• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.3
• /
• pp.1-9
• /
• 2014

This study deals with development of Integrated Transportation Analysis System for Large-scale event. Based on case studies, the requirements of the system were defined and the direction of development was established. The large-scale events that require fast and accurate transportation policy were selected. The data warehouse and data mart were developed by integrating the large-scale event data and the traffic data. Business intelligence system was designed and developed users to allow timely decisions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.6
• /
• pp.1245-1255
• /
• 2009

Recently, to analyze the cause of the railway accident objectively and quickly and prevent the accident, many countries are legislating for the installation of the black box what we call an event recorder, which records information about the operation of railway vehicle. Thus, the study of the event recorder has been in progress. Moreover, the analysis software that can analyze and express the stored data in the event recorder is required for the correct decision on the accident. Therefore, in this paper, we presented a design of analysis software which analyzes the data, plays the audio and video in the event recorder system. This software can quickly and accurately identify the cause of the accident and recognize the driving patterns and habits of the driver according to the operating section. In addition, by analyzing the audio and video data simultaneously in the previous accident, we expect that it is possible to prevent accidents in advance.

• Ocean Systems Engineering
• /
• v.8 no.1
• /
• pp.41-55
• /
• 2018

Accidental oil and gas leak is a critical concern for the offshore industry because it can lead to severe consequences and as a result, it is imperative to evaluate the probabilities of occurrence of the consequences of the leakage in order to assess the risk. Event Tree Analysis (ETA) is a technique to identify the consequences that can result from the occurrence of a hazardous event. The probability of occurrence of the consequences is evaluated by the ETA, based on the failure probabilities of the sequential events. Conventional ETA deals with events with crisp failure probabilities. In offshore applications, it is often difficult to arrive at a single probability measure due to lack of data or imprecision in data. In such a scenario, fuzzy set theory can be applied to handle imprecision and data uncertainty. This paper presents fuzzy ETA (FETA) methodology to compute the probability of the outcomes initiated due to oil/gas leak in an actual offshore-onshore installation. Post FETA, sensitivity analysis by Fuzzy Weighted Index (FWI) method is performed to find the event that has the maximum contribution to the severe sequences. It is found that events of 'ignition', spreading of fire to 'equipment' and 'other areas' are the highest contributors to the severe consequences, followed by failure of 'leak detection' and 'fire detection' and 'fire water not being effective'. It is also found that the frequency of severe consequences that are catastrophic in nature obtained by ETA is one order less than that obtained by FETA, thereby implying that in ETA, the uncertainty does not propagate through the event tree. The ranking of severe sequences based on their probability, however, are identical in both ETA and FETA.

• Korean Journal of Clinical Pharmacy
• /
• v.33 no.2
• /
• pp.122-127
• /
• 2023

Background: This study was aimed to identify the safety signals of metoclopramide in Korea Adverse Event Reporting System (KAERS) database by proportionality analysis methods. Methods: The study was conducted using Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD) reported from January 2013 to December 2017 through KAERS. Signals of metoclopramide that satisfied the data-mining indices of proportional reporting ratio (PRR), reporting odds ratio (ROR) and information component (IC) were defined. The detected signals were checked whether they included in drug labels in the Ministry of Food and Drug Safety (MFDS), U.S. Food and Drug Administration (FDA) and Micromedex^®. Results: A total number of drug AE reports associated with all drugs of data in this study was 2,665,429. Among them, the number of AE reports associated with metoclopramide was 22,583. Forty-two meaningful signals of metoclopramide were detected that satisfied with the criteria of data-mining indicies. Especially neurological signals including extrapyramidal reactions, represented in the safety letter of regulatory agencies were identified in this study. Conclusion: Neurological signals of metoclopramide including extrapyramidal reactions were detected. It is believed that this search for signals can contribute to ensuring safety in the use of metoclopramide.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.183-203
• /
• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.3 s.168
• /
• pp.170-178
• /
• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.1428-1432
• /
• 2007

One of the most important targets of transportation is to transport human and commodities to the destination safely. Railway has low risk, compared with land, ocean and flight route and it assures high security as well as high speed driving, since it runs on regular track. However, train accident may result in tragic accident due to small carelessness, so special event recorder is preferably used in order for clarity of responsibility in case of accident, maintenance of signal device and defect analysis. JRU(Juridical Recorder Unit) for ATC/ATS/ATP can be more advanced event recorder. Event recorder of KTX-I which is running now is installed one by one on each leading car and last car, and operation plan of event recorder in case of single trainset is suggested. But regarding train operation of multi-coupled trainset operation such as KTX-II, more detailed study is required for event recorder revitalization and record data process method. Therefore, this research aims at operation plan used in existing event recorder, and suggests effective operation and management plan of event recorder in multi-coupled trainset such as new High Speed Train.

• Journal of Distribution Science
• /
• v.21 no.3
• /
• pp.93-100
• /
• 2023

Purpose: The purpose of this paper is to confirm whether stocks belonging to the distribution industry in Korea have reversals, following large daily stock price changes accompanied by large trading volumes. Research design, data, and methodology: We examined whether there were reversals after the event date when large-scale stock price changes appeared for the entire sample of distribution-related companies listed on the Korea Composite Stock Price Index from January 2004 to July 2022. In addition, we reviewed whether the reversals differed depending on abnormal trading volume on the event date. Using multiple regression analysis, we tested whether high trading volume had a significant effect on the cumulative rate of return after the event date. Results: Reversals were confirmed after the stock price shock in the Korean distribution industry and the return after the event date varied depending on the size of the trading volume on the event day. In addition, even after considering both company-specific and event-specific factors, the trading volume on the event day was found to have significant explanatory power on the cumulative rate of return after the event date. Conclusions: Reversals identified in this paper can be used as a useful tool for establishing a trading strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.10
• /
• pp.2473-2492
• /
• 2012

Energy conservation is a vital issue in wireless sensor networks. Recently, employing mobile sinks for data gathering become a pervasive trend to deal with this problem. The sink can follow stochastic or pre-defined paths; however the controlled mobility pattern nowadays is taken more into consideration. In this method, the sink moves across the network autonomously and changes its position based on the energy factors. Although the sink mobility would reduce nodes' energy consumption and enhance the network lifetime, the overhead caused by topological changes could waste unnecessary power through the sensor field. In this paper, we proposed EEDARS, an energy-efficient dual-sink algorithm with role switching mechanism which utilizes both static and mobile sinks. The static sink is engaged to avoid any periodic flooding for sink localization, while the mobile sink adaptively moves towards the event region for data collection. Furthermore, a role switching mechanism is applied to the protocol in order to send the nearest sink to the recent event area, hence shorten the path. This algorithm could be employed in event-driven and multi-hop scenarios. Analytical model and extensive simulation results for EEDARS demonstrate a significant improvement on the network metrics especially the lifetime, the load and the end-to-end delay.

• Convergence Security Journal
• /
• v.5 no.2
• /
• pp.1-8
• /
• 2005

Network event analysis gives useful information on the network status that helps protect attacks. It involves finding sets of frequently used packet information such as IP addresses and requires real-time processing by its nature. Apriori algorithm used for data mining can be applied to find frequent item sets, but is not suitable for analyzing network events on real-time due to the high usage of CPU and memory and thus low processing speed. This paper develops a network event audit module by applying association rules to network events using a new algorithm instead of Apriori algorithm. Test results show that the application of the new algorithm gives drastically low usage of both CPU and memory for network event analysis compared with existing Apriori algorithm.