• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• Journal of the Korean Vacuum Society
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• v.2 no.4
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• pp.501-506
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• 1993

The spatial potential distribution of electron cyclotron resonance plasma is measured as a function of tehsubstrate DC bias by Langmuir probe method. It is observed that the substrate DC bias changes the slope of the plasma potential near the subsrate, resulting in changes in flux and energy of the impinging ions across plasma $_strate boundary along themagnetric field. The effect of the substrate DC bias on the low-temperature silicon homoepitaxy (below $560^{\circ}C$) is examine dby in situ reflection high energy electron diffraction (RHEED), cross-section transmission electron microscopy (XTEM),plan-view TEM and high resolution transmision electron microscopy(HRTEM). While the polycrystalline silicon layers are grow withnegative substrate biases, the single crystaline silicon layers are grown with negative substrate biases, the singel crystalline silicon layers are grown with positive substrate biases. As the substrate bias changes form negative to positive values, the growth rate decreases. It is concluded that the control of the ion energy during plasma deposition is very important in silicon epitaxy at low temperatures below $560^{\circ}C$ by UHV-ECRCVD.VD.

• The Korean Journal of Food And Nutrition
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• v.29 no.6
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• pp.952-961
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• 2016

The objective of this study is to examine the effects of consumer beliefs regarding three food certifications on their behavioral intention and the behavioral intention biases to purchase (purchasing intention biases) certified agricultural products as predicted by a subjective probability model. The food certifications used for this study are 'Organic food', 'Traceability system of food products,' and 'Hazard Analysis Critical Control Point (HACCP)'. Tofu (bean curd) was selected as being representative of agricultural food products, for the purposes of this study. In 2016, we surveyed 243 consumers regarding the strength of their belief regarding their prior beliefs relative to each certification, as well as the strength of their intention to purchase certified tofu based on their belief strengths for this study. The study resulted in the following findings: Firstly, consumers hold more than two different prior beliefs for each of the three certifications included in this study. Consumers' prior beliefs regarding these certifications have an impact on their consideration as to whether they plan to buy those certified agricultural products. Secondly, consumers try to persuade themselves to ensure that their particular belief about the product's certification could lead to a purchasing decision regarding that agricultural product.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.595-602
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• 2008

In this paper, performance of the initial alignment for INS whose attitude is not leveled is investigated. Observability of the initial alignment filter is analyzed and estimation errors of the estimated state variables are derived. First, the observability is analyzed using the rank test of observability matrix and the normalized error covariance of the Kalman filter based on the 10-state model. In result, it can be seen that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and the non-leveling tilt angles of a vehicle containing the INS. Especially, this paper shows that the larger the tilt angles of the vehicle are, the larger the estimation errors corresponding to the sensor biases are. Finally, it is shown that the performance of the 8-state model excepting the accelerometer biases on horizontal axes is better than that of the 10-state model in the initial alignment by simulation.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.94-94
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• 2010

Scanning tunneling microscopy (STM) was used to study the electronic structure of cobalt(II) tetraphenylporphyrin (CoTPP) on the Fe/W(110) substrate. Clover-like conformation of CoTPP was observed and showed bias dependent STM images. The central Co(II) ion of this porphyrin was protruded on the positive biases, but it was depressed on the negative biases. On the positive biases, the phenyl rings of CoTPP appeared to be bright contrary to the invisible pyrrole rings. These results were compared the first-principles calculations using GGA and GGA+U to elucidate the influence of the Fe substrate. GGA+U results agreed well with the experimental results; however, GGA did not. These results show that proper treatment of the on-site Coulomb repulsion of the Fe ions is crucial to describe the electronic structure of this system. By the comparison between the GGA+U calculations on the Fe substrate and the gas phase calculations, it can be noted that chemical potential shift occurred accompanying charge transfer from the Fe ions of the substrate to the pyrrole ligand of the porphyrin.

