• Proceedings of the KSR Conference
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• 2007.11a
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• pp.411-418
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• 2007

To analyze dynamic and long-term behavior of high speed rail system, and seismic measurements been installed at the various locations in the high speed rail system. Among these, 76 measurements were installed Chunan and Deajeon, and 80 measurements between Kwangmyung and Chunan. From these 156 measurements, real time data are continuously corrected and be sent to the main monitoring system to be used further analysis. These two systems were installed by different institution, and so there are not only basic differences in the system itself, but system running methods and data collecting/transferring methodology. There has been systematic difficulties due to these differences. This study shows the whole steps of high speed rail data monitoring systems include measurement equipment itself and data collecting/transferring system. study will proposed a more effective methods for collecting. This study also discuss the real time analysis method which can be used for future high speed rail monitoring system.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.4
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• pp.425-434
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• 2022

Objectives: This study analyzes TVOC concentrations in root industry workplaces in order to prevent probable occupational disease among workers. Root industry includes all the infrastructure of manufacturing, such as casting and molding. Methods: Real-time TVOC sensors were deployed in three root industry workplaces. We measured TVOC concentrations with these sensors and analyzed the results using a data-analysis tool developed with Python 3.9. Results: During the study period, the mean of the TVOC concentrations remained in an acceptable range, 0.30, 2.15, and 1.63 ppm across three workplaces. However, TVOC concentrations increased significantly at specific times, with respective maximum values of 4.98, 28.35, and 26.65 ppm for the three workplaces. Moreover, the analysis of hourly TVOC concentrations showed that during working hours or night shifts TVOC concentrations increased significantly to higher than twice the daily mean values. These results were scrutinized through classical decomposition results and autocorrelation indices, where seasonal graphs of the corresponding classical decomposition results showed that TVOC concentrations increased at a specific time. Trend graphs showed that TVOC concentrations vary by day. Conclusions: Deploying a real-time TVOC sensor should be considered to reflect irregularly high TVOC concentrations in workplaces in the root industry. It is expected that the real-time TVOC sensor with the presented data analysis methodology can eradicate probable occupational diseases caused by detrimental gases.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Korean Journal of Breeding Science
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• v.43 no.5
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• pp.448-456
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• 2011

We used suppression subtractive hybridization (SSH) combined with mirror orientation selection (MOS) method to screen differentially expressed genes from red-fleshed kiwifruit 'Hongyang'. As a result, the 288 clones were obtained by subcloning PCR product and 192 clones that showed positive clones on colony PCR analysis were selected. All the positive clones were sequenced. After comparisons with the NCBI/Genbank database using the BLAST search revealed that 30 clones showed sequence similarity to genes from other organisms; 10 clones showed significant sequence similarity to known genes. Among these clones, 3 clones (AcF21, AcF42 and AcF106) had sequence homology to 1-aminicyclopropane-carboxylic acid (ACC)-oxidase (ACO) that known to be related to fruit ripening. The expression patterns of differentially expressed genes were further investigated to validate the SSH data by reverse transcription PCR (RT-PCR) and quantitative real-time PCR (qReal-time PCR) analysis. All the data from qReal-time PCR analysis coincide with the results obtained from RT-PCR analysis. Three clones were expressed at higher levels in 'Hongyang' than 'Hayward'. AcF21 was highly expressed in the other genes at 120 days after full bloom (DAFB) and 160 DAFB of 'Hongyang'.

