• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.411-421
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• 2014

This study investigated the effect of cognitive narrowing on the consumers' belief biases in the light of the escape theory. Current study researched the relationship between the cognitive narrowing and the consumers' belief biases. The result of this study is as below. The degree of the consumers' belief biases is higher when the degree of the cognitive narrowing is higher than lower. On the basis of this result, theoretical and practical implications were suggested and the limitations and future research were discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.702-710
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• 2008

In this paper, the observability of the INS/GPS integrated system for a land vehicle is analyzed on measurements and different filters with respect to the measurements are designed. In the stationary case, it is shown that horizontal accelerometer biases and vertical attitude errors and gyro biases are unobservable. An 8-state filter is designed based on the observability analysis. When GPS signal is available, a 15-state filter is used with position and velocity measurements. To estimate the INS errors even in the case that GPS signal is blocked a filter is designed in consideration of the non-holonomic constraints of a land vehicle. In this case, the horizontal position and velocity errors and vertical attitude error are unobservable. However, a 12-state filter including the velocity states is designed to estimate the accelerometer biases. When GPS signal recovers, a 9-state filter is used excluding the sensor biases. This paper presents a multi-dimensional filter that switches the four filters according to the usable measurements and maneuver environments. A simulation is carried out to verify the performance of the proposed filter.

• Korean Journal of Childcare and Education
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• v.10 no.1
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• pp.27-42
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• 2014

The primary purpose of this study was to examine the influence of mothers' emotion expressiveness and children's attributional biases on children's aggressive behavior, focusing on gender differences. The data were collected from a total of 86 children; 46 6-year-old boys and 40 6-year-old girls in kindergartens, and their mothers in Seoul. The emotion expressiveness of the mothers were measured by a self-reported Korean version of SEFQ(Self Expressivness Family Questionnaire). Attributional biases of the children were evaluated by using Dodge and Frame's Story-Based Interview Scale. Children's aggressive behavior were measured by teachers using a children's Aggressive Behavior Scale developed by Crick(1995). T-test, correlation analysis, and multiple regression were used to analyze the collected data. The results showed that the relational attributional biases of children positively influenced overt/relational aggressive behaviors. The emotion expressiveness of mothers and the aggressive behavior of children, however, were not significant. Regarding gender differences in children, the negative emotion expressiveness of mothers predicted the girl's relational aggressive behavior negatively. Implications and limitations of this study were discussed.

• Atmosphere
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• v.19 no.2
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• pp.213-226
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• 2009

In order to investigate the characteristics of Changma over the Korean peninsula, KEOP-2007 IOP (Intensive Observing Period) was conducted from 15 June 2007 to 15 July 2007. KEOP-2007 IOP is high spatial and temporal radiosonde observations (RAOB) which consisted of three special stations (Munsan, Haenam, and Ieodo) from National Institute of Meteorological Research, five operational stations (Sokcho, Baengnyeongdo, Pohang, Heuksando, and Gosan) from Korea Meteorological Administration (KMA), and two operational stations (Osan and Gwangju) from Korean Air Force (KAF) using four different types of radiosonde sensors. The error statistics of the sensor of radiosonde were investigated using quality control check. The minimum and maximum error frequency appears at the sensor of RS92-SGP and RS1524L respectively. The error frequency of DFM-06 tends to increase below 200 hPa but RS80-15L and RS1524L show vice versa. Especially, the error frequency of RS1524L tends to increase rapidly over 200 hPa. Systematic biases of radiosonde show warm biases in case of temperature and dry biases in case of relative humidity compared with ECMWF (European Center for Medium-Range Weather Forecast) analysis data and precipitable water vapor from GPS. The maximum and minimum values of systematic bias appear at the sensor of DFM-06 and RS92-SGP in case of temperature and RS80-15L and DFM-06 in case of relative humidity. The systematic warm and dry biases at all sensors tend to increase during daytime than nighttime because air temperature around sensor increases from the solar heating during daytime. Systematic biases of radiosonde are affected by the sensor type and the height of the sun but random errors are more correlated with the moisture conditions at each observation station.

