• Journal of the Korean Applied Science and Technology
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• v.18 no.3
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• pp.197-213
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• 2001

Conjugate trienoic acids (CTA) occurred in triacylglycerols (TGs) of the seed oils of Trichosanthes kirilowii, Momordica charantia and Aleurites fordii, and they were easily converted to their methyl esters in a mixture of sodium methoxide-methanol without any structural destruction. The main fatty acids in triacylglycerol (TG) fraction of the seed oils of Trichosanthes kirilowii are $C_{18:2{\omega}6}$ (32.2 mol %), $C_{18:3{\;}9c.11t,13c}$ (38.0 mol %) and $C_{18:1{\omega}9}$ (11.8 mol %), followed with $C_{16:0}$ (4.8 mol %) and $C_{18:0}$ (3.1 mol %). The TG fraction was resolved into 20 TG molecular species according to the partition number (PN) by reversed-phase (RP)-HPLC. The main TG species were $DT_{c2}$, $MDT_{c}$ and $D_{2}T_{c}$, of which amounts reached 63 mol % of total TG molecular species. The TG sample was fractionated into 11 fractions according to the number of double bond in the molecule by $Ag^{+}-HPLC$ and the species of $DT_{c2}$, $MDT_{c}$ and $D_{2}T_{c}$ were also eluted as main components. The TG species containing CTA showed unusual behaviours in the order of elution by HPLC ; first, TG moleular species of $DT_{c2}$ (D; dienoic acid, $T_{c}$; punicic acid, $T_{ci}$; ${\alpha}-eleostearic$ acid, M ; monoenoic acid, $S_{t}$; stearic acid) was eluted earlier than $Mt_{c2}$, although they have the same PN number of 40, and, secondly, the species of $DT_{ci2}$ with eight double bonds was eluted earlier than that of $D_2T_{ci}$ with seven double bonds. Intact TG of the seed oils of Momordica charantia contained mainly fatty acids such as $C_{18:3{\omega}9c,11t,13t}$ (57.7 mol %), $C_{18:1{\omega}9}$ (17.4 mol %), $C_{18:0}$ (12.3 mol %) and $C_{18:2{\omega}6}$ (10.6 mol %), and was classified into 13 fractions by RP-HPLC. The main TG species were as follows ; $MT_{ci2}$ [$(C_{18:1{\omega}9})(C_{18:3\;9c,11t,13t})_{2}$, 39.1 mol %] and $S_{t}T_{ci2}$ [$(C_{18:0})(C_{18:3\;9c,11t,13t})_2$, 33.9 mol %] comprising about 73 mol % of total TG species, accompanied by $DT_{ci2}$ [$(C_{18:2{\omega}6})(C_{18:3\;9c,11t,13t})_{2}$, 7.3 mol %], $D_{2}T_{ci}$ [$ (C_{18:2{\omega}6})_{2}(C_{18:3\;9c,11t,13t})$, 3.6 mol %] and $MDT_{ci}$ [$(C_{18:1{\omega}9})(C_{18:2{\omega}6})(C_{18:3\;9c,11t,13t})$, 3.5 mol %]. Simple TG species of $T_{ci3}$ [$(C_{18:3\;9c,11t,13t})_3]$ was present in a small amount of 1.4 mol %, but other simple TG species were not detected. The TG was also resolved into 11 fractions according to the number of double bond by $Ag^{+}-HPLC$, and the species were mainly occupied by $MT_{ci2}$ [$(C_{18:1{\omega}9})(C_{18:3\;9c,11t,13t})_{2}$, 39.4 mol %] and $S_tT-{ci2}$ [$(C_{18:0})(C_{18:3\;9c,11t,13t})_{2}$, 35.4 mol %] $DT_{ci2}$ species with eight double bonds was also developed faster than $D_2T_{ci}$ one with seven double bonds as indicated in the analysis of TG of the seed oils of T. kirilowii, and $MT_{ci2}$ species with cis, trans, trans-configurated double bond was eluted earlier than $MT_{c2}$ species with cis, trans, cis-configurated double bond. The main components of fatty acid in total TG fraction isolated from the seed oils of of Aleurites fordii were in the following order ; $C_{18:3\;9c,11t,13t}$ (81.2 mol %)> $C_{18:2{\omega}6}$ (8.5 mol %)> $C_{18:1{\omega}9}$ (5.4 mol %)$. With resolution of the TG by RP-HPLC, eight fractions such as $T_{ci3}$, $Dt_{ci2}$, $D_{2}T_{ci}$, $MT_{ci2}$, $PT_{ci2}$ (P; palmitic acid), $PMT_{ci}$, $PDT_{ci}$ and $S_{t}T_{ci2}$ ($S_{t}$; stearic acid) were isolated, respectively. TG species of $T_{ci3}$ [$(C_{18:3\;9c,11t,13t})_{3}$, 54.2 mol %], $DT_{ci2}$ [$(C_{18:2{\omega}6})(C_{18:3\;9c,11t,13t})_{2}$, 15.0 mol %] and $MT_{ci2}$ [$(C_{18:1{\omega}9})(C_{18:3 9c,11t,13t})_{2}$, 14.8 mol %] were present as main species.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Food Hygiene and Safety
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• v.34 no.3
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• pp.227-234
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• 2019

