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徐永旭,金源培,權得基,李重吉,宋文源 최신의학사 1967 最新醫學 Vol.10 No.2
This clinical study disclosed 89 cesarean sections in 2,014 deliveries, during January 1, 1960 and October 1, 1965 at the Dept. of Ob. & Gyn., Taegu Presbyterian Hospital, Taegu, Korea. Results obtained were as follows: 1) 89 cesarean sections in total 2,014 deliveries, an operative incidence of 4.4 per cent, in 5 year and 9 month periods indicates an increase in incidence. 2) The commonest indication of the cesarean section was cephalopelvic disproportion (33.7%), and was followed by placenta previa (20.2%), eclampsia (14.6%), vaginal stricture (7.8%), ruptured uterus (6.8%), repeat section (4.5%), malpresentation (4.5%), abruptio placentae (3.4%), and so forth. 3) The predominant anesthetic method used in this study was local infiltration by I% procaine with 2.5 % pentothal sodium induction and with ether inhalation general anesthesia (73%). There was no anesthetic death. 4) The low cervical type of cesarean sectieon (73%) was the predominant operation used, while the classical cesarean section (20.2%) has not been completely abandoned yet. Cesarean hysterectomy was performed on 8 cases (9.6%). 5) Postoperative maternal morbidity was 19.1 %. 6) Maternal mortality was 2.2%, and death of these 2 cases were due to severe eclampsia per se, and not due to operation. 7) Perinatal mortality was 23.5%. 8) A comparative study of statistics and a review of literature of cesarean section were made.
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서영욱,이훈수,배형진,박은수,임현섭,김문성,조병관 한국농업기계학회 2019 Journal of Biosystems Engineering Vol.44 No.2
This study proposes a nondestructive sorting method based on the short-wave infrared hyperspectral imaging technique (SWIR-HIT) to detect and classify watermelon seeds infected with the cucumber green mosaic mottle virus (CGMMV). Virus-infected watermelon seeds were collected from virus-infected watermelon plants. Five plates each with 81 seeds were scanned. A total of 304 mean reflectance spectra were used to develop and evaluate virus-infected seed classification models with multivariate analysis methods such as partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and least squares support vector machine (LS-SVM). To determine the optimal preprocessing method, three preprocessing methods were employed: multivariate scatter correct (MSC) as well as first- and second-derivative preprocessing with the Savitzky–Golay algorithm. Among these methods, secondderivative preprocessing with the LS-SVM method showed an approximately 75% accuracy with a 0.57 kappa coefficient for all three classification classes (infected, infection suspected, and sound seeds). Binary classification between infected and sound seeds by LSSVM with second-derivative preprocessing showed an approximately 92% accuracy with a 0.75 kappa coefficient. To improve the classification accuracy, the genetic algorithm was implemented, and 9 bands were selected. The selected wavelengths were applied to develop and compare classification models with full wavelengths. The three-class classification with the selected bands showed an approximately 80%accuracy, whereas binary classification in infected and sound seeds showed a more than 93%accuracy with a 0.78 kappa coefficient. These results indicate that SWIR-HIT is a valuable nondestructive tool for rapidly classifying CGMMV-infected watermelon seeds using LS-SVM with raw spectra.
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Evaluation of Nonthermal Plasma Treatment by Measurement of Stored Citrus Properties
서영욱,박종률,박회만 한국농업기계학회 2018 Journal of Biosystems Engineering Vol.43 No.4
Decay of fruit is one of the greatest issues in fruit storage. Purpose: In this study, citrus sterilization was performed to evaluate a dry sterilization method using an atmospheric-pressure nonthermal plasma treatment based on a dielectric-barrier discharge technique. Methods: Citrus samples were stored under four different environmental conditions as follows: group A had cold storage with plasma treatment with a temperature of 6.2 ± 1.0℃ and relative humidity (RH) of 93.4 ± 8.2%, group B had ambient-temperature storage with 22.9 ± 2.3℃ and 82.1 ± 4.5% RH, group C ambient-temperature storage with plasma treatment with 25.3 ± 2.2℃ and 90.0 ± 2.8% RH, and group D had cold storage with 5.7 ± 1.0℃ and 93.4 ± 6.5% RH. Results: As a result of citrus surface sterilization by plasma treatment, treatment groups A and C together showed an average of 16.1 CFU/mL of mold colonies, while control groups B and D showed an average of 2.2ⅹ102 CFU/mL or approximately 13 times greater than the treatment groups. Regarding the mean concentration of aerobic bacteria colonies, the treatment groups (A and C) and control groups (B and D) showed an average of 7.1 CFU/mL and 1.9ⅹ103 CFU/mL, respectively. This is approximately a 270-fold difference in the concentration of pathogen colonies between treatment and control groups. Conclusions: The results showed the potential of nonthermal plasma treatment for citrus storage in enhancing storage duration and quality preservation.
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Detection of Spinach Juice Residues on Stainless Steel Surfaces Using VNIR Hyperspectral Images
서영욱,모창연,임종국,이아영,김밝금,장재경,김기영 한국농업기계학회 2021 Journal of Biosystems Engineering Vol.46 No.2
Purpose Spinach is one of the most commonly consumed fresh-cut vegetables. Hygiene and sanitation in automated processing facilities have been an important issue. This research aimed to develop a line-scan hyperspectral imaging technique for detecting spinach droplets on a stainless steel surface. Methods The hyperspectral imaging system uses UV-A (365 nm) light sources to obtain 3D hypercube data with spatial and spectral data in the visible and near-infrared (VNIR) region ranging from 400 to 1000 nm. Freshly made 100% spinach juice and distilled water were used to prepare juice dilutions at 20%, 10%, 5%, 2%, and 1% juice. For each of the six juice concentrations, fifteen droplets were placed on a stainless steel sheet, and VNIR hyperspectral image data was collected for the 6 × 15 array of droplets on the metal sheet. To detect and classify the diluted droplets on the spectral domain, three classification models (support vector machine, partial least square discriminant analysis, and random forest) and six pre-processing methods were implemented. Results Among them, support vector machine (SVM) showed the best classification accuracy with A = 0.95. Besides, the classification model used to reduce the number of wavelengths and calculation time, the genetic algorithm (GA) applied to the SVM showed the most accurate result as A = 0.90 among three methods. Conclusions The developed classifier demonstrated potential for detecting and classifying spinach juice droplets on the surface of stainless steel sheet metal.
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