• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.833-834
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• 2016

최근 에너지 시장의 흐름은 공급위주에서 효율위주로 이동함에 따라 가정 내에서 스마트 그리드와 연동하고 태양광 및 에너지저장시스템을 활용한 에너지 관리 시스템 기술에 대한 연구와 개발이 활발히 진행되고 있다. 하지만 시중에서 판매되는 제품은 인터페이스를 갖추고 있지 않거나 제조사별 독자 프로토콜 사용으로 인한 기존 홍 네트워크와 호환성 문제를 가지고 있다 본 논문에서는 호환성 향상을 위한 SEP2.0 기반의 흉 에너지 관리 시스템 개발과 에너지 관리 스케줄링에 대해 기술하고자 한다.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2955-2960
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• 2012

Background: Health disparities exist among and within countries, while developing and low income countries suffer more. The aim of this study was to quantify cancer disparities with regard to socioeconomic position (SEP) in 22 districts of Tehran, Iran. Method: According to the national cancer registry, 7599 new cancer cases were recorded within 22 districts of Tehran in 2008. Based on combined data from census and a population-based health equity study (Urban HEART), socioeconomic position (SEP) was calculated for each district. Index of disparity, absolute and relative concentration indices (ACI & RCI) were used for measuring disparities in cancer incidence. Results: The overall cancer age standardised rate (ASR) was 117.2 per 100,000 individuals (120.4 for men and 113.5 for women). Maximum ASR in both genders was seen in districts 6, 3, 1 and 2. Breast, colorectal, stomach, skin and prostate were the most common cancers. Districts with higher SEP had higher ASR (r=0.9, p<0.001). Positive ACI and RCI indicated that cancer cases accumulated in districts with high SEP. Female disparity was greater than for men in all measures. Breast, colorectal, prostate and bladder ASR ascended across SEP groups. Negative ACI and RCI in cervical and skin cancers in women indicate their aggregation in lower SEP groups. Breast cancer had the highest absolute disparities measure. Conclusion: This report provides an appropriate guide and new evidence on disparities across geographical, demographic and particular SEP groups. Higher ASR in specific districts warrants further research to investigate the background predisposing factors.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• Journal of Food Hygiene and Safety
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• v.1 no.2
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• pp.171-176
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• 1986

ABSTRACT-In order to detect the aflatoxins in home-made Takju and peanut butter, the samples were collected in Chungbuk region and cleaned up Sep-pak silica cartridge. Aflatoxins were detected by thin layer chromatographic and high performance liquid chromatographic behavior. Determination was carried out by thin layer densitometer. The results were as follows; 1. Aflatoxin B, was detected in 78% of the home-made Takju, and the highest concentration was 1.2 ppb and average 0.36 ppb. 2. Aflatoxins were not detected in any peanut butter smaples. 3. Clean-up method by Sep-pak silica cartridge was more efficient and economical than column chromatography of AOAC method.method.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.34.2-34.2
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• 2010

Cosmic rays registered by Neutron Monitors on the surface of the Earth are believed to be coming from outer space, and sometimes also from the exotic objects of the Sun. Ground level enhancement (GLE) is the sudden, sharp and short-lived increase in cosmic rays originated from the Sun. Since GLE is the signature in solar cosmic ray intensity, different solar factors erupted from the Sun can be responsible for causing it. In this context, an attempt has been made to determine quantitative relationships of GLEs > 5% with simultaneous solar, interplanetary and geophysical factors from 1997 through 2006 thereby searching the perpetrators which seem to be causing them. The study has revealed that solar flares are stronger ($0.71{\times}10-4$ w/m2) during GLE peaks than the solar flares ($1.10{\times}10-5$ w/m2) during GLE non-peaks and backgrounds. On the average, the solar wind plasma velocity and interplanetary magnetic field are found stronger during the GLE peaks than the GLE non-peaks and backgrounds indicating that the solar flares, in conjunction with interplanetary shocks, sometimes may cause GLE peaks. Direct proportionality of GLE peaks to simultaneous solar energetic particle (SEP) fluxes imply that the GLE peaks may often be caused by SEP fluxes. Although the high intensity of SEP fluxes are also seen extended few minutes even after GLE peaks, the mean (373.62 MeV) of the GLE associated SEP fluxes is much stronger than the mean (10.35 MeV) of the non-GLE associated SEP fluxes. Evidences are also supported by corresponding SEP fluences that the the mean fluence (${\sim}5.32{\times}107/cm2$) across GLE event was more intense than the mean fluence (${\sim}2.53{\times}106/cm2$) of SEP fluxes across non-GLE event.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.322-330
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• 2011

