• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.639-642
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• 2013

In this paper, proposed NED(nuclear event detectors) for detection of a transient radiation. Nuclear event detector was blocked of power temporary for defence of critical damage at a electric device when a induced transient radiation. Conventional NED consist of BJT, resistors and capacitors. The NED supply voltage of 5V and MCM(Multi Chip Module) structures. The proposed NED were designed for low supply voltage using 0.18um CMOS process. The response time of proposed NED was 34.8ns. In addition, pulse radiation experiments using a electron beam accelerator, the output signal has occurred.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.83.3-84
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• 2017

On 2016 April $13^{th}$ the Jovian satellite Ganymede occulted a $7^{th}$ magnitude star. The predicted occultation track (occultation shadow) crossed the Northern Pacific Ocean, Japan, and South Korea. Hence, it was a very favorable event due to the star brightness in order to be accessible for small-aperture telescopes as well. While no other similar event is expected for the next 10 years, only two occultation events are reported in the literature in the past, from Earth in 1972 and from Voyager, in large disagreement in respect to the atmospheric detection. However, evidence of an exosphere around Ganymede was inferred through H Lyman alpha emission detected by Galileo UVS, through HST/GHRS detection of far-UV atomic O airglow emissions, signature of dissociated molecular oxygen. We organized a short-notice international coordinated occultation monitoring network with the aim to search for a signature of Ganymede's exosphere in the occultation light-curve by using facilities on Mauna Kea (NASA-IRTF) and Sobaeksan Optical Astronomy Observatory (SOAO) in South Korea.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.373-376
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• 2014

최근 모바일 기기 및 무선기기의 발달로 인하여 센서 네트워크가 다양한 분야에서 응용되고 있다. 따라서 센서에서 실시간으로 발생하는 스트리밍 데이터에서 이벤트를 감지하고 분석하는 것은 중요한 연구 분야로 부각되고 있다. 단순 이벤트의 발생 조건을 빠르게 판별하기 위해 비트맵 인덱스 기반 복합 이벤트 검출 기법 등 여러 가지 방법들이 사용되고 있지만, 아직까지 이기종 센서에서 발생하는 각기 다른 형태의 데이터를 융합하여 이벤트를 검출하는 복합 이벤트 처리에 대한 연구는 미비한 실정이다. 본 논문에서는 각기 다른 형태를 가지는 스트리밍 데이터에 멤버쉽 함수를 적용하여 퍼지화 함으로서 이기종 센서에서 발생하는 데이터를 융합 처리가능하며, Quine-Mccluskey 감축기법을 통하여 규칙의 신뢰도 및 속도가 향상된 의사결정을 하는 고속 이벤트 탐지기법을 제안한다.