• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.169-178
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• 2019

This study examined the influence and role of college students' career preference type on the decision-making process of career selection, and also analyzed the effect on the confession bias in the process. The data was collected from June 20th to July 9th, 2017. The case analysis method was used for three times over 20 days by interviewing university students in the metropolitan area. The results of the analysis of the data from the study participants show that there are differences in the degree of individuals regardless of the preferred occupation type and career aptitude, but mostly there is a bias toward career and career. Confirmed biased thinking is a phenomenon that is shaped by the psychological and environmental factors of the research participants and that they try to fit themselves into a framework that reflects social awareness. The implication of this study is that the preference type affects the selection of the major, and that confirmation bias is a hindrance to proper employment and a job search. The preference for the individual job type should be properly established. Environment and opportunity should be provided.

• Composites Research
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• v.23 no.5
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• pp.8-14
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• 2010

Three types of glass woven fabrics (plain, balanced twill, and unbalanced twill) having various sample sizes and aspect ratios were tested using the bias-extension tests. Real-time deformation images, force, and displacement data were collected. For the bias-extension test, the shear angle of the fabrics from the equation based on the crosshead displacement and fabric size was compared with direct manual measurements of the warp and weft angles as well as the optical measurement software. To determine the shear force, an analytical equation was introduced considering the kinematics of the bias-extension test. The obtained shear behaviors were further compared with the results by the trellis-frame test. The optical measurement methods showed that the mathematical method was reasonable before the shear angle of the fabrics reaches $30^{\circ}$ in the bias-extension tests. Also, the bias-extension test gave consistent behaviors with the trellis-frame test only for isotropic and homogeneous fabrics such as balanced plain and twill weaves.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.229-237
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• 2016

The purpose of this paper is to empirically identify whether unit bias exists or not under the flat rate pricing in the Korean mobile telecommunication market and to give the desirable form of pricing plans for minimizing this irrational behaviors. Our results show that with the flat rate pricing consumers tends to make more voice or data traffic over their optimal consumption level, meaning the existence of unit bias in the Korean mobile market. These results imply that under the current pricing plans subscribers may pay higher monthly fee than their optimal cost which maximizes their utility, for using the telecommunications service. Thus, policy makers need to consider adopting mobile operators' segmentation of the flat rate pricing plans for the reduction of subscribers' telecommunications costs and the improvement of consumer welfare.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.999-1005
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• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.103-129
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• 2019

The main goal of a case-control study is to learn the association between various risk factors and a primary outcome (e.g., disease status). Particularly recently, it is also quite common to perform secondary analyses of the case-control data in order to understand certain associations between the risk factors of the primary outcome. It has been repeatedly documented with case-control data, association studies of the risk factors that ignore the case-control sampling scheme can produce highly biased estimates of the population effects. In this article, we review the issues of the naive secondary analyses that do not account for the biased sampling scheme, and also the various methods that have been proposed to account for the case-control ascertainment. We additionally compare the results of many of the discussed methods in an example examining the association of a particular genetic variant with smoking behavior, where the data were obtained from a lung cancer case-control study.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.201-214
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• 2020

This study proposes a bias correction method of dual-pol radar rain rate in real time using the dual Kalman filter. Unlike the conventional Kalman filter, the dual Kalman filter predicts state variables with two systems (state estimation system and model estimation system) at the same time. Bias of rain rate is corrected by applying the bias correction ratio to the rain rate estimate. The bias correction ratio is predicted from the state-space model of the dual Kalman filter. This method is applied to a storm event with long duration occurred in July 2016. Most of the bias correction ratios are estimated between 1 and 2, which indicates that the radar rain rate is underestimated than the ground rain rate. The AR (1) model is found to be appropriate for explaining the time series of the bias correction ratio. The time series of the bias correction ratio predicted by the dual Kalman filter shows a similar tendency to that of observation data. As the variability of the bias correction increases, the dual Kalman filter has better prediction performance than the Kalman filter. This study shows that the dual Kalman filter can be applied to the bias correction of radar rain rate, especially for long and heavy storm events.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.429-435
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• 2010

This study proposed a methodology for evaluating the radar rainfall data, whose basic idea is similar to the analysis of variance in statistics. This method enables us to represent separately the error from the bias and that from the data variability. The proposed method was then applied to two storm events for its evaluation. As results, the error from the bias was found to comprises most of the raw radar data error, which becomes significantly decreased in the quality improved cases. On the other hand, the error from the data variability was rather increased due to the quality improvement procedure. The proposed methodology was found to be effective for evaluating the data quality of a storm event for steps of quality improvement, but has a limitation for comparing qualities of storm events. This limitation should be implemented for its general application.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1180-1193
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• 2023

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.