• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.12
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• pp.61-67
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• 2005

This paper presents the carrier phase DGPS method providing a stable navigation solution under the condition of frequent blockage of the GPS signals. The proposed algorithm reject the channels having large errors using a clock bias drift and then calculated the more accurate solution. By investigating the relation between visible satellites` elevation and their clock bias drift, a proper threshold is set. Simulation shows that the presented result is as good as that of commercial system with real data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.368-368
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• 2010

Using TFTs crystallized by MICC and ELA, electron mobility and threshold voltage were measured according to various substrate temperature from $-40^{\circ}C$ to $100^{\circ}C$. Basic curve, $V_G-I_D$, is also measured under various stress time from 1s to 10000s. Consequently, due to the passivation effect and number of grains, mobility of MICC is varied in the range of -8% ~ 7.6%, while that of ELA is varied from -11.04% ~ 13.25%. Also, since $V_G-I_D$ curve is dominantly affected by grain size, active layer interface, the graph remained steady under the various gate bias stress time from 1s to 10000s. This proves the point that MICC can be alternative technic to ELA.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.670-674
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• 1992

This paper presents a velocity regulation scheme for a DC motor subjected to random torque and velocity measurement noises of white noise type as well as unknown constant load torque (bias). The scheme separately estimates an unknown bias in addition to state estimation by the bias-free Kalman Filter, and reflects the effect of the bias estimate to the armature input voltage such that velocity variations be regulated. It is shown via computer simulations that the performance of the present scheme is superior to that of the conventional analog PI regulator.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.254-260
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• 2000

This paper deals with the development of fault tolerant control for a nonlinear boiler system with noise and disturbance. The MCMBPC(Multivariable Constrained Model Based Predictive Control) is adopted for the control of the specific boiler turbin model. The fault detection and diagnosis are accomplished with the Kalman filter and two bias estimators. Once a fault is detected, two Bias estimators are driven to estimate the fault and to discriminate Process fault and sensor fault. In this paper, a fault tolerant control scheme combining MCMBPC with a fault compensation method based on the bias estimator is proposed. The proposed scheme has been applied to the nonlinear boiler system and shown a satisfactory performance through some simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.56-61
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• 1998

The velocity measurement error in the velocity-aided SDINS on the maneuvering vehicle is unavoidable and degrades the performance of the SDINS. The characteristics of the velocity measurement error can be modeled as a random bias. This paper proposes a new method for estimating the velocity measurement error in the SDINS. The generalized likelihood ratio test is used for detecting the error and a modified separate-bias Kalman filter in the feedback configuration is suggested for estimating the magnitude of the velocity measurement error.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.1
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• pp.58-66
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• 2003

This paper discusses the practical issue of the bias estimation of the KOREASAT ground tracking data. First, a batch filter based orbit determination algorithm including the turn around range measurement in addition to the range, azimuth and elevation measurement is presented. Then the estimation performance is analyzed through simulation studies. Additionally, this paper proposes a tracking antenna bias estimation strategies using accurately tuned secondary ground tracking station. Finally the relationship between antenna biases are analyzed to give comprehensive tool for estimation results evaluation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• Wind and Structures
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• v.30 no.4
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• pp.393-403
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• 2020

One of the growing concerns of the wind energy production is wind ramp events. To improve the wind ramp event forecasts, the nonlinear Kalman filter bias correction method was applied to 24-h wind speed forecasts issued from the WRF model at 70-m height in Zhangbei wind farm, Hebei Province, China for a two-year period. The Kalman filter shows the remarkable ability of improving forecast skill for real-time wind speed forecasts by decreasing RMSE by 32% from 3.26 m s_-1 to 2.21 m s_-1, reducing BIAS almost to zero, and improving correlation from 0.58 to 0.82. The bias correction improves the forecast skill especially in wind speed intervals sensitive to wind power prediction. The fact shows that the Kalman filter is especially suitable for wind power prediction. Moreover, the bias correction method performs well under abrupt weather transition. As to the overall performance for improving the forecast skill of ramp events, the Kalman filter shows noticeable improvements based on POD and TSS. The bias correction increases the POD score of up-ramps from 0.27 to 0.39 and from 0.26 to 0.38 for down-ramps. After bias correction, the TSS score is significantly promoted from 0.12 to 0.26 for up-ramps and from 0.13 to 0.25 for down-ramps.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.661-665
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• 2022

The 3 MeV proton irradiation effects on SiGe low noise amplifier (LNA) (NXP BGU7005) performance under different voltage supply VCC (0 V, 2.5 V) conditions were firstly experimental studied in this present work. The S parameters including S₁₁, S₂₂, S₂₁, 1 dB compression point and noise figure (NF) of the test samples under different bias voltage supply were measured and compared before and after 3 MeV proton irradiation. The total proton irradiation fluence was 1 × 10¹⁵ protons/cm². The maximum degradation quantities of the gain S₂₁ and NF of the test samples under zero bias are measured respectively 1.6 dB and 1.2 dB. Compared with the samples under 2.5 V bias supply, the maximum degradation of S₂₁ and NF are respectively 1.1 dB and 0.8 dB in the whole frequency band. It is noteworthy that the gain and NF of SiGe LNAs under zero-bias mode suffer enhanced degradation compared with those under normal bias supply. The key influence factors are discussed based on the correlation of the SiGe device and the LNA circuit. Different process of the ionization damage and displacement damage under zero-bias and 2.5 V bias voltage supply contributed to the degradation difference. The underlying physical mechanisms are analyzed and investigated.