• International journal of advanced smart convergence
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• v.10 no.4
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• pp.52-58
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• 2021

Data access bias can be observed in various types of computing systems. In this paper, we characterize the data access bias in modern mobile computing platforms. In particular, we focus on the access bias of data observed at three different subsystems based on our experiences. First, we show the access bias of file data in mobile platforms. Second, we show the access bias of memory data in mobile platforms. Third, we show the access bias of web data and web servers. We expect that the characterization study in this paper will be helpful in the efficient management of mobile computing systems.

• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.295-303
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• 2012

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, for the years of 2006, 2008, 2010, and analyzed the radiosonde seasonal, diurnal bias according to radiosonde sensor types. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. Overall, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the nighttime except for 2006 winter, and in comparison for summer, RS92-SGP sensor showed the highest quality.

• Journal of Astronomy and Space Sciences
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• v.27 no.4
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• pp.367-375
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• 2010

In this study, we compared the precipitable water vapor (PWV) data derived from the radiosonde observation data at Sokcho Observatory and the PWV data at Sokcho Global Positioning System (GPS) Observatory provided by Korea Astronomy and Space Science Institute, from 0000 UTC, June 1, 2007 to 1200 UTC, May 31, 2009, and analyzed the radiosonde bias between the day and the night. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. In addition, for all the rainfall events, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased was significantly less distinctive in nighttime than in daytime. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the second year, regardless of nighttime or daytime rainfall, and the non-rainfall root mean square error (RMSE) was similar to that of the previous studies, while the rainfall RMSE was larger to a certain extent.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.61-71
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• 2015

Quantitative variability of AMSR2 (Advanced Microwave Scanning Radiometer 2) soil moisture data shows that the remotely sensed soil moisture is underestimated during Spring and Winter seasons and is overestimated during Summer and Fall seasons. Therefore the bias correction of the remotely sensed data is essential for the purpose of water resource management. To enhance their applicability, the bias of AMSR2 soil moisture data was corrected using ground observation data at Cheorwon Chuncheon, Suwon, Cheongju, Jeonju, and Jinju sites. Test statistics demonstrated that the correlation coefficient R is improved from 0.107~0.328 to 0.286~0.559 and RMSE is improved from 9.46~14.36 % to 5.38~9.62 %. Bias correction using ground network data improved the applicability of remotely sensed soil moisture data.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3865-3869
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• 2013

Background: Cigarette per day (CPD) use is a key smoking behaviour indicator. It reflects smoking intensity which is directly proportional to the occurrence of tobacco induced cancers. Self reported CPD assessment in surveys may suffer from digit bias and under reporting. Estimates from such surveys could influence the policy decision for tobacco control efforts. In this context, this study aimed at identifying underlying factors of digit bias and its implications for Global Adult Tobacco Surveillance. Materials or Methods: Daily manufactured cigarette users CPD frequencies from Global Adult Tobacco Survey (GATS) - India data were analyzed. Adapted Whipple Index was estimated to assess digit bias and data quality of reported CPD frequency. Digit bias was quantified by considering reporting of '0' or '5' as the terminal digits in the CPD frequency. The factors influencing it were identified by bivariate and logistic regression analysis. Results: The mean and mode of CPD frequency was 6.7 and 10 respectively. Around 14.5%, 15.1% and 15.2% of daily smokers had reported their CPD frequency as 2, 5 and 10 respectively. Modified Whipple index was estimated to be 226.3 indicating poor data quality. Digit bias was observed in 38% of the daily smokers. Heavy smoking, urban residence, North, South, North- East region of India, less than primary, secondary or higher educated and fourth asset index quintile group were significantly associated with digit bias. Discussion: The present study highlighted poor quality of CPD frequency data in the GATS-India survey and need for its improvement. Modeling of digit preference and smoothing of the CPD frequency data is required to improve quality of data. Marketing of 10 cigarette sticks per pack may influence CPD frequency reporting, but this needs further examination. Exploring alternative methods to reduce digit bias in cross sectional surveys should be given priority.

• The Journal of Korea Robotics Society
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• v.4 no.2
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• pp.112-120
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• 2009

In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

• Journal of Korean Academy of Nursing
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• v.40 no.1
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• pp.52-59
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• 2010

Purpose: This study was conducted to provide basic data for developing education and health promotion programs for the prevention of cancer by identifying the relation between optimistic bias about cancer and cancer preventive behavior in Korean, Chinese, American, and Japanese residents in Korea. Methods: Using a questionnaire administered by the researcher, data were collected from a convenience sample of 600, 19 to 64-yr-old male and female Korean, Chinese, American, and Japanese residents in Korea. Data was collected between February 6 and 28, 2009. Results: Scores for optimistic bias about cancer by nationality were: Koreans, -1.03; Chinese, -0.43; Americans, -0.23; and Japanese, 0.05. The cancer preventive behavior scores were: Koreans, 43.17; Chinese, 71.84; Americans, 71.71; and Japanese, 73.97. Optimistic bias about cancer and cancer preventive behavior showed a significantly positive correlation in all participants: Koreans (r=.223, p=.006); Chinese (r=.178, p=.029); Americans (r=.225, p=.006); and Japanese (r=.402, p<.001). Conclusion: The greater the optimistic bias about cancer is, the lower the cancer preventive behavior. The findings suggest that nursing interventions are needed to reduce optimistic bias about cancer and to form a positive attitude towards cancer prevention because an optimistic bias about cancer adversely affects cancer preventive behavior.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1543-1550
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• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.