• Journal of The Korean Association For Science Education
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• v.39 no.3
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• pp.363-377
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• 2019

This study reveals the aspects of subjectivity in the test results in a science-specific aspect when assessing science-related affective characteristic through self-report items. The science-specific response was defined as the response that appear due to student's recognition of nature or characteristics of science when his or her concepts or perceptions about science were attempted to measure. We have searched for cases where science-specific responses especially interfere with the measurement objective or accurate self-reports. The results of the error due to the science-specific factors were derived from the quantitative data of 649 students in the 1st and 2nd grade of high school and the qualitative data of 44 students interviewed. The perspective of science and the characteristics of science that students internalize from everyday life and science learning experiences interact with the items that form the test tool. As a result, it was found that there were obstacles to accurate self-report in three aspects: characteristics of science, personal science experience, and science in tool. In terms of the characteristic of science in relation to the essential aspect of science, students respond to items regardless of the measuring constructs, because of their views and perceived characteristics of science based on subjective recognition. The personal science experience factor representing the learner side consists of student's science motivation, interaction with science experience, and perception of science and life. Finally, from the instrumental point of view, science in tool leads to terminological confusion due to the uncertainty of science concepts and results in a distance from accurate self-report eventually. Implications from the results of the study are as follows: review of inclusion of science-specific factors, precaution to clarify the concept of measurement, check of science specificity factors at the development stage, and efforts to cross the boundaries between everyday science and school science.

• Journal of IKEEE
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• v.25 no.1
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• pp.156-167
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• 2021

In this paper, we propose a digital transceiver unit design for in-building of 5G optical repeaters that extends the coverage of 5G mobile communication network services and connects to a stable wireless network in a building. The digital transceiver unit for driving the proposed 5G optical repeater is composed of 4 blocks: a signal processing unit, an RF transceiver unit, an optical input/output unit, and a clock generation unit. The signal processing unit plays an important role, such as a combination of a basic operation of the CPRI interface, a 4-channel antenna signal, and response to external control commands. It also transmits and receives high-quality IQ data through the JESD204B interface. CFR and DPD blocks operate to protect the power amplifier. The RF transmitter/receiver converts the RF signal received from the antenna to AD, is transmitted to the signal processing unit through the JESD204B interface, and DA converts the digital signal transmitted from the signal processing unit to the JESD204B interface and transmits the RF signal to the antenna. The optical input/output unit converts an electric signal into an optical signal and transmits it, and converts the optical signal into an electric signal and receives it. The clock generator suppresses jitter of the synchronous clock supplied from the CPRI interface of the optical input/output unit, and supplies a stable synchronous clock to the signal processing unit and the RF transceiver. Before CPRI connection, a local clock is supplied to operate in a CPRI connection ready state. XCZU9CG-2FFVC900I of Xilinx's MPSoC series was used to evaluate the accuracy of the digital transceiver unit for driving the 5G optical repeater proposed in this paper, and Vivado 2018.3 was used as the design tool. The 5G optical repeater digital transceiver unit proposed in this paper converts the 5G RF signal input to the ADC into digital and transmits it to the JIG through CPRI and outputs the downlink data signal received from the JIG through the CPRI to the DAC. And evaluated the performance. The experimental results showed that flatness, Return Loss, Channel Power, ACLR, EVM, Frequency Error, etc. exceeded the target set value.

• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.147-157
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• 2022

This study tried to propose a smart farm technology strategy suitable for the domestic situation, focusing on the differentiation suitable for the domestic situation of ICT technology. In the case of advanced countries in the overseas agricultural industry, it was confirmed that they focused on the development of a specific stage that reflected the geographical characteristics of each country, the characteristics of the agricultural industry, and the characteristics of the people's demand. Confirmed that no enemy development is being performed. Therefore, in response to problems such as a rapid decrease in the domestic rural population, aging population, loss of agricultural price competitiveness, increase in fallow land, and decrease in use rate of arable land, this study aims to develop smart farm ICT technology in the future to create quality agricultural products and have price competitiveness. It was suggested that the smart farm should be promoted by paying attention to the excellent performance, ease of use due to the aging of the labor force, and economic feasibility suitable for a small business scale. First, in terms of economic feasibility, the ICT technology is configured by selecting only the functions necessary for the small farm household (primary) business environment, and the smooth communication system with these is applied to the ICT technology to gradually update the functions required by the actual farmhouse. suggested that it may contribute to the reduction. Second, in terms of performance, it is suggested that the operation accuracy can be increased if attention is paid to improving the communication function of ICT, such as adjusting the difficulty of big data suitable for the aging population in Korea, using a language suitable for them, and setting an algorithm that reflects their prediction tendencies. Third, the level of ease of use. Smart farms based on ICT technology for the development of the Industry6.0 (1.0(Agriculture, Forestry) + 2.0(Agricultural and Water & Water Processing) + 3.0 (Service, Rural Experience, SCM)) perform operations according to specific commands, finally suggested that ease of use can be promoted by presetting and standardizing devices based on big data configuration customized for each regional environment.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.604-617
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• 2022

Sea surface temperature (SST) is a key variable that can be used to understand ocean-atmosphere phenomena and predict climate change. Satellite microwave remote sensing enables the measurement of SST despite the presence of clouds and precipitation in the sensor path. Therefore, considering the high utilization of microwave SST, it is necessary to continuously verify its accuracy and analyze its error characteristics. In this study, the validation of the microwave global precision measurement (GPM)/GPM microwave imager (GMI) SST around the Northwest Pacific and Korean Peninsula was conducted using surface drifter temperature data for approximately eight years from March 2014 to December 2021. The GMI SST showed a bias of 0.09K and an average root mean square error of 0.97K compared to the actual SST, which was slightly higher than that observed in previous studies. In addition, the error characteristics of the GMI SST were related to environmental factors, such as latitude, distance from the coast, sea wind, and water vapor volume. Errors tended to increase in areas close to coastal areas within 300 km of land and in high-latitude areas. In addition, relatively high errors were found in the range of weak wind speeds (<6 m s^-1) during the day and strong wind speeds (>10 m s^-1) at night. Atmospheric water vapor contributed to high SST differences in very low ranges of <30 mm and in very high ranges of >60 mm. These errors are consistent with those observed in previous studies, in which GMI data were less accurate at low SST and were estimated to be due to differences in land and ocean radiation, wind-induced changes in sea surface roughness, and absorption of water vapor into the microwave atmosphere. These results suggest that the characteristics of the GMI SST differences should be clarified for more extensive use of microwave satellite SST calculations in the seas around the Korean Peninsula, including a part of the Northwest Pacific.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.