• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.63-74
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• 2020

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market. Foreign investors' trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio. The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020. The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique. It then applies vector autoregressions and impulse responses to test for the investors' chosen strategy, herd behavior, and market destabilization. The results show that foreign investors' abnormal trading volume is negative and significant. An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd. Although foreign investors' abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade. The methodology is new; the findings are useful for researchers, local authorities, and investors.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.114-114
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• 2020

Water contamination in a water distribution network (WDN) is harmful since it directly induces the consumer's health problem and suspends water service in a wide area. Actions need to be taken rapidly to countermeasure a contamination event. A contaminant source ident ification (CSI) is an important initial step to mitigate the harmful event. Here, a CSI approach focused on determining the contaminant intrusion possible location and time (PLoT) is introduced. One of the methods to discover the PLoT is an inverse calculation to connect all the paths leading to the report specification of a sensor. A filtering procedure is then applied to narrow down the PLoT using the results from individual sensors. First, we spatially reduce the suspect intrusion points by locating the highly suspicious nodes that have similar intrusion time. Then, we narrow the possible intrusion time by matching the suspicious intrusion time to the reported information. Finally, a likelihood-score is estimated for each suspect. Another important aspect that needs to be considered in CSI is that there are inherent uncertainties, such as the variations in user demand and inaccuracy of sensor data. The uncertainties can lead to overlooking the real intrusion point and time. To reflect the uncertainties in the CSI process, the Monte-Carlo Simulation (MCS) is conducted to explore the ranges of PLoT. By analyzing all the accumulated scores through the random sets, a spread of contaminant intrusion PLoT can then be identified in the network.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.699-706
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• 2009

The convergence tendency accelerates the realization of the ubiquitous healthcare (u-Healthcare) between the technology including the power generaation and IT-BT-NT of the ubiquitous computing technology. By rapidly analyzing a large amount of collected from the sensor network with processing and delivering to the medical team an u-Healthcare can provide a patient for an inappropriate regardless of the time and place. As to the existing u-Healthcare, since the sensor node all transmitted collected data by using with the Zigbee protocol the processing burden of the base node was big and there was many communication frequency of the sensor node. In this paper, the u-Healthcare system in which it can efficiently apply to mobile apparatuses it provided the transfer rate in which it is superior to the bio-signal delivery where there are the life and direct relation which by using the Bluetooth instead of the Zigbee protocol and in which it is variously used in the ubiquitous environment was designed. Moreover, by applying the EEF(Embedded Event Filtering) technique in which data in which it includes in the event defined in advance selected and it transmits with the base node, the communication frequency and were reduced. We confirmed to be the system in which it is efficient through the simulation result than the existing Electrocardiogram Measurement system.

• Journal of Biomedical Engineering Research
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• v.30 no.6
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• pp.516-520
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• 2009

The purpose of this study is to determine optimal filtering condition and threshold for the detection of gait-cycles for various walkway slopes as well as gait velocities. Ten young healthy subjects with accelerometer system on thigh and ankle walked on a treadmill at 9 conditions (three speeds and three slopes) for 5 minutes. Two direction signals, i.e. anterior-posterior (AP) and superior-inferior (SI) directions, of each sensor (four sensor orientations) were used to detect specific events of gait cycle. Variation of the threshold (from -1G to 1G) and lowpass cutoff frequency (fc) were applied to the event detection and their performance was evaluated according to the error index (EI), which was defined as the combination of the accuracy and false positive rate. Optimal fc and threshold were determined for each slope in terms of the EI. The optimal fc, threshold and their corresponding EI depended much on the walkway slope so that their coefficients of variation (CV) ranged 19~120%. When all data for 3 slopes were used in the identification of optimal conditions for each sensor, the best error indices for all sensor orientations were comparable ranging 1.43~1.76%, but the optimal fc and threshold depended much on the sensor position. The result indicates that the gait-cycle detection robust to walkway slope is possible by threshold method with well-defined filtering condition and threshold.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.55-70
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• 2011

