• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.97-104
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• 2022

The purpose of the existing work is to inspect the impact of knowledge management on organizational performance. Business experts now appreciate how important knowledge management is for organizational performance. Earlier studies have investigated the research model with causal linkages, however, only a few of them have considered sample-selecting bias problems when analyzing the model of knowledge management on organizational performance. The number of 312 executives related to knowledge management from 312 enterprises that have been approved with quality management systems offered suitable responses for analyses. The data was employed to investigate the effect of knowledge management on organizational performance, considering sample-selecting bias. The empirical outcomes indicate that sample-selecting bias exists in the causal impact of knowledge management on organizational performance. The empirical findings are helpful to scholars of knowledge management as well as business executives by giving an insight into the casual effect of knowledge management on organizational performance with the intervention of sample-selecting bias. The acceptance of knowledge management should be tailored to improve competitive advantages that will lead to better organizational performance.

• 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 Biosystems Engineering
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• v.23 no.1
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• pp.1-12
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• 1998

This study was intended to investigate experimentally the effect of inflation pressure on tractive performance of bias-ply tires for agricultural tractors. Traction tests were conducted at the three velocities of 3, 4, and 5.5km/h under few different surface conditions using a 13.6-28 6PR bias-ply tire as driving wheel of the test tractor. When the inflation pressure was reduced from 250kPa to 40kPa by a decrement of either 30 or 50kPa depending upon the test surface conditions, some of the test results showed that the tractive coefficient and efficiency were increased maximally by 40% and 17％, respectively, at 20% slippage. However, it was failed to derive any consistent rules depicting the effect of inflation pressure of bias ply tires on the tractive performance of tractors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.923-929
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• 2004

A magnetostrictive sensor is a sensor measuring elastic waves. Because of its unique non-contact measurement feature, the sensor receives more attentions in recent years. These sensors have been mainly used to measure longitudinal and torsional waves in ferromagnetic waveguides, but there increases an interest in using the sensor for flexural wave measurement. Since the performance of the sensor is strongly influenced by the applied bias magnetic field distribution, the design of the bias magnetic system providing the desired magnetic field is critical. The motivation of this investigation is to design a bias magnetic system consisting of electromagnets and yokes and the specific objective is to formulate the design problem as a bias yoke topology optimization. For the formulation, we employ linear magnetic behavior and examine the optimized results for electromagnets located at various locations. After completing the design optimization, we fabricate the prototype of the proposed bias magnetic system, and test its performance through flexural wave measurements.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.11
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• pp.869-873
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• 2001

The effects of alternate bias stress on p-channel poly-Si TFT\`s has been systematically investigated. We alternately applied positive and negative bias stress on p-channel poly-Si TFT\`s, device Performance(V$\_$th/, g$\_$m/, leakage current, S-slope) are alternately appeared to be increasing and decreasing. It has been shown that device performance degrade under the negative bias stress while improve under the positive bias stress. This effects have been related to the hot carrier injection into the gate oxide rather than the generation of defect states within the poly-Si/SiO$_2$ interface under alternate bias stress.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.179-187
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• 2024

The performance of a Strapdown Inertial Navigation System (SDINS) relies heavily on the accuracy of sensor error calibration. Systematic calibration is usually employed when only a 2-axis turntable is available. For systematic calibration, the body frame is commonly defined with respect to sensor axes for ease of computation. The drawback of this approach is that sensor axes may undergo time-varying deflection under temperature change, causing pseudo gyro bias. The effect of pseudo gyro bias on navigation performance is negligible for low grade navigation systems. However, for higher grade systems undergoing rapid temperature change, the error is no longer negligible. This paper describes in detail conditions leading to the presence of pseudo gyro bias, and proposes two techniques for mitigating the error. Experimental results show that applying these techniques improves navigation performance for precision SDINS, especially under rapid temperature change.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.222-227
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• 2009

The bias characteristics due to the changes of temperature and temperature gradient of fiber coil are investigated in fiber-optic gyroscope. The bias performance is degraded with the changes of temperature and temperature gradient of fiber coil. The temperature compensation using both the temperature-dependent bias measurement and the temperature-induced error model of fiber-optic gyroscope improves the bias stability about 3 times as much as the uncompensated original case, which leads to very stable bias performance over the temperature range from $-35^{\circ}C$ to $+77^{\circ}C$.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.554-558
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• 2002

A magnetostrictive sensor is used to measure stress waves propagating in a ferromagnetic cylinder without physical contact. The performance of a magnetostrictive sensor is affected most significantly by the bias magnetic field applied around the measurement location. The goal of this paper is to carry out the topology optimization of the bias magnet and yoke assembly to maximize the sensor output for traveling bending waves. We will use the multi-resolution topology optimization strategy to find the assembly of the bias magnet and the yoke that is easy to realize. The effectiveness of the present design is confirmed by an actual measurement of the sensor signal with the proposed bias magnet and yoke configuration.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.51-57
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• 2012

In this paper, design of an error model is presented in which the bias characteristic of the MEMS IMU is taken into consideration for performance enhancement of the MEMS IMU-based GPS/INS integrated navigation system. The drift bias of the MEMS IMU is modeled as a 1st-order Gauss-Markov (GM) process, and the autocorrelation function is obtained from the collected IMU data, and the correlation time is estimated from this. Prior to obtaining the autocorrelation function, the noise of IMU data is eliminated based on wavelet. As a result of simulation, it is represented that the parameters of error model can be estimated correctly only when a proper denoising is performed according to dynamic behavior of drift bias, and that the integrated navigation system based on error model, in which the drift bias is considered, provides more correct navigation performance compared to the integrated navigation system based on error model in which the drift bias is not considered.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.133-142
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• 1998

In Bayesian SPECT reconstruction, the incorporation of elaborate forms of priors can lead to improved quantitative performance in various statistical terms, such as bias and variance. In particular, the use of higher-order smoothing priors, such as the thin-plate prior, is known to exhibit improved bias behavior compared to the conventional smoothing priors such as the membrane prior. However, the bias advantage of the higher-order priors is effective only when the hyperparameters involved in the reconstruction algorithm are properly chosen. In this work, we further investigate the quantitative performance of the two representative smoothing priors-the thin plate and the membrane-by observing the behavior of the associated hyperparameters of the prior distributions. In our experiments we use Monte Carlo noise trials to calculate bias and variance of reconstruction estimates, and compare the performance of ML-EM estimates to that of regularized EM using both membrane and thin-plate priors, and also to that of filtered backprojection, where the membrane and thin plate models become simple apodizing filters of specified form. We finally show that the use of higher-order models yields excellent "robustness" in quantitative performance by demonstrating that the thin plate leads to very low bias error over a large range of hyperparameters, while keeping a reasonable variance. variance.