• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1099-1100
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• 2006

The running of the electric train are done by powering, breaking and coasting. Powering to start and accelerate the rolling stock means driving the three-phase electric induction motors controlled by VVVF inverters which are connected to overhead power line directly(DC) or indirectly(AC, DC/AC). Breaking is achieved by blending control which is the proportional combination between regenerative breaking of VVVF inverter and air pressure control of breaking operating unit(BOU). Therefore, Data obtained during two operation are very important items to evaluate the running performance of RS. This paper has investigated in real time data acquired from VVVF inverter and measured by pressure sensor directly connected to air breaking line(motor car and trailer car). By analyzing data in the region of time and frequency, fundamental methods for eavaluating the running performance of RS quantitatively and objectively have been suggested.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.65-69
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• 2018

In this paper, we propose an interworking architecture for building indoor air quality monitoring server (BEMS) using IIoT (Industrial Internet of Things). The proposed monitoring server adopts IIoT-based standard protocol so that interaction with BEMS installed in existing buildings can be performed easily. It can effectively communicate with indoor air quality measurement sensor installed in the building based on IIoT, Indoor air quality monitoring is possible. We implemented a proposed monitoring server, and confirmed the availability and monitoring of data from sensors in the building.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.7-13
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• 2012

This paper is to estimate aerodynamic coefficients needed to determine the missiles' controller design and stability from simulation data of Skid-to-Turn missile. Method of determining aerodynamic coefficients is to apply Neural Network and Recursive Least Square and results were compared and researched. Also analysing actual flight test data was considered and sensor noise was added. Estimate parameter of data with sensor noise added and estimated performance and reliability for both methods that did not need initial values. Both Neural Network and Recursive Least Square methods showed excellent estimate results without adding the noise and with noise added Neural Network method showed better estimate results.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.804-813
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• 2015

The electrical-impedance method has been widely used for void-fraction measurement in two-phase flow due to its many favorable features. In the impedance method, the response characteristics of the electrical signal heavily depend upon flow pattern, as well as phasic volume. Thus, information on the flow pattern should be given for reliable void-fraction measurement. This study proposes an improved electrical-conductance sensor composed of a three-electrode set of adjacent and opposite electrodes. In the proposed sensor, conductance readings are directly converted into the flow pattern through a specified criterion and are consecutively used to estimate the corresponding void fraction. Since the flow pattern and the void fraction are evaluated by reading conductance measurements, complexity of data processing can be significantly reduced and real-time information provided. Before actual applications, several numerical calculations are performed to optimize electrode and insulator sizes, and optimal design is verified by static experiments. Finally, the proposed sensor is applied for air-water two-phase flow in a horizontal loop with a 40-mm inner diameter and a 5-m length, and its measurement results are compared with those of a wire-mesh sensor.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.65-72
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• 2018

Since data-driven building technologies have been widely applied to building energy systems, the accuracy of building sensors has more impacts on the building performance and system performance analysis. Various building sensors, however, can have typical errors including a random error (noise) and a systematic error (bias). The systematic error is indicated by the difference between the mean of measurements and their true value. It may occur due to the sensor's physical condition, measured phenomena, working environments inside the systems. Unfortunately, a conventional calibration method has limitations in calibrating the systematic errors because of the difference between working environments and calibration conditions. In such situations, a novel sensor calibration method is needed to handle various sensor errors, especially for systematic errors, in building energy systems having various thermodynamic environments. This study proposes a building sensor calibration method named Virtual In-situ Calibration (VIC) and shows how it is applied into a real building system and how it solves the sensor errors.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1073-1082
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• 2023

Thermal sensors, also called thermal infrared wavelength sensors, measure temperature based on the intensity of infrared signals that reach the sensor. The infrared signals recognized by the sensor include infrared wavelength(0.7~3.0㎛) and radiant infrared wavelength(3.0~100㎛). Infrared(IR) wavelengths are divided into five bands: near infrared(NIR), shortwave infrared(SWIR), midwave infrared(MWIR), longwave infrared(LWIR), and far infrared(FIR). Most thermal sensors use the LWIR to capture images. Thermal sensors measure the temperature of the target in a non-contact manner, and the data can be affected by the sensor's viewing angle between the target and the sensor, the amount of atmospheric water vapor (humidity), air temperature, and ground conditions. In this study, the characteristics of three thermal imaging sensor models that are widely used for observation using unmanned aerial vehicles were evaluated, and the optimal application field was determined.

