• Journal of Radiation Protection and Research
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• v.10 no.2
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• pp.96-108
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• 1985

A study for the assessment of natural environmental radiation exposure at a flat and open field of about $10,000m^2$ in area in CNU Daeduk campus has been carried out by means of gamma-ray scintillation spectrometry and thermoluminescence dosimetry for one year period of time from October 1984. The detectors used were 3'${\phi}{\times}$3' NaI(T1) and two different types of LiF TLD, namely, chip sealed in plastic sheet which tightly pressed on two open holes of a metal plate and Teflon disk. Three 24-hour cycles of in-situ spectrometry, and two 3-month and one 1-month cycles of field TL dosimetry were performed. All the spectra measured were converted into exposure rate by means of G(E) opertaion, and therefrom exposure rate due to terrestrial component of environmental radiation was figured out. Exposure rate determined by the spectrometry was, on average, $(10.54{\pm}2.96){\mu}R/hr$, and the rates of $(12.0{\pm}3.4){\mu}R/hr$ and $(11.0{\pm}3.6){\mu}R/hr$ were obtained from chip and disk TLD, respectively. Fluctuations in diurnal variation of the exposure rate measured by the spectrometry were noticeable sometime even in a single cycle of 24 hours. It is concluded that appropriately combined use of TLD with iu-sitn gamma-ray spectrometry system can give more accurate and precise measure of environmental radiation exposure, and further study for more adequate and sensitive TLD for environmental dosimetry, including improvement and elevation of accuracy in data assessment through inter-laboratory or international intercomparison is necessary.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.85-95
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• 2018

In this research, a biofilter system equipped with a biofilter process and a humidifier composed of a fluidized aerobic and an anoxic reactor, was constructed to treat odorous waste air containing hydrogen sulfide, ammonia and VOC, frequently generated from pig and poultry housing facilities, compost manufacturing factories and publicly owned facilities. Its optimum operating condition was revealed and discussed. In the experiment of complex feed, the ammonia of fed-waste air was removed by ca. 75% and more than 20% at the stage of the humidifier and the biofilter, respectively. The toluene of the fed-waste air was removed by ca. 20% and more than 70% at the stage of the humidifier and the biofilter, respectively. Therefore the water-soluble ammonia and the water-insoluble toluene were treated mainly at the stage of the humidifier and the biofilter, respectively. In addition, hydrogen sulfide was almost absorbed at the stage of the humidifier so that it was not detected at the biofilter process. In the experiment of ammonia-containing feed, the ammonia of fed-waste air was removed by ca. 65% and 35% at the stage of the humidifier and the biofilter, respectively. Its removal efficiency of ammonia at the stage of the humidifier was 10% less than that in the experiment of complex feed, due to no supply of such carbon source as toluene required in the process of denitrification. In the experiments of complex feed, ammonia-containing feed with and without (instead, glucose) the addition of yeast extract, the absorption rates of ammonia-nitrogen were ca. 0.28 mg/min, 0.23 mg/min and 0.27 mg/min, respectively. The corresponding denitrification rates in the anoxic reactor were 0.42 mg/min, 0.55 mg/min and 0.27 mg/min, respectively. In addition, in the modeling of bubble column(the fluidized aerobic reactor of the humidifier) process, the value of specific surface area(a) of bubbles multiplied by enhanced mass transfer coefficient (E $K_y$) was evaluated to be 0.12/hr.

• Journal of Dairy Science and Biotechnology
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• v.14 no.2
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• pp.157-164
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• 1996

This study aimed increase the quality during ripening of Cheddar cheese made with proteinase-negative mutant of Streptococcus lactis KCTC 1913 selected by curing. The degradation of protein during cheese ripening were investigated by electrophoresis and chromatography. The results were summarized as follow ; 1. The number of lactic acid bacteria decreased with the ripening stage, and that of the control cheese decreased faster than that of the cheese made with mutant. 2. Polyacrylamide gel electrophoretic analysis of cheese caseins revealed no difference between the cheese made with mutant and the control cheese, but differences along with the ripening stage were evident. 3. On Sephadex G-25 column chromatography, the extracts of bitter components from the green cheese and 3 month ripended cheese were fractionated into 3 fractions. With the progress of ripening, bitter peptides were degraded to rather small peptides or free amino acids. 4. Sensory evaluation of the 3 month ripended Cheddar cheese found no significant differences in color but the cheese made with mutant evidenced higher palatability in flavor and better texture than the control cheese. 5. The yields of the cheddar cheese made with mutant was 0.14% higher than that of the control cheese.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.3-11
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• 2015

Purpose It is hard to obtain high quality images of knee and T.M joint because of a lot of soft tissues in the knee and T.M joint area. Most conventional system for high resolution scintigraphy was used by 4 mm aperture pinhole collimator. Performance comparison of high-resolution pinhole SPECT for Micro deluxe phantom using conventional system. the aim of this study is to evaluate performance of each aperture according to the diameter size and the usefulness of 24-hour delayed bone scintigraphy. Materials and Methods In this study 6 mm, 8 mm diameter pinhole collimators were mounted on Siemens E.CAM systems. In order to evaluate performance evaluation of each aperture and Micro Deluxe phantom was used for performance comparison of conventional SPECT system, Projection data were obtained with 9 degree increment per 30 second. Transverse images were reconstructed using dedicated OSEM algorithm with recovery of detector blurring. $^{99m}Tc-HDP$ source was used for 24-hour delayed bone scintigraphy. Results The knee joint images obtained with 24-hour delay were improved more than those obtained with 3-hour delay in our study. The 6 mm and 8 mm pinhole collimators FWHM have improved by 28% SNR and Uniformity have improved by 35%, Contrast has improved by 7% in 24-hour delayed knee joint image. While in 24-hour delayed T.M joint image of the 6 mm and 8 mm pinhole collimators FWHM have decreased by 60% SNR has decreased by 20% and Uniformity has decreased by 25%, Contrast has decreased significantly. Conclusion Pinhole collimators with 6 mm and 8 mm diameter could offer a superior performance for 24-hour delayed bone scintigraphy. The use of 24-hour delayed image provides additional benefits for pinhole scintigraphy of knee joint. Therefore, we expect that it is useful for precise diagnosis of knee joint and it is applicable to others joint imaging.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.6
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• pp.921-927
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• 2015

The aim of this study was to investigate the effect of die temperature and repeated extrusion on physical properties of extruded white ginseng (EWG). The die temperature was adjusted to 100, 120, and $140^{\circ}C$, and extrusion was repeated under the same conditions with their corresponding samples. Specific mechanical energy input decreased as die temperature increased during extrusions. The secondary extruded white ginseng (SEWG) at a die temperature of $120^{\circ}C$ showed a higher expansion index than other extrudates. Elevation of both die temperature and repeated extrusion increased the specific length of extrudates. The highest apparent elastic modulus, breaking strength, and water solubility index obtained from SEWG at a die temperature of $100^{\circ}C$ were $7.53{\times}10^8N/m^2$, $7.49{\times}10^5N/m^2$, and 39.02%, respectively. When die temperature increased, water absorption index (WAI) decreased. The WAI of SEWG was higher than that of EWG. In conclusion, repeated extrusion affected physical properties of white ginseng and could be applied to produce improved quality of ginseng products.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.447-463
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• 2019

Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.