• Atmosphere
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• v.29 no.1
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• pp.53-59
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• 2019

The quality of troposphere ozone in three reanalysis datasets is evaluated with longterm ozonesonde measurement at Pohang, South Korea. The Monitoring Atmospheric Composition and Climate (MACC), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERAI) and Modern Era Retrospective-Analysis for Research and Applications version 2 (MERRA2) are particularly examined in terms of the vertical ozone structure, seasonality and long-term trend in the lower troposphere. It turns out that MACC shows the smallest biases in the ozone profile, and has realistic seasonality of lower-tropospheric ozone concentration with a maximum ozone mixing ratio in spring and early summer and minimum in winter. MERRA2 also shows reasonably small biases. However, ERAI exhibits significant biases with substantially lower ozone mixing ratio in most seasons, except in mid summer, than the observation. It even fails to reproduce the seasonal cycle of lower-tropospheric ozone concentration. This result suggests that great caution is needed when analyzing tropospheric ozone using ERAI data. It is further found that, although not statistically significant, all datasets consistently show a decreasing trend of 850-hPa ozone concentration since 2003 as in the observation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1231-1240
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• 2021

A shift in perspective from standard finance to behavioral finance has taken place in the past two decades that explains how cognition and emotions are associated with financial decision making. This study aims to investigate the influence of various psychological factors on investment decision-making. The psychological factors that are investigated are differentiated into two aspects, cognitive and emotional aspects. From the cognitive aspect, we examine the influence of anchoring, representativeness, loss aversion, overconfidence, and optimism biases on investor decisions. Meanwhile, from the emotional aspect, the influence of herding behavior on investment decisions is analyzed. A quantitative approach is used based on a survey method and a snowball sampling that result in 165 questionnaires from individual investors in Yogyakarta. Further, we use the One-Sample t-test in testing all hypotheses. The research findings show that all of the variables, anchoring bias, representativeness bias, loss aversion bias, overconfidence bias, optimism bias, and herding behavior have a significant effect on investment decisions. This result emphasizes the influence of behavioral factors on investor's decisions. It contributes to the existing literature in understanding the dynamics of investor's behaviors and enhance the ability of investors in making more informed decision by reducing all potential biases.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2844-2853
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• 2023

A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.233-242
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• 2019

ZHD (Zenith Hydrostatic Delay) model is important parameter in estimating of GNSS (Global Navigation Satellite System) PWV (Precipitable Water Vapor) along with weighted mean temperature. The ZWD (Zenith Wet Delay) is tend to accumulate the ZHD error, so that biases from ZHD will be affected on the precision of GNSS PWV. In this paper, we compared the accuracy of GNSS PWV with radiosonde PWV using three ZHD models, such as Saastamoinen, Hopfield, and Black. Also, we adopted the KWMT (Korean Weighted Mean Temperature) model and the mean temperature which was observed by radiosonde on the retrieval processing of GNSS PWV. To this end, GNSS observation data during one year were processed to produce PWVs from a total of 5 GNSS permanent stations in Korea, and the GNSS PWVs were compared with radiosonde PWVs for the evaluating of biases. The PWV biases using mean temperature estimated by the KWMT model are smaller than radiosonde mean temperature. Also, we could confirm the result that the Saastamoinen ZHD which is most used in the GNSS meteorology is not valid in South Korea, because it cannot be exclude the possibility of biases by latitude or height of GNSS station.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.158-166
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• 2022

Background: The effects of radiation on the health of radiation workers who are constantly susceptible to occupational exposure must be assessed based on an accurate and reliable reconstruction of organ-absorbed doses that can be calculated using personal dosimeter readings measured as H_p(10) and dose conversion coefficients. However, the data used in the dose reconstruction contain significant biases arising from the lack of reality and could result in an inaccurate measure of organ-absorbed doses. Therefore, this study quantified the biases involved in organ dose reconstruction and calculated the bias-corrected H_p(10)-to-organ-absorbed dose coefficients for the use in epidemiological studies of Korean radiation workers. Materials and Methods: Two major biases were considered: (a) the bias in H_p(10) arising from the difference between the dosimeter calibration geometry and the actual exposure geometry, and (b) the bias in air kerma-to-H_p(10) conversion coefficients resulting from geometric differences between the human body and slab phantom. The biases were quantified by implementing personal dosimeters on the slab and human phantoms coupled with a Monte Carlo method and considered to calculate the bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients. Results and Discussion: The bias in H_p(10) was significant for large incident angles and low energies (e.g., 0.32 for right lateral at 218 keV), whereas the bias in dose coefficients was significant for the posteroanterior (PA) geometry only (e.g., 0.79 at 218 keV). The bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients derived in this study were up to 3.09- fold greater than those from the International Commission on Radiological Protection publications without considering the biases. Conclusion: The obtained results will aid future studies in assessing the health effects of occupational exposure of Korean radiation workers. The bias-corrected dose coefficients of this study can be used to calculate organ doses for Korean radiation workers based on personal dose records.