• Progress in Medical Physics
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• v.17 no.1
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• pp.1-5
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• 2006

A parallel plate detector containing PTFE films in FEP film for relative dosimetry was designed to measure the response of detectors to S and 10 MV X-rays from a medical linear accelerator through different thicknesses of lead. The dielectric materials were 100 m thick. The set-up conditions for measurements with this detector were as follows: SSD=100 cm the test detector was at a depth of 5 cm and the reference chamber was at a depth of 10 cm from the phantom surface for 6 and 10 MV X-rays. Lead blocks were designed to cover the irradiated field. They were added to the tray to increase thickness sequentially. We found that the detector response decreased exponentially with the thickness of lead added. The linear attenuation coefficients of the test detector and reference chamber were 0.1414 and 0.541, respectively, for 6 MV X-rays and 0.1358 and 0.5279 for 10 MV X-rays. The test detector response was greater than that of the reference chamber. The response function was calculated from the measured values of the test detector and reference chamber using optimization. These optimized constants for the detector response function were independent of theenergy. As a result of optimizing the response function between detectors, the use of a relative dosimeter was validated, because the response of the test detector was 1% for 6 MV X-rays and 4% for 10 MV X-rays.

• 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.

• Food Engineering Progress
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• v.15 no.4
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• pp.388-397
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• 2011

The purpose of this study was to determine the optimum ethanol extraction conditions for maximum extraction of functional components such as ferulic acid, oryzanol, and toopherol from black rice bran using Response Surface Methodology (RSM). A central composite design was applied to investigate the effects of the independent variables of solvent ratio ($X_{1}$), extraction temperature ($X_{2}$) and extraction time ($X_{3}$) on the dependent variables such as total phenol components ($Y_{1}$), total flavonoids compounds ($Y_{2}$), electron donating ability ($Y_{3}$), $\gamma$-oryzanol ($Y_{4}$), ferulic acid ($Y_{5}$) and $\alpha$-toopherol components ($Y_{6}$). ANOVA results showed that coefficients of determination (R-square) of estimated models for dependent variables ranged from 0.8939 to 0.9470. It was found that solvent ratio and extraction temperature were the main effective factors in this extraction proess. Particularly, the extraction efficiency of ferulic acid, $\gamma$-oryzanol and $\alpha$-toopherol components were significantly affected by extraction temperature. As a result, optimum extraction conditions were 20.35 mL/g of solvent ratio, 79.4$^{\circ}C$ of extraction temperature and 2.88 hr of extraction time. Predicted values at the optimized conditions were acceptable when compared with experimental values.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.13-21
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• 2019

A method was developed for the simultaneous detection of an antibiotic fungicide, streptomycin, and its metabolite (dihydrostreptomycin) in agricultural products using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The samples were extracted using methanol adjusted to pH 3 using formic acid, and purified with a HLB (Hydrophilic lipophilic balance) cartridge. The matrix-matched calibration curves were constructed using seven concentration levels, from 0.001 to 0.1 mg/kg, and linearity of five agricultural products (hulled rice, potato, soybean, mandarin, green pepper), with coefficients of determination $(R^2){\geq}0.9906$, for streptomycin and dihydrostreptomycin. The mean recoveries at three fortification levels (LOQ, $LOQ{\times}10$, $LOQ{\times}50$, n = 5) were from 72.0~116.5% and from 72.1~116.0%, and relative standard deviations were less than 12.3% and 12.5%, respectively. The limits of quantification (LOQ) were 0.01 mg/kg, which are satisfactory for quantification levels corresponding with the Positive List System. All optimized results satisfied the criteria ranges requested in the Codex guidelines and the Food Safety Evaluation Department guidelines. The present study could serve as a reference for the establishment of maximum residue limits and be used as basic data for detection of streptomycin and dihydrostreptomycin in food.

• Journal of Food Hygiene and Safety
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• v.37 no.6
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• pp.373-384
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• 2022

Fluoxastrobin a fungicide developed from Strobilurus species mushroom extracts, can be used as an effective pesticide to control fungal diseases. In this study, we optimized the extraction and purification of fluoxastrobin according to its physical and chemical properties using the QuEChERS method and developed an LC-MS/MS-based analysis method. For extraction, we used acetonitrile as the extraction solvent, along with MgSO₄ and PSA. The limit of quantitation of fluoxastrobin was 0.01 mg/kg. We used 0.01, 0.1, and 0.5 mg/kg of five representative agricultural products and treated them with fluoxastrobin. The coefficients of determination (R²) of fluoxastrobin and fluoxastrobin Z isomer were > 0.998. The average recovery rates of fluoxastrobin (n=5) and fluoxastrobin Z isomer were 75.5-100.3% and 75.0-103.9%, respectively. The relative standard deviations (RSDs) were < 5.5% and < 4.3% for fluoxastrobin and fluoxastrobin Z isomer, respectively. We also performed an interlaboratory validation at Gwangju Regional Food and Drug Administration and compared the recovery rates and RSDs obtained for fluoxastrobin and fluoxastrobin Z isomer at the external lab with our results to validate our analysis method. In the external lab, the average recovery rates and RSDs of fluoxastrobin and fluoxastrobin Z isomer at each concentration were 79.5-100.5% and 78.8-104.7% and < 18.1% and < 10.2%, respectively. In all treatment groups, the concentrations were less than those described by the 'Codex Alimentarius Commission' and the 'Standard procedure for preparing test methods for food, etc.'. Therefore, fluoxastrobin is safe for use as a pesticide.