• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.27-35
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• 2021

Generally, a proper evaluation method of the aging phenomenon of PV modules is required as the electrical performance and lifespan of PV modules can degrade significantly due to several environmental factors, while they are generally known as devices that are used semi-permanently for more than 20 years. On the other hand, there is a lack of objectivity in the existing evaluation method of the aging phenomenon, which compares the adjusted PV output based on STC with the initial PV module specifications due to the data distortion while adjusting the measured data. Therefore, this study implemented a test device for an aging diagnosis to measure and collect actual data from a PV module section and modeled the data for aging using MATLAB S/W to minimize the variability of the PV output, communication error, and delay. Furthermore, this study confirmed the usefulness of the presented test device for aging diagnosis of the PV modules by diagnosing the total period and yearly-basis degradation rate of aging PV modules as 25.73% and 1.55%, respectively, according to the on-site output characteristics of the PV modules by season.

• New Physics: Sae Mulli
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• v.68 no.11
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• pp.1192-1195
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• 2018

We studied the electrical properties of organic thin-film transistors (OTFTs) fabricated at different deposition rates by measuring the field-effect mobility and the threshold voltages. As the active layer, pentacene thin film with a thickness of 50 nm was deposited at a rate of $0.05{\AA}/s$ to $1.14{\AA}/s$. The thickness of the drain-source gold electrode was 50 nm. The mobility was $1.9{\times}10^{-1}cm^2/V{\cdot}s$ at a deposition rate of $0.05{\AA}/s$, the mobility increased to $5.2{\times}10^{-1}cm^2/V{\cdot}s$ when the deposition rate was increased to $0.4{\AA}/s$, and then the mobility decreased to $6.5{\times}10^{-1}cm^2/V{\cdot}s$ when the deposition rate decreased to $1.14{\AA}/s$. Thus, the mobility of pentacene OTFTs was observed to depend on the thermal deposition rate.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.597-602
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• 2021

Even in the era of the 4th industrial revolution, the prevention of industrial accidents is still an important issue in industrial sites. In solving the problem of industrial safety, a product can be difficult to market if there is a lack of standard or method for a reliable performance evaluation. The purpose of this study was to develop and evaluate a test method for a wearable airbag product for protecting the body from falls that was newly developed to respond to fall accidents in industrial sites. As a research method, reliable evaluation standards were developed and applied through four stages of the evaluation and development process (Step 1: Product review, Step 2: Data research, Step 3: Expert meeting, Step 4: Drawing evaluation standard). In addition, the impact force was evaluated according to the developed evaluation standard. The fall impact force obtained through the evaluation showed a reduction effect of approximately 96% compared to the existing impact force. Therefore, the fall impact force was reduced significantly when the airbag was applied. This will enable new convergence products to be launched on the market and produce an environment where industrial workers can work safely.

• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.67-78
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• 2022

Interest in bio-signal monitoring of wearable devices is increasing significantly as the next generation needs to develop new devices to dominate the global market of the information and communication technology industry. Accordingly, this research developed a resistive textile strain sensor through a wetting process in a single-wall carbon nanotube dispersion solution using an E-Band with low hysteresis. To measure the resistance signal in the E-Band to which electrical conductivity is applied, a universal material tester, an Arduino, and LCR meters that are microcontroller units were used to measure the resistance change according to the tensile change. To effectively handle various noises generated due to the characteristics of the fabric textile strain sensor, the filter performance of the sensor was evaluated using the moving average filter, Savitsky-Golay filter, and intermediate filters of signal processing. As a result, the reliability of the filtering result of the moving average filter was at least 89.82% with a maximum of 97.87%, and moving average filtering was suitable as the noise filtering method of the textile strain sensor.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.1019-1025
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• 2021

In nearly all space environments, the solar cell degradation is dominated by protons[1]. Even through a GEO orbit lines in the electron radiation belts, the protons emitted from any solar event will still dominate the degradation[1]. Since COMS launch on June 26 2010, the proton events with the fluence of more than approximately 30 times the average level of perennial observations were observed between January 23 - 29 2012 and March 07 - 14 2012[16]. This paper studies the solar cell degradation by solar proton events in January and March 2012 for the open circuit voltage(Voc) of a witness cell and the short circuit current(Isc) of a section connected to a shunt switch. To evaluate the performance of solar cell, the flight data of voltage and current are corrected to the temperature, the Earth-Sun distance and the Sun angle and then compare with the solar cell characteristics at BOL. The Voc voltage dropped about 23.6mV compare after the March 2012 proton events to before the January 2012 proton events. The Voc voltage dropped less than 1% at BOL, which is 2575mV. The Isc current decreased negligible, as expected, in the March 2012 proton events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• Journal of IKEEE
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• v.26 no.3
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• pp.510-514
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• 2022

In this paper, we propose the development of high-sensitivity low-end radiation measuring sensor module. The proposed measurement sensor module is a scintillator + photomultiplier(SiPM) sensor optimization structure design, amplification and filter and control circuit design for sensor driver, control circuit design including short-distance communication, sensor mechanism design and manufacturing, and GUI development applied to prototypes consists of, etc. The scintillator + photomultiplier(SiPM) sensor optimization structure design is designed by checking the characteristics of the scintillator and the photomultiplier (SiPM) for the sensor structure design. Amplification, filter and control circuit design for sensor driver is designed to process fine scintillation signal generated by radiation with a scintillator using SiPM. Control circuit design including short-distance communication is designed to enable data transmission through MCU design to support short-range wireless communication function and wired communication support. The sensor mechanism design and manufacture is designed so that the glare generated by wrapping a reflective paper (mirroring) on the outside of the plastic scintillator is reflected to increase the efficiency in order to transmit the fine scintillation signal generated from the plastic scintillator to the photomultiplier(SiPM). The GUI development applied to the prototype expresses the date and time at the top according to each screen and allows the measurement unit and time, seconds, alarm level, communication status, battery capacity, etc. to be expressed. In order to evaluate the performance of the proposed system, the results of experiments conducted by an authorized testing institute showed that the radiation dose measurement range was 30 𝜇Sv/h ~ 10 mSv/h, so the results are the same as the highest level among products sold commercially at domestic and foreign. In addition, it was confirmed that the measurement uncertainty of ±7.4% was measured, and normal operation was performed under the international standard ±15%.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.184-193
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• 2021

Purpose: In this study, since the operation principle of the CCFL Exit light is the same as that of general lighting equipment, the characteristics of the CCFL Exit light were analyzed by deriving test items that can affect the characteristics of the light source from the KS standard, which is the standard for lamp ballast performance certification of general lighting equipment. Method: The samples used in the experiment were performed on products of two manufacturers for each size, such as large, medium, and small, and the test items were power factor, crest factor, and current harmonic distortion. Result: As a result of the experiment, the power factor showed a value between 0.4 and 0.6 in all samples, which was smaller than the 0.9 value set by KS. The crest factor ranged from 3.6 to 3.7 for large, 4.4 to 4.7 for medium, and 3.5 to 3.7 for small. It showed a value more than two times higher than the KS standard of 1.7. Current total harmonic distortion ranged from 81% to 110%, and considering that the KS standard was less than 20%, it could be confirmed that all samples had a value significantly exceeding the KS standard. Conclusion: The crest factor and current total harmonic distortion may affect the temperature rise of the light source and the burnout of the device. When developing an exit light, if this item is developed within the scope of the KS standard, the quality improvement and maintenance of the exit light will be greatly improved.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.