• Journal of Information Technology Services
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• v.20 no.3
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.783-790
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• 2010

For safety navigation of ships at sea, ships monitor their location obtained from Global Positioning Satellite System (GNSS). In this study, we computed near-real-time positions of a ship at sea using GPS Precise Point Positioning (PPP) technique and analyzed precision of the near-real-time positions. We conducted ship borne GPS observations in the south sea of Korea. To process the GPS data using PPP technique, GIPSY-OASIS (GPS Inferred Positioning System-Orbit Analysis and Simulation Software) developed by the Jet Propulsion Laboratory was used. Antenna phase center variations, ocean tidal loading displacements, and azimuthal gradients of the atmosphere were corrected or estimated as standard procedures of high-precision GIPSY-OASIS data processing. As a result, the precisions of near-real-time positions was ~1cm.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.12-26
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• 2017

The objective of this study was to develop a runoff analysis system of the Nakdong River watershed using the GRM (Grid-based Rainfall-runoff Model), a physically-based distributed rainfall-runoff model, and to assess the system run time performance according to Microsoft Azure VM (Virtual Machine) settings. Nakdong River watershed was divided into 20 sub-watersheds, and GRM model was constructed for each subwatershed. Runoff analysis of each watershed was calculated in separated CPU process that maintained the upstream and downstream topology. MoLIT (Ministry of Land, Infrastructure and Transport) real-time radar rainfall and dam discharge data were applied to the analysis. Runoff analysis system was run in Azure environment, and simulation results were displayed through web page. Based on this study, the Nakdong River real-time runoff analysis system, which consisted of a real-time data server, calculation node (Azure), and user PC, could be developed. The system performance was more dependent on the CPU than RAM. Disk I/O and calculation bottlenecks could be resolved by distributing disk I/O and calculation processes, respectively, and simulation runtime could thereby be decreased. The study results could be referenced to construct a large watershed runoff analysis system using a distributed model with high resolution spatial and hydrological data.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.375-383
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• 2018

In order to apply the Network RTK (real time kinematics) technology, which has been used for positioning of stationary points, to the navigation of vehicles, its infrastructure should provide correction data with a quality indicator that can show the expected accuracy in real time. In this paper, we analyzed various indicator generation algorithms such as I95 (ionospheric index 95) / G95 (geodetic index 95), SBI (semivariance based index) and RIU (residual interpolation uncertainty). We also applied them to the raw observables from the reference stations of National Geographic Information Institute and VRS (virtual reference station) users, and then examined its feasibility to be used as a real-time performance index of the Network RTK rover. 24 hour data analysis shows that the RIU index, which can represent the non-linearty of the correction, has the strongest correlation with the Network RTK rover accuracy. Therefore, RIU index is expected to be used as a real-time performance index of the Network RTK rover.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.32-32
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• 2011

Plasma etching is used in microelectronic processing for patterning of micro- and nano-scale devices. Commonly, optical emission spectroscopy (OES) is widely used for real-time endpoint detection for plasma etching. However, if the viewport for optical-emission monitoring becomes blurred by polymer film due to prolonged use of the etching system, optical-emission monitoring becomes impossible. In addition, when the exposed area ratio on the wafer is small, changes in the optical emission are so slight that it is almost impossible to detect the endpoint of etching. For this reason, as a simple method of detecting variations in plasma without contamination of the reaction chamber at low cost, a method of measuring plasma impedance is being examined. The object in this research is to investigate the suitability of using plasma impedance monitoring (PIM) with statistical approach for real-time endpoint detection of $SiO_2$ etching. The endpoint was determined by impedance signal variation from I-V monitor (VI probe). However, the signal variation at the endpoint is too weak to determine endpoint when $SiO_2$ film on Si wafer is etched by fluorocarbon plasma on inductive coupled plasma (ICP) etcher. Therefore, modified principal component analysis (mPCA) is applied to them for increasing sensitivity. For verifying this method, detected endpoint from impedance analysis is compared with optical emission spectroscopy (OES). From impedance data, we tried to analyze physical properties of plasma, and real-time endpoint detection can be achieved.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.209-214
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• 2019

Precise point positioning (PPP) requires precise orbit and clock products. International GNSS service (IGS) real-time service (RTS) data can be used in real-time for PPP, but it may not be possible to receive these corrections for a short time due to internet or hardware failure. In addition, the time required for IGS to combine RTS data from each analysis center results in a delay of about 30 seconds for the RTS data. Short-term orbit prediction can be possible because it includes the rate of correction, but the clock correction only provides bias. Thus, a short-term prediction model is needed to preidict RTS clock corrections. In this paper, we used a long short-term memory (LSTM) network to predict RTS clock correction for three minutes. The prediction accuracy of the LSTM was compared with that of the polynomial model. After applying the predicted clock corrections to the broadcast ephemeris, we performed PPP and analyzed the positioning accuracy. The LSTM network predicted the clock correction within 2 cm error, and the PPP accuracy is almost the same as received RTS data.