• Korean Journal of Optics and Photonics
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• v.16 no.1
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• pp.13-20
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• 2005

Fiber-Bragg-grating external cavity laser(FGL) modules were fabricated and experimentally analyzed. Proposed as a cost-effective solution for optical sources in the WDM-PON access network, FGL modules were packaged to TO-CAN type. We obtained a low threshold current of 13 mA, and an optical output power of 3.6 mW with a bias current of 60 mA at $25^{\circ}C$. The lasing wavelength dependencies on current and temperature were as small as 5.2 pm/mA and 30 pm/$^{\circ}C$, respectively. These change rates of the wavelength with the temperature and current are smaller than those of the DFB laser. Single-mode oscillations with the side-mode suppression ratio(SMSR) over 30 dB are maintained above the threshold current level. The FGL modules can be directly modulated at 155 Mbps, PRBS(2$^{23}$ -1) NRZ signal. Through the BER plots, we did not see the significant degradations before and after the transmission over 20km of the SMF at 155 Mb/s.

• Food Science and Preservation
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• v.24 no.6
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• pp.778-785
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• 2017

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and $30^{\circ}C$). Baranyi model was used as a primary order model for growth data. The maximum growth rate (${\mu}max$) and lag-phase duration (LPD) for each temperature (except for $10^{\circ}C$ LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.149-155
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• 2015

This paper proposes 5-MS/s 10-bit digital-to-analog converter(DAC) with the improved linearity. The proposed DAC consists of a 10-bit R-2R-based DAC, an output buffer using a differential voltage amplifier with rail-to-rail input range, and a band-gap reference circuit for the bias voltage. The linearity of the 10-bit R-2R DAC is improved as the resistor of 2R is implemented by including the turn-on resistance of an inverter for a switch. The output voltage range of the DAC is determined to be $2/3{\times}VDD$ from an rail-to-rail output voltage range of the R-2R DAC using a differential voltage amplifier in the output buffer. The proposed DAC is implemented using a 1-poly 8-metal 130nm CMOS process with 1.2-V supply. The measured dynamic performance of the implemented DAC are the ENOB of 9.4 bit, SNDR of 58 dB, and SFDR of 63 dBc. The measured DNL and INL are less than +/-0.35 LSB. The area and power consumption of DAC are $642.9{\times}366.6{\mu}m^2$ and 2.95 mW, respectively.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1697-1707
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• 2021

On 19 February 2020, the 2nd Geostationary Ocean Color Imager (GOCI-II), a maritime sensor of GEO-KOMPSAT-2B, was launched. The GOCI-II instrument expands the scope of aerosol retrieval research with its improved performance compared to the former instrument (GOCI). In particular, the newly included UV band at 380 nm plays a significant role in improving the sensitivity of GOCI-II observations to the absorbing aerosols. In this study, we calculated the aerosol index and detected absorbing aerosols from January to June 2021 using GOCI-II 380 and 412 nm channels. Compared to the TROPOMI aerosol index, the GOCI-II aerosol index showed a positive bias, but the dust pixels still could be clearly distinguished from the cloud and clear pixels. The high GOCI-II aerosol index coincided with ground-based observations indicating dust aerosols were detected. We found that 70.5% of dust and 80% of moderately-absorbing fine aerosols detected from the ground had GOCI-II aerosol indices larger than the 75th percentile through the whole study period.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Analytical Science and Technology
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• v.33 no.6
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• pp.274-284
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• 2020

This study described the analytical method for simultaneous determination of 89Sr and 90Sr in liquid sample using automated separation system. Radiostrontium in 0.5 kg of liquid sample was concentrated as SrCO3 to reduce the volume of sample, and purified from the sample using Sr-resin 2 mL (BV, Bed volume). The behavior of Sr and interferences such as Ba, Ca and Y were estimated with various flow rate ranging from 1 to 4 mL min-1. The detailed procedure for the purification of Sr on Sr-resin was presented. The purified radiostronitum was measured in Cerenkov mode and then measured in Scintillation mode by mixing scintillation cocktail. The measured value in both modes were used to calculate the activity of 89Sr and 90Sr. The performance tests were carried out the lab-control-sample having various activity ratio of between 89Sr and 90Sr. The recovery of Sr was ranged from 68 to 94 %. The relative bias of 89Sr activity was ranged from -5 to 20 %, and it was ranged from -10 to 10 % for 90Sr.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• Korean Journal of Culture and Arts Education Studies
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• v.9 no.3
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• pp.45-61
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• 2014

Recently, there is a discussion about culture theory in the area of traffic safety regulation. It has the view that the subject of criticism, etc. by drivers' regulation interpretation, awareness about the danger of regulation violation and nonacceptance of regulation can be changed according to the way drivers' cultural bias was formed. According to the culture theory, fundamental views of the world in particular social relations surrounding individuals, world view or cosmology, are formed and the world view makes an effect on individual behavior and attitude. In this context, cultural cognition and cultural learning theory which are suggested in Christoph Wulf's study on historical-cultural anthropology provide new approach toward this phenomenon. According to his insistence, core mechanisms which can explain cultural cognition and cultural learning are systematized by five things; physical characteristic, mimesis, performance theory, rite and image. The purpose of this research is to investigate the changes by the way Korean people cognize traffic regulations culturally and experiences of traffic regulation violation through the analytic frame of Christoph Wulf's five core mechanisms. To achieve it, cognition of traffic culture was analyzed by analytical phenomenology for drivers who had been educated due to their violation of traffic regulations. Value, lifestyle and practicing methods which are pursued by people work in sociocultural context rather than are influenced by cognitive structure of individuals.