• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.11a
• /
• pp.645-646
• /
• 2008

• Journal of KIISE:Databases
• /
• v.35 no.2
• /
• pp.143-154
• /
• 2008

RFID events are large volume of stream data which come out continuously. Many studies have been done to detect a business event in RFID stream. However, the existing methods have many problems which increase unnecessary operations when business events do not satisfy minimum conditions. In this paper, to remove unnecessary operations, we define the minimum condition of business events and propose an efficient method that detects business events only when the minimum condition is satisfied. To check the minimum condition of business events, we register business queries in a query index. We detect business events using the query index and bitmap. It is shown through various experiment that the proposed method outperforms the existing methods.

• Journal of information and communication convergence engineering
• /
• v.15 no.4
• /
• pp.244-249
• /
• 2017

Wearable sensor-based gait analysis has been widely conducted to analyze various aspects of human ambulation abilities under the free-living condition. However, there have been few research efforts on using wearable sensors to analyze human walking on an unstable surface such as on a ship during a sea voyage. Since the motion of a ship on the unstable sea surface imposes significant differences in walking strategies, investigation is suggested to find better performing wearable sensor-based gait analysis algorithms on this unstable environment. This study aimed to compare two representative gait event algorithms including time domain and frequency domain analyses for detecting heel strike on an unstable platform. As results, although two methods did not miss any heel strike, the frequency domain analysis method perform better when comparing heel strike timing. The finding suggests that the frequency analysis is recommended to efficiently detect gait event in the unstable walking environment.

• Earthquakes and Structures
• /
• v.7 no.4
• /
• pp.449-462
• /
• 2014

Seismically induced structural damage, as well as any damage caused by a natural catastrophic event, covers a wide area. This suggests to supervise the event consequences by vision tools. This paper reports the evolution from the results obtained by the project RADATT (RApid Damage Assessment Telematics Tool) funded by the European Commission within FP4. The aim was to supply a rapid and reliable damage detector/estimator for an area where a catastrophic event had occurred. Here, a general open-source methodology for the detection and the estimation of the damage caused by natural catastrophes is developed. The suitable available hazard and vulnerability data and satellite pictures covering the area of interest represent the required bits of information for updated telematics tools able to manage it. As a result the global damage is detected by the simple use of open source software. A case-study to a highly dense agglomerate of buildings is discussed in order to provide the main details of the proposed methodology.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.137-142
• /
• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Proceedings of the KSR Conference
• /
• 2009.05a
• /
• pp.1984-1988
• /
• 2009

MRT has various surveillance systems for passenger's safety and facility protection which are consisted of fire detection, trespasser observation and so on. However, these systems are not closely related each other because it is designed just for its own purpose, so it could be make wrong decision to surveillance system without important information to determine an accident or disaster. For more accurate event detection, surveillance system needs total situation-aware method using complementary data. This study introduces context-aware system for complex and accurate event detection. Therefore, we apply context-aware system to MRT surveillance system, selecting context-aware parameters and appling them to it.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.10
• /
• pp.447-454
• /
• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.121-128
• /
• 2017

In this paper, we propose a dual-channel acoustic event detection (AED) method using nonnegative tensor factorization (NTF) and hidden Markov model (HMM) in order to improve detection accuracy of AED in multisource environments. The proposed method first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique applied to dual-channel input signals. After that, an HMM-based likelihood ratio test is carried out to verify the detected events by using channel gains. The detection accuracy of the proposed method is measured by F-measures under 9 different multisource conditions. Then, it is also compared with those of conventional AED methods such as Gaussian mixture model and nonnegative matrix factorization. It is shown from the experiments that the proposed method outperforms the convectional methods under all the multisource conditions.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.109-118
• /
• 2020

Recently, people are spending a lot of time inside their homes because of various diseases. It is difficult to ask others for help in the case of a single-person household that is injured in the house or infected with a disease and needs help from others. In this study, an algorithm is proposed to detect emergency event, which are situations in which single-person households need help from others, such as injuries or disease infections, in their homes. It proposes vision pattern detection algorithms using home CCTVs, audio pattern detection algorithms using artificial intelligence speakers, activity pattern detection algorithms using acceleration sensors in smartphones, and dust pattern detection algorithms using air purifiers. However, if it is difficult to use due to security issues of home CCTVs, it proposes a fusion method combining audio, activity and dust pattern sensors. Each algorithm collected data through YouTube and experiments to measure accuracy.

• Journal of Plant Biotechnology
• /
• v.47 no.4
• /
• pp.309-315
• /
• 2020

Advances in biotechnology have led to progress in crop genetic engineering to improve agricultural productivity. The use of genetically modified (GM) crops has increased, as have consumers' and regulators' concerns about the safety of GM crops to human health, and ecological biodiversity. As such, the identification of GM crops is a critical issue for developers and distributors, and their labeling is mandatory. Multiplex polymerase chain reaction (PCR) has been developed and its use validated for the detection and identification of GM crops in quarantine. Herein, we established a simultaneous detection method to identify four GM maize events. Event-specific primers were designed between the junction region of transgene and genome of four GM maize lines, namely 5307, DAS-40278-9, MON87460, and MON87427. To verify the efficiency and accuracy of the multiplex PCR we used specificity analysis, limit of detection evaluation, and mixed certified reference materials identification. The multiplex PCR method was applied to analyze 29 living, modified maize volunteers collected in South Korea in 2018 and 2019. We performed multiplex PCR analysis to identify events and confirmed the result by simplex PCR using each event-specific primer. As a result, rather than detecting each event individually, the simultaneous detection PCR method enabled the rapid analysis of 29 GM maize volunteers. Thus, the novel multiplex PCR method is applicable for living modified organism volunteer identification.