• Journal of Pharmaceutical Investigation
• /
• v.41 no.5
• /
• pp.317-322
• /
• 2011

Sertraline HCl, (1S-cis)-4-(3, 4-dichloro-phenyl)-1, 2, 3, 4-tetrahydro-N-methyl-l-naphthalenamine hydrochloride, is a potent and selective serotonin reuptake inhibitor which is used in the treatment of depression and obsessivecompulsive disorders. The purpose of the present study was to evaluate the bioequivalence of two sertraline HCl tablets, Traline tablet (Myungin Pharm. Co. Ltd.) and Zoloft$^{(R)}$ tablet (Pfizer Inc.), according to the guidelines of the Korea Food and Drug Administration (KFDA). The in vitro release of sertraline from the two sertraline HCl formulations was tested using KP VIII Apparatus II method with various dissolution media. Twenty four healthy Korean male volunteers, $23.50{\pm}1.74$ years in age and $64.09{\pm}7.10\;kg$ in body weight, were divided into two groups and a randomized $2{\times}2$ crossover study was employed. After a single tablet containing 50 mg as sertraline HCl was orally administered, blood samples were taken at predetermined time intervals and the concentrations of sertraline in serum were determined using an online columnswitching HPLC method with UV/Vis detection. The dissolution profiles of two formulations were similar in all tested dissolution media. The pharmacokinetic parameters such as $AUC_t$, $C_{max}$ and $T_{max}$ were calculated, and computer programs (Equiv Test and K-BE Test) were utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$, $C_{max}$ and un-transformed $T_{max}$. The results showed that the differences between two formulations based on the reference drug, Zoloft$^{(R)}$ tablet, were 0.04, 3.26 and -1.29% for $AUC_t$, $C_{max}$, and $T_{max}$, respectively. There were no sequence effects between two formulations in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log0.8 to log1.25. Thus, the criteria of the KFDA bioequivalence guideline were satisfied, indicating Traline tablet was bioequivalent to Zoloft$^{(R)}$ tablet.

• Journal of Pharmaceutical Investigation
• /
• v.41 no.2
• /
• pp.117-123
• /
• 2011

Loxoprofen sodium, a 2-phenylpropionate non-steroidal anti-inflammatory drug (NSAID), has marked analgesic and antipyretic activities and relatively weak gastrointestinal ulcerogenicity. The purpose of the present study was to evaluate the bioequivalence of two loxoprofen sodium tablets, Hana loxoprofen sodium tablet (Hana Pharm. Co., Ltd.) and Dongwha Loxonin$^{(R)}$ tablet (Dongwha Pharm. Co., Ltd.), according to the guidelines of the Korea Food and Drug Administration (KFDA). The in vitro release of loxoprofen from the two loxoprofen sodium formulations was tested using KP IX Apparatus II method with various dissolution media. Twenty four healthy Korean male volunteers, $22.83{\pm}1.862$ years in age and $69.92{\pm}9.14$ kg in body weight, were divided into two groups and a randomized $2{\times}2$ crossover study was employed. After a single tablet containing 60 mg as loxoprofen sodium was orally administered, blood samples were taken at predetermined time intervals and the concentrations of loxoprofen in serum were determined using a online column-switching HPLC method with UV/Vis detection. The dissolution profiles of two formulations were similar in all tested dissolution media. The pharmacokinetic parameters such as $AUC^t$, $C_{max}$ and $T_{max}$ were calculated, and computer programs (Equiv Test and K-BE Test 2002) were utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$, $C_{max}$ and un-transformed $T_{max}$. The results showed that the differences between two formulations based on the reference drug, Dongwha Loxonin$^{(R)}$ tablet, were 2.03, 2.99 and -9.49% for $AUC_t$, $C_{max}$, and $T_{max}$, respectively. There were no sequence effects between two formulations in these parameters. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log0.8 to log1.25 (e.g., log0.9831~log1.0535 and log0.9455~log1.1386 for $AUC_t$ and $C_{max}$, respectively). Thus, the criteria of the KFDA bioequivalence guideline were satisfied, indicating Hana loxoprofen sodium tablet was bioequivalent to Dongwha Loxonin$^{(R)}$ tablet.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.3
• /
• pp.577-582
• /
• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Korean Journal of Remote Sensing
• /
• v.38 no.3
• /
• pp.237-250
• /
• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.161-177
• /
• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.1
• /
• pp.569-574
• /
• 2014

We developed and evaluated a fluorescence-based optical DO sensor (OS-100, Global Optical Communication Ltd., Korea) for long-term monitoring of the dissolved oxygen concentration in waste water treatment. Fluorescent sensing membrane containing $Ru(Dpp)_3{^{2+}}$ (tris(4,7diphenyl-1, 10-phenanthroline) ruthenium(II)) was prepared with GA sol-gel matrix and coated on a quartz plate by sprayed method. Properties of sensor film exhibit deviation about ${\pm}1%$ under wide range of DO concentration from 3 to 10. The developed optical DO sensor was actually mounted in waste water from dyeing industry and successfully applied for on-line DO monitoring. Online monitoring results showed the changes of DO concentrations in wastewater treatment processes with accuracy better than ${\pm}2%$ during the 6 months measurements period in vicious environmental conditions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.28 no.2
• /
• pp.96-105
• /
• 2017

