• Food Science and Biotechnology
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• v.18 no.3
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• pp.694-699
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• 2009

A multiplex polymerase chain reaction (PCR) method was developed for the detection of 4 events of genetically modified (GM) soybean. The event-specific primers were designed from 4 events of GM soybean (RRS, A2704-12, DP356043-5, and MON89788). The lectin was used as an endogenous reference gene of soybean in the PCR detection. The primer pair YjLec-4-F/R producing 100 bp amplicon was used to amplify the lectin gene and no amplified product was observed in any of the 9 different plants used as templates. This multiplex PCR method allowed for the detection of event-specific targets in a genomic DNA mixture of up to 1% GM soybean mixture containing RRS, A2704-12, DP356043-5, and MON89788. In this study, 20 soybean products obtained from commercial food markets were analyzed by the multiplex PCR. As a result, 6 samples contained RRS. These results indicate that this multiplex PCR method could be a useful tool for monitoring GM soybean.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.401-406
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• 2008

Fall event detection is one of the most common problems for elderly people, especially those living alone because falls result in serious injuries such as joint dislocations, fractures, severe head injuries or even death. In order to prevent falls or fall-related injuries, several previous methods based on video sensor showed low fall detection rates in recent years. To improve this problem and outperform the system performance, this paper presented a novel approach for fall event detection in the elderly using a subtraction between successive difference images and temporal templates in real time environment. The proposed algorithm obtained the successful detection rate of 96.43% and the low false positive rate of 3.125% even though the low-quality video sequences are obtained by a USB PC camera sensor. The experimental results have shown very promising performance in terms of high detection rate and low false positive rate.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.83-93
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• 2022

Research is underway to solve the traffic problem by using crowdsourcing, where drivers use their mobile devices to provide traffic information. If it is used for traffic event detection through crowdsourcing, the task of collecting related data is reduced, which lowers time cost and increases accuracy. In this paper, we propose a scheme to collect traffic-related data using crowdsourcing and to detect events affecting traffic through this. The proposed scheme uses machine learning algorithms for processing large amounts of data to determine the event type of the collected data. In addition, to find out the location where the event occurs, a keyword indicating the location is extracted from the collected data, and the administrative area of the keyword is returned. In this way, it is possible to resolve a location that is broadly defined in the existing location information or incorrect location information. Various performance evaluations are performed to prove the superiority and feasibility of the proposed scheme.

• Korean Journal of Clinical Pharmacy
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• v.32 no.1
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• pp.1-7
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• 2022

Objective: The purpose of this study was to detect signals of Adverse Events (AEs) after varenicline treatment using spontaneous AEs reporting system in Korea. Methods: This study was conducted by Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD) reported from January 2013 to December 2017 through Korea Adverse Event Reporting System. Signals of varenicline that satisfied the data-mining indices, proportional reporting ratio, reporting odds ratio and information component were defined. The detected signals were checked whether they included in drug labels in South Korea and United States of America (USA). Results: A total number of drug AE reports associated with all drugs in the KIDS-KD reported between January 2013 and December 2017 was 2,665,429. Among them, the number of AE reports associated with varenicline was 1,398. Eighteen meaningful signals of varenicline were detected that satisfied with the criteria of data-mining indices. Finally, two signals such as hypotonia, incorrected dose administered were not included in the drug labels. Conclusion: New AE signals of varenicline that were not listed on the drug labels in South Korea and USA were detected. However, further pharmacoepidemiological studies such as randomized controlled trial are needed to evaluate the causality of the signals of varenicline.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.139-147
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• 2021

This paper introduces and implements a Sound Event Detection (SED) system based on weakly-supervised learning where only part of the data is labeled, and analyzes the effect of parameters. The SED system estimates the classes and onset/offset times of events in the acoustic signal. In order to train the model, all information on the event class and onset/offset times must be provided. Unfortunately, the onset/offset times are hard to be labeled exactly. Therefore, in the weakly-supervised task, the SED model is trained by "strongly labeled data" including the event class and activations, "weakly labeled data" including the event class, and "unlabeled data" without any label. Recently, the SED systems using the mean-teacher model are widely used for the task with several parameters. These parameters should be chosen carefully because they may affect the performance. In this paper, performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4. Effects and the optimal values of the parameters were discussed.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.7-18
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• 2017

Recently, the number of SNS user has rapidly increased due to smart device industry development and also the amount of generated data is exponentially increasing. In the twitter, Text data generated by user is a key issue to research because it involves events, accidents, reputations of products, and brand images. Twitter has become a channel for users to receive and exchange information. An important characteristic of Twitter is its realtime. Earthquakes, floods and suicides event among the various events should be analyzed rapidly for immediately applying to events. It is necessary to collect tweets related to the event in order to analyze the events. But it is difficult to find all tweets related to the event using normal keywords. In order to solve such a mentioned above, this paper proposes A Generation and Matching Method of Normal-Transient Dictionary for realtime topic detection. Normal dictionaries consist of general keywords(event: suicide-death-loop, death, die, hang oneself, etc) related to events. Whereas transient dictionaries consist of transient keywords(event: suicide-names and information of celebrities, information of social issues) related to events. Experimental results show that matching method using two dictionary finds more tweets related to the event than a simple keyword search.

• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.31-38
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• 2014

This paper reveals continuity of related events which are occurred and changing from moment to moment accident/events collected from various social media channels. Among them, we especially define the events which have big social influence as "issue event" and investigate the type and characteristics of continuous issue event for each domain. We also introduce a automatic issue detection system in social media text. Based on the extracted issue event results in a particular domain, we analyse the continuity of those events by illustrating in time and place-axis. Furthermore, we identify the relationship between social media in terms of issue events propagation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.11-17
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• 2008

Typical wireless sensor networks used in structural monitoring are continuous types wherein data transmission is progressive at all time that may include irrelevant and insignificant data and information. Continuous types of wireless monitoring systems often pose problems of handling large-sized data that may deteriorate the performance of the system. The proposed method is to suggest an event-triggered monitoring system that captures and transmits relevant data only. An error signal generated by the Principal Components Analysis (PCA) is utilized as an index for event detection and selective data transmission. With this new monitoring scheme, the remote server is relieved of unwanted data by receiving only relevant information from the wireless sensor networks. The performance of the proposed scheme was verified with simulation studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.990-992
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• 2007

The purpose of this study is to develop a practical gait-event detection system which is necessary for the FES (functional electrical stimulation) control of locomotion in paralyzed patients. The system is comprised of a sensor board and an event recognition algorithm. We focused on the practicality improvement of the system through 1) using accelerometer to get the angle of shank and dispensing with the foot-switches having limitation in indoor or barefoot usage and 2) using a rule-base instead of threshold to determine the heel-off/heel-strike events corresponding the stimulation on/off timing. The sensor signals are transmitted through RF communication and gait-events was detected using the peaks in shank angle. The system could detect two critical gait-events in all five paralyzed patients. The standard deviation of the gait events time from the peaks were smaller when 1.5Hz cutoff frequency was used in the derivation of the shank angle from the acceleration signals.