• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1053-1059
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• 2013

Heading bias effects in PNS using IMUs attached to shoes are analyzed in this paper. The navigation algorithms of a single foot PNS where one IMU is attached to a foot and dual foot PNSs where two IMUs are attached to each foot are derived. Two navigation algorithms are proposed for the dual foot PNS: 1) the positions from the independent right and left foot PNSs are averaged to provide the final position, 2) the right and left foot PNSs are correlated and it provides positions of each foot. Furthermore, it is proven that two methods are equal. Using the derived navigation algorithms the effect of heading bias caused by a misalignment of the moving direction and IMU is analyzed. The analysis explains the position error of a single foot PNS is diverged while the heading bias is effectively compensated in dual foot PNSs because of the symmetry of heading biases. The experimental results confirm the analysis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.58-62
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• 2003

Silicon carbide (SiC) was etched in a $NF_3/CH_4$ inductively coupled plasma. The etch process was modeled by using a neural network called generalized regression neural network (GRNN). For modeling, the process was characterized by a $2^4$ full factorial experiment with one center point. To test model appropriateness, additional test data of 16 experiments were conducted. Particularly, the GRNN predictive capability was drastically improved by a genetic algorithm (GA). This was demonstrated by an improvement of more than 80% compared to a conventionally obtained model. Predicted model behaviors were highly consistent with actual measurements. From the optimized model, several plots were generated to examine etch rate variation under various plasma conditions. Unlike the typical behavior, the etch rate variation was quite different depending on the bias power Under lower bias powers, the source power effect was strongly dependent on induced dc bias. The etch rate was strongly correated to the do bias induced by the gas ratio. Particularly, the etch rate variation with the bias power at different gas ratio seemed to be limited by the etchant supply.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.36-37
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• 2006

Both negative and positive substrate bias effects on the structural properties and field-emission characteristics are investigated. carbon nanotubes (CNTs) are grown on Ni catalysts employing an inductively-coupled plasma chemical vapor deposition (ICP-CVD) method. Characterization using various techniques, such as field-emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), Auger spectroscopy (AES), and Raman spectroscopy, shows that the physical dimension as well as the crystal quality of CNTs grown can be changed and controlled by the application of substrate bias during CNT growth. It is for the first time observed that the prevailing growth mechanism of CNTs, which is either due to tip-driven growth or based-on-catalyst growth, may be influenced by substrate biasing. It is also seen that negative biasing would be more effectively role in the vertical-alignment of CNTs compared to positive biasing. However, the CNTs grown under the positively bias condition display much better electron emission capabilities than those grown under negative bias or without bias. The reasons for all the measured data regarding the structural properties of CNTs are discussed to confirm the correlation with the observed field-emissive properties.

• Journal of Internet Computing and Services
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• v.2 no.1
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• pp.23-30
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• 2001

ILP processors require an accurate branch prediction scheme to achieve higher performance. Two-Level branch predictor has been known to achieve high prediction accuracy. But, when a branch accesses a PHT entry that was, previously updated by other branch, Two-level predictor may cause interferences. Negative interferences among all interferences have a negative effect on performance, since they can cause branch mispredictions. Agree predictor achieve high prediction accuracy by converting negative interferences to positive interferences by adding bias bits to BTB, but negative interferences may occur when bias bit is set incorrectly. This paper presents a new dynamic branch predictor which reduces negative interferences. In the proposed predictor, we attach hit bits to entries in BTB to change bias bit dynamically during the execution time, h a result the proposed scheme improve the accuracy of prediction by reducing negative Interferences effectively, To illustrate the effect of the proposed scheme, we evaluate the performance of this scheme using SPEC92int benchmarks, The results show that the proposed scheme can outperform traditional branch predictors.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.375-387
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• 2013

Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.