Objectives: The purpose of this study is to investigate the professional knowledge and perceptions of the early screening of Autism Spectrum Disorder (ASD) in practitioners who have contact with patients with ASD. Methods: A survey was carried out among 674 practitioners in total, where practitioners are defined as those who work at primary medical centers, public institutions, educational institutions and treatment institutions. The survey was carried out both online and offline, and it mainly focused on 1) knowledge about ASD symptoms, 2) knowledge about the early screening of ASD, 3) measures taken after ASD detection, 4) thoughts on the development of early screening tools for ASD, and 5) the current status of ASD treatment. The data collected were analyzed through descriptive statistics, analysis of frequency and cross tabulation analysis using SPSS WIN 22.0. Results: The results of this study suggest that the practitioners were not aware of the exact symptoms of ASD and their professional knowledge and the environment for early screening were insufficient. Furthermore, very few and inappropriate measures were taken after the detection of ASD. In addition, there was a high demand for early ASD screening tools to be used on site and, regarding treatment, the significance of the implementation of evidence based treatments as well as the continuity of relevant research came to the fore. Conclusion: It seems that there is a lack of knowledge and perception of the early screening of ASD and that education and training among practitioners is urgently required. This issue is discussed in more detail in the paper.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.10
• /
• pp.752-760
• /
• 2009

Emerging peer-to-peer systems benefit from the large amount of resources provided by many peers. However, many peer-to-peer systems or applications suffer from malicious peers and it is not guaranteed that peers are always online. Micropayment systems are accounting and charging mechanism for buying services, so we can apply them to solve these problems. In the past the majority of micropayment system uses a centralized broker but the problem with most existing micropayment system is a heavy load on the broker. For instance, when an owner of the coin is offline, the broker delegates the owner and handles payment messages. It occurs frequently because of characteristic of peer-to-peer system and is another load of the broker. In this paper we introduce DPay, a peer-to-peer micropayment system that uses distributed hash table (DHT) for storing encrypted payment messages and increases scalability and reduces the load of broker by removing downtime protocol. We show the idea of real-time double spending detection in DPay and report the results of several evaluations in order to compare DPay and other payment scheme. In simulation result, the load of broker in DPay is reduced by 30% on average of other previous payment scheme. We expect that DPay can apply various peer-to-peer systems because it provides a real-time double spending detection and stores more secure payment messages.

• Analytical Science and Technology
• /
• v.31 no.1
• /
• pp.39-46
• /
• 2018

Carbaryl and seven organophosphorous pesticides were analyzed simultaneously using on-line solid phase extraction (on-line SPE) coupled with liquid chromatography tandem mass spectrometry (LC/MS/MS). The target pesticides are Carbaryl, Methyl demeton, Fenitrothion, Malathion, Parathion, Phenthoate, Diazinon, and EPN. This method includes the direct injection of $500{\mu}L$ in the water sample, a 15 min separation period using a rapid resolution liquid chromatography system with on-line SPE, and detection through electrospray ionization (ESI) positive mode. The percentage of recovery of all pesticides ranged from 85.3 % to 100 %. This method showed an accuracy of ${\geq}90.0%$, possessing limits of detection and quantification within 0.05 to $0.28{\mu}g/L$ and 0.16 to $0.89{\mu}g/L$, respectively. The correlation coefficients (r) for the calibration curves within a range of 0.5 to $8.0{\mu}g/L$ were higher than 0.99. The evaluation results showed the efficacy of the method for all contents, and no pesticides were detected in the water quality sample.

• The Journal of the Korea Contents Association
• /
• v.12 no.2
• /
• pp.40-51
• /
• 2012

Recently, many users discuss and argue with others using replying comments. This implies that a series of comments can be a new source of information since various opinions can be appeared in the dispute. It is important to understand the implicit dispute structure immanent in the comment set. In this paper, we examine the characteristics of disputes using replying comments in the Internet forum sites using a set of test articles with the comments collected from SketicalLeft and Agora, which are famous Internet forum sites in Korea. And we propose a new method for detecting and visualizing the dispute sections and relations from a large set of replying comments. To show the performance of our method, we measured precision, recall, and F-measure. According to the experimental results, the F-measures of the detection of the comments in dispute are about 0.84 (SketpcialLeft) and 0.83 (Agora); those of the detection of the commenter pairs in dispute are 0.75 (SketpcialLeft) and 0.82 (Agora), respectively. Since our method exploits the temporal order of commenters to detect the disputes, it is not dependent on the host language nor on the typos in comments. Also, our method can help the readers to grasp the structure of controversy hidden in the comment set through the visualized view.