An analytical method was developed for the determination of oxytetracycline in agricultural products using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method by liquid chromatography-tandem mass spectrometry (LC-MS/MS). After the samples were extracted with methanol, the extracts were adjusted to pH 4 by formic acid and sodium chloride was added to remove water. Dispersive solid phase extraction (d-SPE) cleanup was carried out using $MgSO_4$ (anhydrous magnesium sulfate), PSA (primary secondary amine), $C_{18}$ (octadecyl) and GCB (graphitized carbon black). The analytes were quantified and confirmed with LC-MS/MS using ESI (electrospray ionization) in positive ion MRM (multiple reaction monitoring) mode. The matrix-matched calibration curves were constructed using six levels ($0.001{\sim}0.25{\mu}g/mL$) and coefficient of determination ($r^2$) was above 0.99. Recovery results at three concentrations (LOQ, $10{\times}LOQ$, and $50{\times}LOQ$, n=5) were from 80.0 to 108.2% with relative standard deviations (RSDs) less than of 11.4%. For inter-laboratory validation, the average recovery was in the range of 83.5~103.2% and the coefficient of variation (CV) was below 14.1%. All results satisfied the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food Safety Evaluation Department guidelines (2016). The proposed analytical method was accurate, effective and sensitive for oxytetracycline determination in agricultural commodities. This study could be useful for safety management of oxytetracycline residues in agricultural products.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.157-166
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• 2015

Purpose : Cyber-Knife tumor tracking system, based on the correlation relationship between the position of a tumor which moves in response to the real time respiratory cycle signal and respiration was obtained by the LED marker attached to the outside of the patient, the location of the tumor to predict in advance, the movement of the tumor in synchronization with the therapeutic device to track real-time tumor, is a system for treating. The purpose of this study, in the cyber knife tumor tracking radiation therapy, trying to evaluate the accuracy of tumor tracking radiation therapy system due to the change in the form of unpredictable sudden breathing due to cough and sleep. Materials and Methods : Breathing Log files that were used in the study, based on the Respiratory gating radiotherapy and Cyber-knife tracking radiosurgery breathing Log files of patients who received herein, measured using the Log files in the form of a Sinusoidal pattern and Sudden change pattern. it has been reconstituted as possible. Enter the reconstructed respiratory Log file cyber knife dynamic chest Phantom, so that it is possible to implement a motion due to respiration, add manufacturing the driving apparatus of the existing dynamic chest Phantom, Phantom the form of respiration we have developed a program that can be applied to. Movement of the phantom inside the target (Ball cube target) was driven by the displacement of three sizes of according to the size of the respiratory vertical (Superior-Inferior) direction to the 5 mm, 10 mm, 20 mm. Insert crosses two EBT3 films in phantom inside the target in response to changes in the target movement, the End-to-End (E2E) test provided in Cyber-Knife manufacturer depending on the form of the breathing five times each. It was determined by carrying. Accuracy of tumor tracking system is indicated by the target error by analyzing the inserted film, additional E2E test is analyzed by measuring the correlation error while being advanced. Results : If the target error is a sine curve breathing form, the size of the target of the movement is in response to the 5 mm, 10 mm, 20 mm, respectively, of the average $1.14{\pm}0.13mm$, $1.05{\pm}0.20mm$, with $2.37{\pm}0.17mm$, suddenly for it is variations in breathing, respective average $1.87{\pm}0.19mm$, $2.15{\pm}0.21mm$, and analyzed with $2.44{\pm}0.26mm$. If the correlation error can be defined by the length of the displacement vector in the target track is a sinusoidal breathing mode, the size of the target of the movement in response to 5 mm, 10 mm, 20 mm, respective average $0.84{\pm}0.01mm$, $0.70{\pm}0.13mm$, with $1.63{\pm}0.10mm$, if it is a variant of sudden breathing respective average $0.97{\pm}0.06mm$, $1.44{\pm}0.11mm$, and analyzed with $1.98{\pm}0.10mm$. The larger the correlation error values in both the both the respiratory form, the target error value is large. If the motion size of the target of the sine curve breathing form is greater than or equal to 20 mm, was measured at 1.5 mm or more is a recommendation value of both cyber knife manufacturer of both error value. Conclusion : There is a tendency that the correlation error value between about target error value magnitude of the target motion is large is increased, the error value becomes large in variation of rapid respiration than breathing the form of a sine curve. The more the shape of the breathing large movements regular shape of sine curves target accuracy of the tumor tracking system can be judged to be reduced. Using the algorithm of Cyber-Knife tumor tracking system, when there is a change in the sudden unpredictable respiratory due patient coughing during treatment enforcement is to stop the treatment, it is assumed to carry out the internal target validation process again, it is necessary to readjust the form of respiration. Patients under treatment is determined to be able to improve the treatment of accuracy to induce the observed form of regular breathing and put like to see the goggles monitor capable of the respiratory form of the person.