To compare of quality control for chest radiography between special examination (SEP) and medical institution for pneumoconiosis (MIP). For the first time, we had visited at 33 institutions (SEP; 17 institutions, MIP; 16 institutions) to evaluate the quality control of chest radiography which is used in diagnosis of patients with pneumoconiotic complications. Image quality was rated by two experienced chest radiologists, and evaluated for radiological technique (RT), reading environment (RE) and image quality (IQ) between SEP and MIP according to the guideline published by OSHRI. Generator capacity, used duration and modality of chest radiography equipment were not signigicant difference between SEP and MIP, but there were signigicant difference in tube voltage and grid ratio used for chest radiography except to tube current and exposure time. SEP was statistically significant higher in RT (71.2 vs. 54.5, p=0.015), RE (78.8 vs. 51.5, p=0.007) to MIP, but not significant difference in IQ (64.8 vs. 59.3, p=0.180). For reliable and precisional diagnosis of patients with pneumoconiotic complications, the MIP requires the evaluation and education of quality control for improving chest radiography.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.571-582
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• 1998

The PLS(Partial Least Square) and ANN(Artificial Neural Network) were introduced to develop the soluble solids content prediction model of apples which is followed by making a subsequent selection of photosensor. For the optimal PLS model, number of factors needed for spectrum analysis were increased until the convergence of prediction residual error sum of squares. Analysis has shown that even part of the overall wavelength with no pretreatment may turn out better performing. The best PLS model was found in the 800 to 1,100nm wavelength region without pretreatment of second derivation, having $R^2$=0.9236, bias= -0.0198bx, SEP=0.2527bx for unknown samples. On the other hand, for the ANN model the second derivation led to higher performance. On partial range of 800 to 1,100nm wavelengh region, prediction model with second derivation for unknown samples reached $R^2$=0.9177, SEP=0.2903bx in contrast to $R^2$=0.7507, SEP =0.4622bx without pretreatment.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.166-176
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• 2012

Purpose: This study was conducted to investigate the potential of interactance mode of NIR spectroscopy technology for the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from local greenhouses in three different harvesting seasons (experiments 1, 2, and 3). The fruit attributes were measured at the 6 points on an equator of each sample where the spectral data were collected. The prediction models were developed using the original spectral data and the spectral data sets preprocessed by 20 methods. The performance of the models was compared. Results: In the prediction of SSC, the highest coefficient of determination ($R_{cv}{^2}$) values of the cross-validation was 0.755 (standard error of prediction, SEP=$0.89^{\circ}Brix$) with the preprocessing of normalization with range in experiment 1. The highest coefficient of determination in the robustness tests, $R_{rt}{^2}$=0.650 (SEP=$1.03^{\circ}Brix$), was found when the best model of experiment 3 was evaluated with the data set of experiment 2. The best $R_{cv}{^2}$ for the prediction of firmness was 0.715 (SEP=3.63 N) when no preprocessing was applied in experiment 1. The highest $R_{rt}{^2}$ was 0.404 (SEP=5.30 N) when the best model of experiment 3 was applied to the data set of experiment 1. Conclusions: From the test results, it can be concluded that the interactance mode of VIS/NIR spectroscopy technology has a great potential to measure SSC and firmness of thick-skinned muskmelons.

• Horticultural Science & Technology
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• v.30 no.6
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• pp.709-717
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• 2012

To establish the standard of ripe kiwifruit sorting, near infrared (NIR) spectroscopy was performed on kiwifruit sampled from three farms. Destructive measurements of flesh firmness, soluble solids content (SSC), and acidity were performed and compared to measurement using NIR reflectance spectrums from 408 to 2,492 nm. NIR predictions of those quality factors were calculated using the modified partial least square regression method. Flesh firmness was predicted with a standard error of prediction (SEP) of 3.32 N and with a correlation coefficient ($R^2$) of 0.88. SSC was predicted with SEP of $0.49^{\circ}Brix$ and with $R^2$ of 0.98. Acidity was predicted with SEP of 0.28% and with $R^2$ of 0.91. Kiwifruit ripened at $20^{\circ}C$ for 15 days showed uneven qualities with normal distribution. Considering the SEP of each parameter, kiwifruit after ripening treatment could be non-destructively predicted their qualities and sorted by flesh firmness or soluble solids content through NIR prediction.