• Clinical and Experimental Pediatrics
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• v.46 no.12
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• pp.1235-1241
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• 2003

Purpose : We assessed the dynamic characteristics of 16 nebulizer/compressor combinations currently available in Korea. Methods : The 16 nebulizer/compressor combinations(Pariboy Type 38/Long life, Pariboy Type N/Long life, Pariboy Type N/Salter 8900, Pariboy Type N/LC, Devilbiss pulmoaid-LT/Hudson, Devilbiss pulmoaid/Hudson, Mesmed neb-300/Own, San-up 3040/Hudson, Midas(Basic)/Own, AirJolie 2/Hudson, Thomas 1127/Salter 8900, Noel NE-2000/Salter 8900, Omron CX3/Hudson, Chang Woo CWN-100/Salter 8900, Voyage/Mefar, Chang Woo ASI-Pro/Medel jet pulse) were evaluated in terms of particle size and mass output. In addition, we determined the effects of nebulizer fill volume on mass output. Results : Pariboy Type N/Long life has the highest respirable mass of 0.184 mg/min and Mesmed Neb-300/Own has the lowest 0.019 mg/min. Pariboy Type N/Long life has the highest mass output of 0.68 mg/min and the shortest mass median aerodynamic diameter(MMAD) of $3.76{\mu}m$. All combinations other than Pariboy Type N/Long life produced a MMAD of over $5{\mu}m$. MMAD over a 5 min nebulization ranged 3.76 to $9.83{\mu}m$. There were no significant effects of fill volume on mass output. Conclusion : We concluded that there is a wide variation in performance of nebulizer/compressor combinations. The characteristics of nebulizer/compressor combinations should be considered in selecting products.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.24-33
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• 2021

For this study, we surveyed concentrations of 8 mycotoxins (aflatoxin B1, B2, G1, G2, ochratoxin A, fumonisin B1, B2 and zearalenone) in agricultural products used for food and medicine by liquid chromatography-tandem mass spectrometry and conducted a risk assessment. Samples were collected at the Yangnyeong Market in Seoul, Korea, between January and November 2019. Mycotoxins were extracted from these samples by adding 0.1% formic acid in 50% acetonitrile and cleaned up by using an ISOLUTE Myco cartridge. The method was validated by assessing its matrix effects, linearity, limit of detection (LOD), limit of quantification (LOQ), recovery and precision using four representative matrices. Matrix-matched standard calibration was used for quantification and the calibration curves of all analytes showed good linearity (r2>0.9999). LODs and LOQs were in the range of 0.02-0.11 ㎍/kg and 0.06-0.26 ㎍/kg, respectively. Sample recoveries were from 81.2 to 118.7% and relative standard deviations lower than 8.90%. The method developed in this study was applied to analyze a total of 187 samples, and aflatoxin B1 was detected at the range of 1.18-7.29 ㎍/kg (below the maximum allowable limit set by the Ministry of Food and Drug Safety, MFDS), whereas aflatoxin B2, G1 and G2 were not detected. Mycotoxins that are not regulated presently in Korea were also detected: fumonisin (0.84-14.25 ㎍/kg), ochratoxin A (0.76-17.42 ㎍/kg), and zearalenone (1.73-15.96 ㎍/kg). Risk assessment was evaluated by using estimated daily intake (EDI) and specific guideline values. These results indicate that the overall exposure level of Koreans to mycotoxins due to the intake of agricultural products used for food and medicine is unlikely to be a major risk factor for their health.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.124-134
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• 2019

An analytical method was developed for the determination of broflanilide and its metabolites in agricultural products. Sample preparation was conducted using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method and LC-MS/MS (liquid chromatograph-tandem mass spectrometer). The analytes were extracted with acetonitrile and cleaned up using d-SPE (dispersive solid phase extraction) sorbents such as anhydrous magnesium sulfate, primary secondary amine (PSA) and octadecyl ($C_{18}$). The limit of detection (LOD) and quantification (LOQ) were 0.004 and 0.01 mg/kg, respectively. The recovery results for broflanilide, DM-8007 and S(PFP-OH)-8007 ranged between 90.7 to 113.7%, 88.2 to 109.7% and 79.8 to 97.8% at different concentration levels (LOQ, 10LOQ, 50LOQ) with relative standard deviation (RSD) less than 8.8%. The inter-laboratory study recovery results for broflanilide and DM-8007 and S (PFP-OH)-8007 ranged between 86.3 to 109.1%, 87.8 to 109.7% and 78.8 to 102.1%, and RSD values were also below 21%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food and Drug Safety Evaluation guidelines (2016). Therefore, the proposed analytical method was accurate, effective and sensitive for broflanilide determination in agricultural commodities.