• Korean Journal of Environment and Ecology
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• v.30 no.4
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• pp.793-799
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• 2016

This study examined Platanus orientalis and Ulmus davidiana planted in downtown parks to identify the correlations among microclimatic factors such as temperature in the crown, air flow, and wind speed. For the field survey, measurements were taken at 1 hour intervals from 09:00 am to 06:00 pm in August. For the measurement of microclimatic factors, data on temperature, light intensity, air flow, and wind speed were collected using a quantum sensor (PAR Quantum Sensor SKP215), a precision thermometer (Pt1000-Sensor), and a combination anemometer (1467 G4 & HG4). The results of the analysis demonstrated that both Platanus orientalis and Ulmus davidiana, showed a greater cooling effect inside the crown as compared with the outside temperature. The cooling effect inside the crown was more evident with air flow and wind speed factors. With relation to wind, the inner temperature of the crown of Platanus orientalis decreased due to air flow while that of Ulmus davidiana decreased due to wind speed. With no wind, the average variation in temperature inside the crown was $-0.9^{\circ}C$ for Ulmus davidiana and $-0.958^{\circ}C$ for Platanus orientalis, indicating that Platanus orientalis was relatively more effective in lowering the temperature of the planting space than Ulmus davidiana. This study is significant because it shows that different tree species have different effects on the microclimate and that factors affecting the formation of the microclimate of trees may vary with species. Further studies on species other than broad leaf trees, such as evergreen trees and shrubs, are required in order to plan the distribution of landscaping trees that are effective in regulating the microclimate within urban green spaces.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.551-553
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• 2016

Since air compressor, as an essential equipment used in the factory and plant operations, accounts for around 20% of the total domestic electricity consumption, a real time sensor data monitoring based analysis for electricity consumption reduction is important. In particular, flow rates and pressures of these monitored variables has a direct correlation with the power consumption. This paper proposes a method to identify if the measurement error of the flow rate sensor comes from the sensor measurement limit through bivariate classification analysis of the flow rate and power using the EM (Expectation and Maximization) Algorithm and show how to enable more accurate analysis by the correlation between the flow rate and power on the right-censored data.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.41-45
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• 2006

The fabrication process of fiber placement system of carbon fiber reinforced plastic (CFRP) requires real time process control and reliable inspection to ensure quality by preventing defects such as delamination and void. Therefore, novel non-contact inspection technique is required during the non-destructive evaluation in a fiber placement system. For the inspection of delamination in CFRP, various methods to receive laser-generated ultrasound were applied by using piezoelectric transducer, air-coupled transducer, wavelet transform and scanning laser ultrasonic technique. Laser-generated ultrasound was received with a conventional piezoelectric sensor in contacting manner. Then signal characteristics due to defects were analyzed to find a factor for detecting defects. Air-coupled transducer was used for reception of laser-generated guided wave using linear slit array in order to generate high frequency guided wave. And line scan technique was used to confirm the capability of on-line application. The high frequency component of laser-generated guided wave received with piezoelectric sensor disappeared after propagating through delamination region. Nevertheless, it was failed to receive high frequency guided wave in using air-coupled transducer. The first peak of the frequency spectrum under 100kHz in the delamination region is higher than in the sound region. By using this feature, the line scanned frequency data were acquired in fully non-contact generation and reception of ultrasound. This method was proved as useful technique for detecting delamination in CFRP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.186-197
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• 2023

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.