• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.209-216
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• 2005

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. 1K $\times$ 1K CCD color camera and Xenon Lamp(500w) were used for the visualization of a Hele-Shaw cell The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.984-989
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• 2010

In this paper, parallel programming technique by using Java Thread is introduced so as to develop parallel design tool to analyze the small micro flow sensor. To estimate computing time for Thread-level parallelism, the performances of two experimental models for potential problem subject to Thermal transfer equation are examined. As a result, if the number of network PC is increase, computing time for parallelism on network environment is enhanced to be almost n times. The micro sensor design tool based on distributed computing can be utilized to analyze a large scale problem.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.4-65
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• 2001

In case of data transmission using the upper 10GHz frequency, rain results in attenuation of radio waves. And the most serious atmospheric effect in a satellite link is the rainfall. The attenuation of rainfall very seriously affects the quality of transmission line. Because the rain increases thermal noise and interference, and decreases the amplitude of the signal. KOWACO manages the VSAT system instead of VHF network for communication of rain and water-level data from 1998. The purpose of this system is to monitor the change of water-level and rain data during a flood duration. VHF system acquires the data by a call per a hour. But the satellite network obtains the data whenever event data occur. Thus the satellite network is more powerful than the VHF system. In study ...

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.178-184
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• 2022

Renewable energy sources based on solar radiation, wind energy, geothermal energy, and biomass energy have now reached the level of industrial application. A new way to generate electricity using low-potential heat is to install a hydro-steam turbine. In hydro-steam turbines, hot water is supplied directly to turbine rotor nozzles without prior separation into steam and water in separators, which significantly increases the efficiency of hot water energy use. Such turbines are suggested to be used as autonomous energy sources in geothermal heating systems, heating water boilers and cooling systems of chemical reactors, metallurgical furnaces, etc. The authors conclude that the installation of hydro-steam turbines in heating plants and process boiler plants can also be effective if the used exhaust steam-water mixture at the turbine outlet is used to heat the network water or as return water.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.73-85
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• 2024

There is growing interest in introducing artificial ground freezing (AGF) as a method to temporarily secure unstable ground during tunnel construction. In order to efficiently operate an artificial ground freezing system, basic modeling research is needed on the changes in freezing behavior according to various soil environmental conditions as well as design conditions. In this study, a thermal-hydraulic coupled analysis was performed to simulate the artificial ground freezing process of ground containing salt water. The effect of major variables, including pore water salinity, on artificial ground freezing test performance was investigated. Additionally, an artificial neural network-based prediction model was proposed to estimate the time required to achieve the desired arch thickness. The artificial neural network model demonstrated reliable accuracy (R² = 0.9942) in predicting the time it would take to reach the desired arch thickness. Among the major input variables considered, pore water salinity appeared to be the most influential input variable, and initial soil temperature showed the least importance.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.644-647
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• 2004

Optical add/drop multiplexers (OADMs), one of the new network elements, will play a key role enabling greater connectivity and flexibility in the dense wavelength-division multiplexing (DWDM) networks. The importance of OADMs is that they allow the optical network to be local transmitting/extraction on a wavelength-by-wavelength basis to optimize traffic, efficient network utilization, network growth, and to enhance network flexibility. Also, the automatic assembly system of micro optical filters and fibers is a key technology in the development of optical modules with high functionality. Recently, one of remarkable tends in the development of optical communication industry is the miniaturization and integration of products. In this research, we have developed a system capable of automatic alignment of a film filter and a lensed fiber in order to improve the speed and losses in the optical fiber to filter alignment of optical modules. Using the developed automatic alignment system and silicon optical benches, we have fabricated the micro OADM and measured the insertion loss and aging effect.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Polymer(Korea)
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• v.32 no.1
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• pp.56-62
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• 2008

Three types of network polyesters have been newly synthesized by a two-step condensation reaction by the various combination of several diols and diacids. When these polymer films were thermally treated at $240^{\circ}C$, they exhibited absorptions in a visible range despite the forbidden transition of carbonyl group. When excited at wavelengths above 330 nm, the polymers showed fluorescences in a wide visible range from blue to near yellow. These fluorescence phenomena are due to the formation of certain conjugated structures by the Knoevenagel type self-condensation under the high-temperature thermal treatment. These polymers showed significant difference in the thermal properties as a function of the degrees of chemical crosslinking. They also underwent photodegradation. Highly resolved, fluorescent image patterns were successfully obtained by the photodegradation of malonate group under a strong UV-light irradiation.