• Journal of Computing Science and Engineering
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• v.2 no.4
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• pp.375-393
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• 2008

The dynamic cast operation allows flexibility in the design and use of data management facilities in object-oriented programs. Dynamic cast has an important role in the implementation of the Data Management Services (DMS) of the Mission Data System Project (MDS), the Jet Propulsion Laboratory's experimental work for providing a state-based and goal-oriented unified architecture for testing and development of mission software. DMS is responsible for the storage and transport of control and scientific data in a remote autonomous spacecraft. Like similar operators in other languages, the C++ dynamic cast operator does not provide the timing guarantees needed for hard real-time embedded systems. In a recent study, Gibbs and Stroustrup (G&S) devised a dynamic cast implementation strategy that guarantees fast constant-time performance. This paper presents the definition and application of a cosimulation framework to formally verify and evaluate the G&S fast dynamic casting scheme and its applicability in the Mission Data System DMS application. We describe the systematic process of model-based simulation and analysis that has led to performance improvement of the G&S algorithm's heuristics by about a factor of 2. In this work we introduce and apply a library for extracting semantic information from C++ source code that helps us deliver a practical and verifiable implementation of the fast dynamic casting algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.95-106
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• 2016

Biometric data may appear different depending on the person and sasang Medicine has a close relationship with the Department. Biometric data not only mean a human heart rate, a blood pressure, a heart rate, and the past medical history, degree of aging, body mass index, but also is used as a reference measure for determining the state of health of the person. So biometric data should be reproduced for the application purposes, depending on their applications. In previous studies, because the biometric data is changed in real time and applies only to snap shut at the time of the continuity of the current time is excluded. Therefore, in this study in order to solve this problem, we propose a biometric data patton analysis model comprising a continuity of time in the big data environment consisting of biometric data. The proposed model can help determine the choice of needle position carefully when using the electronic acupuncture for care and health promotion.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.2
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• pp.143-151
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• 2019

This study describes a method for analyzing log data generated in a process using process mining techniques. A system for collecting and analyzing a large amount of log data generated in the process of manufacturing an offshore plant piping material was constructed. The analyzed data was visualized through various methods. Through the analysis of the process model, it was evaluated whether the process performance was correctly input. Through the pattern analysis of the log data, it is possible to check beforehand whether the problem process occurred. In addition, we analyzed the process performance data of partner companies and identified the load of their processes. These data can be used as reference data for pipe production allocation. Real-time decision-making is required to cope with the various variances that arise in offshore plant production. To do this, we have built a system that can analyze the log data of real - time system and make decisions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.3
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• pp.130-139
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• 2016

A frequency of earthquake occurrence in the Republic of Korea is increasing over the past few decades. In this situation, an importance of earthquake prevention comes to the fore because the earthquake does damage to structures and causes severe damage of human life. For the earthquake prevention, a real-time vibration measurement for structures is important. As an example, the United States of America and Japan have already been monitoring real-time earthquake acceleration for the important structures and the measured acceleration data has been managed by forming database. This database could be used for revising the seismic design specifications or predicting the damage caused by earthquake. In Korea, Earthquake Recovery Plans Act and Enforcement Regulations are revised and declared lately. Ministry of Public Safety and Security is constructing a integrated management system for the measured earthquake acceleration data. The purpose of this research is to develop a real-time vibration monitoring and analysis system for structures which links to the integrated management system. The developed system contains not only a monitoring function to show real-time acceleration data but also an analysis system to perform fast fourier transform, to obtain natural frequency and earthquake magnitude, to show response spectrum and power spectrum, and to evaluate structural health. Additionally, this system is designed to be able to link to the integrated management system of Ministry of Public Safety and Security. It is concluded that the developed system can be useful to build a safety management network, minimize maintenance cost of structures, and prevention of the structural damage due to earthquake.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1629-1632
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• 2008

Rapid, real-time detection of pathogenic microorganisms is an emerging and quickly evolving field of research, especially with regard to microorganisms that pose a major threat to public health. Herein, a new method that uses bioimpedance and solid culture medium for the real-time detection of microorganisms is introduced. We fabricated a new impedimetric biosensor by integrating solid media and two plane electrodes attached on two facing sides of an acryl well. During bioelectrical impedance analysis, the solid medium showed the characteristics of a homogenous conductive material. In a real-time impedance measurement, our solid-medium biosensor could monitor bacterial growth in situ with a detection time of ${\sim}4$ hrs. Our data indicate that the solid-medium biosensor is useful for detecting airborne microorganisms, thereby providing a new analytical tool for impedance microbiology.