• Journal of the Korean Magnetic Resonance Society
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• v.22 no.3
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• pp.46-53
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• 2018

NMR-based metabolomics needs various knowledge to elucidate metabolic perturbation such as NMR experiments, NMR spectrum processing, raw data processing, metabolite identification, statistical analysis, and metabolic pathway analysis regarding technical aspects. Among them, some concepts of raw data processing and multivariate analysis are not easy to understand but are important to correctly interpret metabolic profile. This article introduces NMR spectrum processing, raw data processing, and multivariate analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1191-1198
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• 2018

The wind profiler radar provides a standing profile of the wind vector and the atmospheric physical signal for the fixed point. Since the wind vector is calculated by the manufacturer's data processing program, the quality control of the date is limited. Therefore, understanding and exploiting the raw spectrum data need to improve the quality of the wind vector. The raw data of the wind vector is the power spectral density stored in binary form. In this study, an algorithm was completed to transform the raw data into the real spectral density, and the use of raw data was evaluated by retrieving zero-order and first-order moments of the spectral based on the spectrum quality control.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1037-1044
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• 2020

Since radar wind profiler (RWP) provides wind field data with high time and space resolution in all weather conditions, their verification of the accuracy and quality is essential. The simultaneous wind measurement from rawinsonde is commonly used to evaluate wind vectors from RWP. In this study, the simulation algorithm which produces the spectrum and raw time series (I/Q) data from the average values of moments is presented as a step-by-step verification method for the signal processing algorithm. The possibility of the simulation algorithm was also confirmed through comparison with the raw data of LAP-3000. The Doppler power spectrum was generated by assuming the density function of the skew-normal distribution and by using the moment values as the parameter. The simulated spectrum was generated through random numbers. In addition, the coherent averaged I/Q data was generated by random phase and inverse discrete Fourier transform, and raw I/Q data was generated through the Dirichlet distribution.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.767-774
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• 2022

In this paper raw spectrum data were analyzed to suggest the moving forward of performance evaluation and quality control of wind profilers of four manufacturers operating in South Korea. For the analysis, the profile of the spectrum averaged by season and the profile of four statistical values (minimum, average, median, and maximum) calculated by Power Spectrum Density (PSD) were used. The quality of spectrum data was the best for LAP-3000, followed by YKJ3, PCL-1300, and CLC-11-H. In Cheorwon and Chupungnyeong, where PCL-1300 was installed, the variability of the spectrum due to ground clutter and non-meteorological signals was large, so ground clutter removal and signal processing such as moving average and multi-peak were required. In Gunsan and Paju, where CLC-11-H was installed, DC (Direct Current) bias and propagation folding were found, so it is necessary to remove the DC bias and limit the effective altitude for observation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1247-1253
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• 2007

The system phase noise spectrum distribution for COMS sensor data transmitter and receiver system was proposed in this paper. On the basis of the analyzed design parameter to reduce the phase noise effect in a receiver, the optimal system phase noise were proposed for raw, IRIT and HRIT data transmission that are sensor data, respectively. The proposed system phase noise provides the qualified transmission performance of sensor data and reduces the performance degradation due to phase noise generating in the transmission channel. Also the system phase noise spectrums are utilized in the design of frequency generation source for sensor data transmission and receiver system.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.61-68
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• 2019

In addition to non-meteorological echoes, meteorological echoes with large scattering effects, such as precipitation, cause errors in wind data measured by wind profiler. In the rainfall situation, the Doppler spectrum of wind profiler shows both the rainfall signal and the atmospheric signal as two peaks. The vertical radial velocity is very large due to the falling rain drop. The radial velocity contaminated by rainfall decreases the accuracy of the horizontal wind vector and leads to inaccurate weather analysis. In this study, we developed an algorithm to process raw data of wind profiler and distinguished rainfall signal and wind signal by partitioning bimodal peak for Doppler spectrum in rainfall environment.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.340-345
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• 1999

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.78-86
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• 2010

The Spectrum Sensing Technology is the core technology of the Cognitive Radio (CR) System that is one of the future wireless communication technologies. In this paper, we proposed the efficient Spectrum Sensing Method using the Short Time Fourier Transform (STFT) that is the algorithm for Time-Frequency analysis of the raw data. Applied window function to STFT algorithm is a Kaiser window, it is piled up its 50% range. For the simulation, the DVB-H signal with the 6MHz bandwidth is used as the Input Signal. And we confirm the Spectrum Sensing result using Modified Periodogram Method, Welch's Method for compared with Short Time Fourier Transform Algorithm. And also, Spectrum Sensing Module is implemented using embedded board.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.4
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• pp.456-461
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• 2012

The leaves of $Stevia$ $rebaudiana$ are well-known in Japan, Korea, and China as a natural sweetener. Medicinal uses of this plant originated in Paraguay and Brazil in the form of aqueous decoctions of the leaves used as a contraceptive agent and for the treatment of hyperglycemia. In the present study, the antioxidant, anti-hypertension, and anti-inflammatory activities of $S.$ $rebaudiana$ extracts are investigated for their use in food. The biologically-active compound was isolated and purified from $S.$ $rebaudiana$. The isolated compound was identified as austroinulin ($C_{20}H_{34}O_3$; molecular weight 322) by mass, IR spectrophotometry, 1D, and 2D-NMR. Austroinulin was characterized as a diterpenoid possessing a 3-methylpenta-2,4-dienyl at C-9. When subjected to an inflammatory mediator inhibitory assay from lipopolysaccharide (LPS)-activated macrophages, the austroinulin inhibited the enhanced production of nitric oxide (NO) and inducible nitric oxide synthase (iNOS) expression (10 ${\mu}g$/mL＝67.9 and 45.1%, respectively). This was significant and dose-dependent. The results suggest that austroinulin from $S.$ $rebaudiana$ inhibited the NO and iNOS in RAW 264.7 cells.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.361-368
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• 2002

The aim of this study was to investigate the effect of preprocessing the transmitted energy spectrum data on development of a robust model to predict the sugar content in intact apples. The spectrum data were measured from 120 Fuji apple samples conveying at the speed of 2 apples per second. Computer algorithms of preprocessing methods such as MSC, SNV, first derivative, OSC and their combinations were developed and applied to the raw spectrum data set. The results indicated that correlation coefficients between the transmitted energy values at each wavelength and sugar contents of apples were significantly improved by the preprocessing of MSC and SNV in particular as compared with those of no-preprocessing. SEPs of the prediction models showed great difference depending on the preprocessing method of the raw spectrum data, the largest of 1.265%brix and the smallest of 0.507% brix. Such a result means that an appropriate preprocessing method corresponding to the characteristics of the spectrum data set should be found or developed for minimizing the prediction errors. It was observed that MSC and SNV are closely related to prediction accuracy, OSC is to number of PLS factors and the first derivative resulted in decrease of the prediction accuracy. A robust calibration model could be d3eveloped by the combined preprocessing of MSC and OSC, which showed that SEP=0.507%brix, bias=0.0327 and R2=0.8823.