• Journal of the Korean Magnetics Society
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• v.19 no.6
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• pp.191-196
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• 2009

Magnetic bias phenomena of field-annealed CoFeSiB amorphous ribbons showing asymmetric giant magnetoimpedance was investigated by MOKE method. The specimens removed the crystalline layer at one surface side by chemical etching were prepared and measured magnetization curves by MOKE to investigate the effect of the crystalline layer on magnetization of inner soft amorphous phase. We observed the shift of hysteresis loop, and concluded that the crystalline layer exerts bias field effect on inner soft amorphous phase and the direction of bias filed is opposite to the magnetization direction of surface crystalline layer.

• Advances in nano research
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• v.15 no.3
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• pp.263-275
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• 2023

The electron transport properties of Y-type zigzag branched carbon nanotubes (CNTs) are of great significance for micro and nano carbon-based electronic devices and their interconnection. Based on the semi-empirical method combining tight-binding density functional theory and non-equilibrium Green's function, the electron transport properties between the branches of Y-type zigzag branched CNT are studied. The results show that the drain-source current of semiconducting Y-type zigzag branched CNT (8, 0)-(4, 0)-(4, 0) is cut-off and not affected by the gate voltage in a bias voltage range [-0.5 V, 0.5 V]. The current presents a nonlinear change in a bias voltage range [-1.5 V, -0.5 V] and [0.5 V, 1.5 V]. The tangent slope of the current-voltage curve can be changed by the gate voltage to realize the regulation of the current. The regulation effect under negative bias voltage is more significant. For the larger diameter semiconducting Y-type zigzag branched CNT (10, 0)-(5, 0)-(5, 0), only the value of drain-source current increases due to the larger diameter. For metallic Y-type zigzag branched CNT (12, 0)-(6, 0)-(6, 0), the drain-source current presents a linear change in a bias voltage range [-1.5 V, 1.5 V] and is symmetrical about (0, 0). The slope of current-voltage line can be changed by the gate voltage to realize the regulation of the current. For three kinds of Y-type zigzag branched CNT with different diameters and different conductivity, the current-voltage curve trend changes from decline to rise when the branch of drain-source is exchanged. The current regulation effect of semiconducting Y-type zigzag branched CNT under negative bias voltage is also more significant.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.78-84
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• 2009

In this paper, ZnO:Al thin film was deposited on polyethylene terephthalate(PET) substrate by capacitively coupled r. f. magnetron sputtering method from a ZnO target mixed with 2wt[%] Al2O3 to investigate the possible application of ZnO:Al film as a transparent conducting electrode for film typed DSCs. The effect of substrate bias on the electrical properties and film structure were studied. The results showed that a positive bias applied to the substrate during sputtering contributed to an improvement of electrical properties of the film by attracting electrons in the plasma to bombard the growing films. These bombardments provided additional energy to the growing ZnO:Al film on the substrate, resulting in significant variations in film structure and electrical properties. Electrical resistivity of the film decreases significantly as the positive bias increases up to +30[V] However, as the positive bias increases over +30[V], the resistivity decreases. The transmittance varies little as the substrate bias is increased from 0 to +60[V], and as r. f. powers increases from 160[W] to 240[W]. The film with electrical resistivity as low as $1.8{\times}10^{-3}[{\Omega}-cm]$ and optical transmittance of about 87.8[%] were obtained for 1,012[nm] thick film deposited with a substrate bias of +30[V].

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.237-243
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• 2011

Thermal characteristics, such as diffusivity and temperature induced change in the fiber mode index of rotation sensing fiber coil are critical factors which determine the time varying, thermo-optically induced bias drift of interferometric fiber-optic gyroscopes (IFOGs). In this study, temperature dependence of the transient effect is analyzed in terms of the thermal characteristics of the fiber coil at three different temperatures. By applying an analytic model to the measured bias in the experiments, comprehensive thermal factors of the fiber coil could be extracted effectively. The validity of the model was confirmed by the fact that the extracted values are reasonable results in comparison with well known properties of the materials of the fiber coil. Temperature induced changes in the critical factors were confirmed to be essential in compensating the transient effect over a wide temperature range.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.643-656
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• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.