• Journal of Korean Port Research
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• v.15 no.1
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• pp.37-46
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• 2001

Fuzzy logic control(FLC) is composed of three parts : fuzzy rule-bases, membership functions, and scaling factors. Well-defined fuzzy rule-base should contain proper physical intuition on the plant, so are needed lots of experiences of the skillful expert. When membership functions are considered, some parameters on the memberships function such as function shape, support, allocation density should be selected well. The rule of scaling factors is 'scaling'(amplifying or reducing) for both input and output signals of the FLC to fit in the membership function support and to operate the plant intentionally. To get a better performance of the FLC, it is necessary to adjust the parameters of the FLC. In general, the adaptation of the scaling factors is the most effective adjustment scheme, compared with that of the fuzzy rule-base or membership function parameters. This study proposes the adaptation scheme of the scaling factors. When the adaptation is performed on-line, the stability of the adaptive FLC should be guaranteed. The stable FLC system can be designed with stability analysis in the sense of Lyapunov stability. To adapt the scaling factors for the error signals, the concept of the conventional MRAC would be introduced into slightly modified form. A tracking accuracy of the control system would be enhanced by the modified shape and support of the membership function. The simulation is achieved on the pilot plant with the hydraulic steering control of a UCT(Unmanned Container Transporter) of which modeling dynamics have lots of severe uncertainties and modeling errors.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.134-140
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• 2010

Readily available high resolution brain MRI scans allow detailed visualization of the brain structures. Researchers have focused on developing methods to quantify shape differences specific to diseased scans. We have developed a novel method to quantify shape information for a specific population based on Multidimensional scaling(MDS). MDS is a well known tool in statistics and here we apply this classical tool to quantify shape change. Distance measures are required in MDS which are computed from pair-wise image registrations of the training set. Registration step establishes spatial correspondence among scans so that they can be compared in the same spatial framework. One benefit of our method is that it is quite robust to errors in registrations. Applying our method to 13 brain MRI showed clear separation between normal and diseased (Cushing's syndrome). Intentionally perturbing the image registration results did not significantly affect the separability of two clusters. We have developed a novel method to quantify shape based on MDS, which is robust to image mis-registration.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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• pp.27-32
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• 1988

Electron Spectroscopy for Chemical Analysis(ESCA) allowes the determination of the elemental composition and the bonding state of the surface atomes in the interface between two materials. In the binding energies of ESCA spectrum, there are zero error, voltage scaling error and random error. Accurate analysis of the intensity energy response functions and accurate calibration of the energy scale are essential to use X-ray photoelectron spectron meter. At the results of the calibration of the ESCA spectra in the copper and cordierite (Mg2Al4Si5kO18) interfaces, the errors relative to the copper are -3.03 eV for the zero error -z,-197 ppm for the voltage scaling error -V and 6.9 meV for the random error -R. The method of the calibration is able to apply for the binding energy calibration of the another ESCA spectra.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.103-107
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• 2007

In doubly fed induction generators (DFIGs), control of rotor currents allows for adjustable speed operation, active, and reactive power control. This paper presents a DFIG control strategy that enhances the active and reactive power control with controllers that can compensate for the errors caused by current measurement path in the DFIG system. The errors can be divided into two categories: offset and scaling errors. These can induce the speed, active, and reactive power pulsations, which are one and two times the fundamental slip frequency in the DFIG. And these undesirable ripples can do the DFIG harm. In this paper, a new compensation algorithm is proposed. Therefore, the proposed algorithm has several advantages: to implement is easy; it require less computation time; it is robust with regard to the variation of the induction generator parameters. In this paper, a new algorithm is introduced by using the integral of phase currents to measure the current ripples of rotor-side converterin the DFIG system. The experiment results are shown the effectiveness of the proposed method.

• Proceedings of the KIPE Conference
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• 2004.11a
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• pp.32-36
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• 2004

In order to achieve high performance vector control, it is essential to measure accurate ac current. The errors generated from current path are inevitable, and they could be divided into two categories: offset error and scaling error. The current data including these errors cause periodic speed ripples which are one and two times of stator electrical frequency respectively. Since these undesirable ripples bring about bad influences to motor driving system, a compensation algorithm must be needed in the control algorithm of the motor drive. In this paper, a new compensation algorithm is proposed. The signal of the integrator output of the d-axis current regulator is chosen and processed to compensate the current measurement errors. The compensation of the current measurement errors is easily implemented to smooth the signal of the integrator output of the d-axis current regulator by subtracting the DC offset value or rescaling the gain of the hall sensor. Therefore, the proposed algorithm has several features: the robustness of the variation of the mechanical parameters, the application of the steady and transient state, the easy implementation, and less computation time.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.873-884
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• 2008

In this study, rainfall quantile was estimated using scale invariance property of rainfall data with different durations and the applicability of such property was evaluated for the rainfall data of South Korea. For this purpose, maximum annual rainfall at 22 recording sites of Korea Meteorological Administration (KMA) having relatively long records were used to compare rainfall quantiles between at-site frequency analysis and scale invariance property. As the results, the absolute relative errors of rainfall quantiles between two methods show at most 10 % for hourly rainfall data. The estimated quantiles by scale invariance property can be generally applied in the 8 of 14 return periods used in this study. As an example of down-scaling method, rainfall quantiles of $10{\sim}50$ minutes duration were estimated by scale invariance property based on index duration of 1 hour. These results show less than 10 % of absolute relative errors except 10 minutes duration. It is found that scale invariance property can be applied to estimate rainfall quantile for unmeasured rainfall durations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.1
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• pp.35-40
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• 2004

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed information about the characteristics and the quantities of radionuclides in waste package. Most of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentration of the Difficult-to-Measure (DTM) nuclide is estimated using the correlations of concentration - it is called the scaling factor - between Easy-to-Measure (Key) nuclides and DTM nuclides with the measured concentration of the Key nuclide. In general, the scaling factor is determined by the log mean average (LMA) method and the regression method. However, these methods are inadequate to apply to fission product nuclides and some activation product nuclides such as 14$^{C}$ and 90$^{Sr}$ . In this study, the artificial neural network (ANN) method is suggested to improve the conventional SF determination methods - the LMA method and the regression method. The root mean squared errors (RMSE) of the ANN models are compared with those of the conventional SF determination models for 14$^{C}$ and 90$^{Sr}$ in two parts divided by a training part and a validation part. The SF determination models are arranged in the order of RMSEs as the following order: ANN model

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.1
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• pp.12-18
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• 2021

In this study, the performance modeling of MT30 gas turbine engine is performed. The design point is determined, and the component performance maps to which the scaling technique is applied are generated using standard maps provided by the commercial program. Off-design point performance analysis is performed with the generated performance model, and this is compared with the performance deck data of the engine. It is confirmed that the data of the performance maps generated by the one-point scaling method had some errors from the performance deck data, and it is determined that correction is necessary to increase the accuracy of the performance model. Therefore, the off-design point analysis is performed by creating the correction performance model in a manner that obtains the scaling factors for each operating point(off-design point) according to the high pressure spool speed.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.107-110
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• 2001

In this paper, we propose a multiplication-free DFT kernel computation technique, whose input sequences are approximated into a ring of Algebraic Integers. This paper also gives computational examples for DFT and IDFT. And we proposes an architecture of the DFT using barrel shifts and adds. When the radix is greater than 4, the proposed method has a high Precision property without scaling errors due to twiddle factor multiplication. A possibility of higher radix system assumes that higher performance can be achievable for reducing the DFT stages in FFT.