• Journal of Magnetics
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• v.21 no.3
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• pp.356-363
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• 2016

To improve the sensing accuracy of the newly developed magnetically suspended sensitive gyroscope (MSSG), it is necessary to analyze the causes of drift error. This paper build the models of disturbing torques generated by stator assembly errors based on the geometric construction of the MSSG with double spherical envelope surfaces, and further reveals the generation mechanism of the drift error. Then the drift error from a single stator magnetic pole is calculated quantitatively with the established model, and the key factors producing the drift error are further discussed. It is proposed that the main approaches in reducing the drift error are guaranteeing the rotor envelope surface to be an ideal spherical and improving the controlling precision of rotor displacement. The common problems associated in a gyroscope with a spherical rotor can be effectively resolved by the proposed method.

• Journal of ILASS-Korea
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• v.6 no.4
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• pp.1-13
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• 2001

In the pesticides sprays, spray and atomization technologies to increase the deposition and reduce the drift are briefly reviewed. Further research is needed to deduce a measure of drift risk in sprays with different structures, velocity profiles. For flat fan nozzles, the data of breakup length and thickness of liquid sheet are essential to understand the atomization processes and develop the transport model to target. In the air-assisted spray technology to reduce drift, further works on the effect of application height on drift and air assistance on droplet size should be followed. In addition, methods for quantifying included air in the air inclusion techniques are required. A few researches on the droplet size of fallout can be found in the literature. A combined technology with electrostatic method into one of method for the reduction of drift may be an effective strategy for increasing deposition and reducing drift.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.130-132
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• 2005

This paper presents two ART-based neural networks for the identification of gas mixtures subject to the drift. A fuzzy ARTMAP neural network is used for classifying $H_2S$, $NH_3$ and their mixture gases including a reference gas. The other fuzzy ART neural network is utilized to detect the drift of a tin oxide gas sensor by tracking a cluster center of the reference gas. After detecting the drift, the previous cluster center of each gas is updated as much as the drift of the reference gas. By the simulations, the proposed method is shown to compensate the drift on-line without making many categories of target gases compared with the previous studies.

• Journal of Electrochemical Science and Technology
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• v.5 no.2
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• pp.53-57
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• 2014

With the use of diffusion-limited aggregation modeling, we have investigated the effect of particle drift for dendritic growth. It is found that the morphology of dendritic growth is sensitive to the particle drift, i.e., the larger drift effect results in the denser growth of dendrite. From the analysis using the correlation function, we found the fractional dimension of each dendrite increases as the particles drift increases. Furthermore, we showed the height of dendrite significantly decrease for the slight change of particles drift. Finally, we discussed the strategy to reduce dendritic growth by modifying the transport properties of electrolytes.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.147-151
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• 2020

In this paper, we propose a method to detect concept drift by applying Convolutional Neural Network (CNN) in a data stream environment. Since the conventional method compares only the final output value of the CNN and detects it as a concept drift if there is a difference, there is a problem in that the actual input value of the data stream reacts sensitively even if there is no significant difference and is incorrectly detected as a concept drift. Therefore, in this paper, in order to reduce such errors, not only the output value of CNN but also the probability vector are used. First, the data entered into the data stream is patterned to learn from the neural network model, and the difference between the output value and probability vector of the current data and the historical data of these learned neural network models is compared to detect the concept drift. The proposed method confirmed that only CNN output values could be used to reduce detection errors compared to how concept drift were detected.

• International Journal of Automotive Technology
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• v.7 no.4
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• pp.449-457
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• 2006

The driver model during drift cornering was examined, and a technique to improve vehicle movement performance during drift cornering was investigated. Based on the results obtained, the driver was found to steer using feedback of the body slip angle and the body slip angle velocity during drift cornering. Moreover, improvement of the cornering force characteristic, at which exceeded the maximum cornering force calm as much as possible is important.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.481-488
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• 1998

The drift and inter-story drift control method for steel structures subjected to seismic forces is formulated into a structural optimization problem in this paper. The formulated optimization problem with constraints on drift, inter-story drifts, and member strengthes are transformed into an unconstrained optimization problem. For the solution of the tranformed optimization problem an searching algorithm based on the gradient projection method utilizing gradient information on eigenvalues and eigenvectors are developed and presented in detail. The performance of the proposed algorithm is demonstrated by application to drift control of a verifying example.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.259-266
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• 2023

It is very difficult to measure the performance of the machine learning model in the business service stage. Therefore, managing the performance of the model through the operational department is not done effectively. Academically, various studies have been conducted on the concept drift detection method to determine whether the model status is appropriate. The operational department wants to know quantitatively the performance of the operating model, but concept drift can only detect the state of the model in relation to the data, it cannot estimate the quantitative performance of the model. In this study, we propose a performance prediction model (PPM) that quantitatively estimates precision through the statistics of concept drift. The proposed model induces artificial drift in the sampling data extracted from the training data, measures the precision of the sampling data, creates a dataset of drift and precision, and learns it. Then, the difference between the actual precision and the predicted precision is compared through the test data to correct the error of the performance prediction model. The proposed PPM was applied to two models, a loan underwriting model and a credit card fraud detection model that can be used in real business. It was confirmed that the precision was effectively predicted.

• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.291-296
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• 2016

The ideal chemical sensor must show the similar result under the same condition for accurate measurement of gases regardless of time. However, the actual responses of chemical sensors have been shown the lacks of repeatability and reproducibility because of the drift which has been caused by aging and pollution of the sensor and the environment change such as temperature and humidity. If the problems are not properly taken into considerations, the stability and reliability of the system using chemical sensors would be decreased. In this paper, we analyzed the sensor's drift and applied the three different compensation methods(DWT( Discrete Wavelets Transform), Baseline Manipulation, Internal Normalization) for reducing the effects of the drift in order to improve the stability and the reliability of short term of the chemical sensors. And in order to compare the results of the methods, the standard deviation was used as a criterion. The sensor drift was analyzed by a trend line graph. We applied the three methods to the successive data measured for three days and compared the results. As a result of comparison, the standard deviation of DWT showed lowest value. (Before compensation: 7.1219, DWT: 1.3644, Baseline Manipulation: 2.5209, Internal Normalization: 3.1425).

• Smart Structures and Systems
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• v.25 no.5
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• pp.531-541
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• 2020

This paper demonstrates the possibility of evaluating the safety of a building right after an earthquake using consumer-grade surveillance cameras installed in the building. Two cameras are used in each story to extract the time history of interstory drift during the earthquake based on camera calibration, stereo triangulation, and image template matching techniques. The interstory drift of several markers on the rigid floor are used to estimate the motion of the geometric center using the least square approach, then the horizontal interstory drift of any location on the floor can be estimated. A shaking table collapse test of a steel building was conducted to verify the proposed approach. The results indicate that the accuracy of the interstory drift measured by the cameras is high enough to estimate the damage state of the building based on the fragility curve of the interstory drift ratio. On the other hand, the interstory drift measured by an accelerometer tends to underestimate the damage state when residual interstory drift occurs because the low frequency content of the displacement signal is eliminated when high-pass filtering is employed for baseline correction.