• Korean System Dynamics Review
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• v.5 no.1
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• pp.127-142
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• 2004

Feedback loop dominance is a key concept to understand structural driving forces of system behavior. In this paper, we propose two kinds of shifts in dominant feedback loops: continuous shifts (CS) and discrete shifts (DS). With the help of questionnaires, we verified three hypotheses regarding cognitive biases in perceiving the shifts in dominant feedback loops: 1) failure in perceiving continuous shifts, 2) tendency of decision making based on discrete shifts, and 3) different perception on the dominant feedback loops between level variables and rate variables. We discussed the implication of these cognitive biases on time delay and timing strategy in decision-making processes.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.536-541
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• 2004

It is important to estimate and calibrate sensor errors in maintaining the performance level of SDINS. In this study, an estimation technique of fixed sensor errors for SDINS calibration is discussed. First, the fixed errors of gyros and accelerometers, excluding gyro biases are estimated by the navigation information of SDINS in multi-position. The SDINS with RLG includes flexure errors. In this study, the gyros flexures are out of consideration, but the proposed procedure selects certain positions and rotations in order to minimize the influence of flexures. Secondly, the influences of random walks, flexures and orientation errors are verified via numerical simulations. Thirdly, applying the previous estimated errors to SDINS, the estimation of gyro biases is conducted via the additional control signals of close-loop self-alignment. Lastly, the experiments illustrate that the extracted calibration parameters are available for the improvement of SDINS.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.63-73
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• 2006

Currently, in order to achieve accurate geolocation of satellite images we need to generate control points from individual scenes. This requirement increases the cost and processing time of satellite mapping greatly. In this paper we investigate the feasibility of modeling entire image strips that has been acquired from the same orbital segments. We tested sensor models based on satellite orbit and attitude with different sets of unknowns. We checked the accuracy of orbit modeling by establishing sensor models of one scene using control points extracted from the scene and by applying the models to adjacent scenes within the same orbital segments. Results indicated that modeling of individual scenes with $2^{nd}$ order unknowns was recommended. In this case, unknown parameters were position biases, drifts, accelerations and attitude biases. Results also indicated that modeling of orbital segments with zero-degree unknowns was recommended. In this case, unknown parameters were attitude biases.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.381-389
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• 1996

A simple modification of the standard back-propagation algorithm to eliminate redundant connections(weights and biases) is described. It was motivated by speculations from the distribution of the magnitudes of the weights and the biases, analysis of the classification boundary, and the nonlinearity of the sigmoid function. After initial training, this algorithm eliminates all connections of which magnitude is below a threshold by setting them to zero. The algorithm then conducts retraining in which all weights and biases are adjusted to allow important ones to recover. In studies with Boolean functions, the algorithm reconstructed the theoretical minimum architecture and eliminated the connections which are not necessary to solve the functions. For simulated random signal classification problems, the algorithm produced the result which is consistent with the idea that easier problems require simpler networks and yield lower misclassification rates. Furthermore, in comparison, our algorithm produced better generalization than the standard algorithm by reducing over fitting and pattern memorization problems.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Journal of the Korea Society for Simulation
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• v.33 no.2
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• pp.27-35
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• 2024

We aimed to shed light on the issue of perpetuating societal disparities by analyzing the impact of inherent biases present in datasets used for training artificial intelligence models on the predictions generated by Artificial Intelligence(AI). Therefore, to examine the influence of data bias on AI models, we constructed an original dataset containing biases related to gender wage gaps and subsequently created a de-identified dataset. Additionally, by utilizing the decision tree algorithm, we compared the outputs of AI models trained on both the original and de-identified datasets, aiming to analyze how data de-identification affects the biases in the results produced by artificial intelligence models. Through this, our goal was to highlight the significant role of data de-identification not only in safeguarding individual privacy but also in addressing biases within the data.