.