• Journal of the Korean Magnetics Society
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• v.26 no.3
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• pp.81-85
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• 2016

This paper introduces a performance integration method to predict variation characteristic of a performance function of electromagnetic machines or devices due to manufacturing tolerances. A normalized performance function space and a hybrid mean value technique are adapted to effectively predict mean and variance, which can identify probabilistic distribution of the performance function. To verify the effectiveness and accuracy of the proposed method, a mathematical problem and a loudspeaker model are tested, and numerical results are compared with those of existing methods such as Monte Carlo simulation and univariate dimension reduction method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2998-3017
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• 2018

Massive (large-scale) MIMO (multiple-input multiple-output) is one of the key technologies in next-generation wireless communication systems. This paper proposes a high-performance low-complexity turbo receiver for SC-FDMA (single-carrier frequency-division multiple access) based MMIMO (massive MIMO) systems. Because SC-FDMA technology has the desirable characteristics of OFDMA (orthogonal frequency division multiple access) and the low PAPR (peak-to-average power ratio) of SC transmission schemes, the 3GPP LTE (long-term evolution) has adopted it as the uplink transmission to meet the demand high data rate and low error rate performance. The complexity of computing will be increased greatly in base station with massive MIMO (MMIMO) system. In this paper, a low-complexity adaptive turbo equalization receiver based on normalized minimal symbol-error-rate for MMIMO SC-FDMA system is proposed. The proposed receiver is with low complexity than that of the conventional turbo MMSE (minimum mean square error) equalizer and is also with better bit error rate (BER) performance than that of the conventional adaptive turbo MMSE equalizer. Simulation results confirm the effectiveness of the proposed scheme.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.97-102
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• 2012

High-resolution carotid MRI allows visualization of carotid atherosclerotic plaque characteristics. MRI serves as a noninvasive option for the detection of active plaque inflammation and intraplaque hemorrhage. Significant gains in signal-tonoise ratio and contrast-to-noise ratio can be obtained for carotid atheroma imaging at 3T compared with 1.5T. Normalized wall index or wall area on MRI has shown its efficacy in monitoring the response after medical therapy. $T(2)^*$ quantification in carotid plaques before and after the administration of ultrasmall superparamagnetic iron oxide particles shows difference in response to treatment according to drug doses. In conclusion, high-resolution MRI is useful in the diagnosis and monitoring of carotid atherosclerotic plaques prone to transient ischemic attack and stroke.

• Computers and Concrete
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• v.21 no.4
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Atmosphere
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• v.21 no.3
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• pp.243-256
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• 2011

This paper presents the development of new algorithm for identifying and tracking the convective cells in three dimensional reflectivity fields in Cartesian coordinates. First, the radar volume data in spherical coordinate system has been converted into Cartesian coordinate system by the bilinear interpolation. The three-dimensional convective cell has then been identified as a group of spatially consecutive grid points using reflectivity and volume thresholds. The tracking algorithm utilizes a fuzzy logic with four membership functions and their weights. The four fuzzy parameters of speed, area change ratio, reflectivity change ratio, and axis transformation ratio have been newly defined. In order to make their membership functions, the normalized frequency distributions are calculated using the pairs of manually matched cells in the consecutive radar reflectivity fields. The algorithms have been verified for two convective events in summer season. Results show that the algorithms have properly identified storm cells and tracked the same cells successively. The developed algorithms may provide useful short-term forecasting or nowcasting capability of convective storm cells and provide the statistical characteristics of severe weather.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.548-551
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• 2005

In this paper, three axial accelerometer was used to develop a small sensor module, which was attached to human body to calculate the acceleration in gravity direction by human motion, when it was positioned in any direction. To measure its wearer's walking or running motion using the sensor module, the acquired sensor data was pre-processed to enable its quantitative analysis. The acquired digital data was transformed to orthogonal coordinate value in three dimension and calculated to be single scalar acceleration data in gravity direction and normalized to be physical unit value.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.5 no.1
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• pp.5-16
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• 1999

PURPOSE: The purpose of this study was to investigate the influences of lumbosacral angle, lumbar lordosis, pelvic level and symptoms after standing lumbar traction on HIVD patients. METHOD: For this investigation standing lumbar traction was administered to 22 patient who were diagnosed of HIVD. Standing lumbar traction was given to the subject patients for 3 weeks, times a week and each standng lumbar traction lasted 25 minutes. RESULT: For lumbosacral angle statistically significant different was not found although the lumbosacral angle was normalized. For lumbar lordosis statistically significant different was not found although the lumbar lordosis angle was decreased. For pelvic level statistically significant different was not found although the pelvic level was equalized. Statistically significant improvement in symptoms was found after standing lumbar traction. There was significant correlation between lumbar lordosis and lumbosacral angle. CONCLUSION: This study was found that the influences of standing lumbar traction was to decrease symptoms than lumbosacral angle of patients with HIVD. Therefore, it is necessary that to treat the patients with HIVD applied the method to correct spine angle and pelvic level with standing lumbar traction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.482-486
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• 2009

Scale-invariant feature is an effective method for retrieving and classifying images. In this study, we analyze a scale-invariant planar curve features for developing 2D shapes. Scale-space filtering is used to determine contour structures on different scales. However, it is difficult to track significant points on different scales. In mathematics, curvature is considered to be fundamental feature of a planar curve. However, the curvature of a digitized planar curve depends on a scale. Therefore, automatic scale detection for curvature analysis is required for practical use. We propose a technique for achieving automatic scale detection based on difference of curvature. Once the curvature values are normalized with regard to the scale, we can calculate difference in the curvature values for different scales. Further, an appropriate scale and its position are detected simultaneously, thereby avoiding tracking problem. Appropriate scales and their positions can be detected with high accuracy. An advantage of the proposed method is that the detected significant points do not need to be located in the same contour. The validity of the proposed method is confirmed by experimental results.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.395-411
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• 2014

Temporal medical data is often collected during patient treatments that require personal analysis. Each observation recorded in the temporal medical data is associated with measurements and time treatments. A major problem in the analysis of temporal medical data are the missing values that are caused, for example, by patients dropping out of a study before completion. Therefore, the imputation of missing data is an important step during pre-processing and can provide useful information before the data is mined. For each patient and each variable, this imputation replaces the missing data with a value drawn from an estimated distribution of that variable. In this paper, we propose a new method, called Newton's finite divided difference polynomial interpolation with condition order degree, for dealing with missing values in temporal medical data related to obesity. We compared the new imputation method with three existing subspace estimation techniques, including the k-nearest neighbor, local least squares, and natural cubic spline approaches. The performance of each approach was then evaluated by using the normalized root mean square error and the statistically significant test results. The experimental results have demonstrated that the proposed method provides the best fit with the smallest error and is more accurate than the other methods.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.765-778
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• 2016

In watermarking schemes, the discrete wavelet transform (DWT) is broadly used because its frequency component separation is very useful. Moreover, LU decomposition has little influence on the visual quality of the watermark. Hence, in this paper, a novel blind watermark algorithm is presented based on LU transform and DWT for the copyright protection of digital images. In this algorithm, the color host image is first performed with DWT. Then, the horizontal and vertical diagonal high frequency components are extracted from the wavelet domain, and the sub-images are divided into $4{\times}4$ non-overlapping image blocks. Next, each sub-block is performed with LU decomposition. Finally, the color image watermark is transformed by Arnold permutation, and then it is inserted into the upper triangular matrix. The experimental results imply that this algorithm has good features of invisibility and it is robust against different attacks to a certain degree, such as contrast adjustment, JPEG compression, salt and pepper noise, cropping, and Gaussian noise.