• Korean Journal of Environmental Agriculture
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• v.41 no.2
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• pp.82-94
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• 2022

BACKGROUND: Spiropidion and its metabolite are tetramic acid insecticide and require the establishment of an official analysis method for the safety management because they are newly registered in Korea. Therefore, this study was to determine the analysis method of residual spiropidion and its metabolite for the five representative agricultural products. METHODS AND RESULTS: Three QuEChERS methods (original, AOAC, and EN method) were applied to optimize the extraction method, and the EN method was finally selected by comparing the recovery test and matrix effect results. Various adsorbent agents were applied to establish the clean up method. As a result, the recovery of spiropidion was reduced when using the dispersive-SPE method with MgSO₄, primary secondary amine (PSA), graphitized carbon black (GCB) and octadecyl (C₁₈) in soybean. Color interference was minimized by selecting the case including GCB and C18 in addition to MgSO₄. This method was established as the final analysis method. LC-MS/MS was used for the analysis by considering the selectivity and sensitivity of the target pesticide and the analysis was performed in MRM mode. The results of the recovery test using the established analysis method and inter laboratory validation showed a valid range of 79.4-108.4%, with relative standard deviation and coefficient of variation were less than 7.2% and 14.4%, respectively. CONCLUSION(S): Spiropidion and its metabolite could be analyzed with a modified QuEChERS method, and the established method would be widely available to ensure the safety of residual insecticides in Korea.

• 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.

• Korean Journal of Food Science and Technology
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• v.45 no.2
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• pp.148-155
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• 2013

Sulfoxaflor is a new active ingredient within the sulfoximine insecticide class that acts via a unique interaction with the nicotinic receptor. The MRLs (maximun residue limit) of sulfoxaflor in apple and pear are set at 0.4 mg/kg and that in pepper is set at 0.5 mg/kg. The purpose of this study was to develop an analytical method for the determination of sulfoxaflor residues in agricultural commodities using HPLC-UVD and LC-MS. The analysis of sulfoxaflor was performed by reverse phase-HPLC using an UV detector. Acetone and methanol were used for the extraction and aminopropyl ($NH_2$) cartridge was used for the clean-up in the samples. Recovery experiments were conducted on 7 representative agricultural products to validate the analytical method. The recoveries of the proposed method ranged from 82.8% to 108.2% and relative standard deviations were less than 10%. Finally, LC-MS with selected ion monitoring was also applied to confirm the suspected residues of sulfoxaflor in agricultural commodities.

• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.142-149
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• 2011

An isocratic high performance liquid chromatography (HPLC) method for routine analysis of deoxynivalenol in noodles was validated and estimated the measurement uncertainty. Noodles (dried noodle and ramyeon) were analyzed by HPLC-ultraviolet detection using immunoaffinity column for clean-up. The limits of detection (LOD) and quantification (LOQ) were 7.5 ${\mu}g$/kg and 18.8 ${\mu}g$/kg, respectively. The calibration curve showed a good linearity, with correlation coefficients $r^2$ of 0.9999 in the concentration range from 20 to 500 ${\mu}g$/kg. Recoveries and Repeatabilities expressed as coefficients of variation (CV) spiked with 200 and 500 ${\mu}g$/kg were $82{\pm}2.7%$ and $87{\pm}1.3%$% in dried noodle, and $97{\pm}1.6%$ and $91{\pm}12.0%$ in ramyeon, respectively. The uncertainty sources in measurement process were identified as sample weight, final volume, and sample concentration in extraction volume as well as components such as standard stock solution, working standard solution, 5 standard solutions, calibration curve, matrix, and instrument. Deoxynivalenol concentration and expanded uncertainty in two matrixes spiked with 200 ${\mu}g$/kg and 500 ${\mu}g$/kg were estimated to be $163.8{\pm}52.1$ and $435.2{\pm}91.6\;{\mu}g$/kg for dried noodle, and $194.3{\pm}33.0$ and $453.2{\pm}91.1\;{\mu}g$/kg for ramyeon using a coverage factor of two which gives a level of statistical confidence with approximately 95%. The most influential component among uncertainty sources was the recovery of matrix, followed by calibration curve.

• Journal of Distribution Research
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• v.17 no.1
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• pp.37-63
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• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.