In this paper, we propose a policy-driven RFID data management event definition language, which is possibly applicable as a partial standard for SSI (Software System Infrastructure) Part 4 (Application Interface, 24791-4) defined by ISO/IEC JTC 1/SC 31/WG 4 (RFID for Item Management). The SSI's RFID application interface part is originally defined for providing a unified interface of the RFID middleware functionality―data management, device management, device interface and security functions. However, the current specifications are too circumstantial to be understood by the application developers who used to lack the professional and technological backgrounds of the RFID middleware functionality. As an impeccable solution, we use the concept of event-constraint policy that is not only representing semantic contents of RFID domains but also providing transparencies with higher level abstractions to RFID applications, and that is able to provide a means of specifying event-constraints for filtering a huge number of raw data caught from the associated RF readers. Conclusively, we try to embody the proposed concept by newly defining an XML-based RFID event policy definition language, which is abbreviated to rXPDL. Additionally, we expect that the specification of rXPDL proposed in the paper becomes a technological basis for the domestic as well as the international standards that are able to be extensively applied to RFID and ubiquitous sensor networks.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 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 Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1554-1563
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• 2008

As the interests on RFID technologies increase, a lot of research activities on RFID middleware systems to handle the data acquired by RFID readers are going on actively. Meanwhile, even though various kinds of RFID middleware methodologies and related techniques have been proposed, the common data type which is dealt with in those systems is an EPC code, mainly. Also, there are few researches of the implementation of collecting the stream data queued from RFID readers endlessly and without blocking, classifying the data into some groups according to usage, and sending the resulting data to specific applications. In this paper, we propose the method of data handling in RFID middleware to efficiently process an EPC event stream data using detail filtering, checking of data modification, creation of data set to transfer, data grouping, and various kinds of RFID data format transform. Our method is based on a de facto international standard interface defined in the ALE middleware specification by EPCglobal, and application and service users can directly set various kinds of conditions to handle the stream data.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.41.1-41.1
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• 2018

We introduce a current status and future plans of Korea Microlensing Telescope Network (KMTNet) microlensing experiments, which include an observational strategy, pipeline, event-finder, and collaborations with Spitzer. The KMTNet experiments were initiated in 2015. From 2016, KMTNet observes 27 fields including 6 main fields and 21 subfields. In 2017, we have finished the DIA photometry for all 2016 and 2017 data. Thus, it is possible to do a real-time DIA photometry from 2018. The DIA photometric data is used for finding events from the KMTNet event-finder. The KMTNet event-finder has been improved relative to the previous version, which already found 857 events in 4 main fields of 2015. We have applied the improved version to all 2016 data. As a result, we find that 2597 events are found, and out of them, 265 are found in KMTNet-K2C9 overlapping fields. For increasing the detection efficiency of event-finder, we are working on filtering false events out by machine-learning method. In 2018, we plan to measure event detection efficiency of KMTNet by injecting fake events into the pipeline near the image level. Thanks to high-cadence observations, KMTNet found fruitful interesting events including exoplanets and brown dwarfs, which were not found by other groups. Masses of such exoplanets and brown dwarfs are measured from collaborations with Spitzer and other groups. Especially, KMTNet has been closely cooperating with Spitzer from 2015. Thus, KMTNet observes Spitzer fields. As a result, we could measure the microlens parallaxes for many events. Also, the automated KMTNet PySIS pipeline was developed before the 2017 Spitzer season and it played a very important role in selecting the Spitzer target. For the 2018 Spitzer season, we will improve the PySIS pipeline to obtain better photometric results.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.36-43
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• 2000

Many formats are used for recording and processing in the research of earthquake seismology and earthquake engineering. It is very difficult to program the Reading and writing algorithm for data processing because fill formats are very different from each other. It is suggested new file format of Korea Standard earthquake data (KSED) two types of ASCII and binary that are read and written easily. The Program package of basic data processing (Analyst) which has function of basic filtering spectrum analysis event gathering phase picking and location is developed In addition this program supports file transformation from another format(Mini SEED OMD K2) to KSED format.