• International Journal of Knowledge Content Development & Technology
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• v.11 no.4
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• pp.7-34
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• 2021

The purpose of the study was to explore the uses and gratification of social media among first-year student groups at a satellite campus of a public university in Ghana. The study employed a descriptive survey design. The study involved all 1061 first-year university students in six academic departments of the College. A total of 680 (64%) participants returned validly completed copies of the questionnaire. Descriptive statistics and thematic analysis were employed for data analysis. The findings indicate that WhatsApp was the most popular application for social media groups, while a need for information-sharing, peer-tutoring and learning, and finding and keeping friends were the primary motivations for joining social media groups. First-year students are involved mainly in reactive activities, as most engage when solving an academic assignment through group discussions. Though challenges persist, such as posting of unwanted images, inadequate participation, and ineffective and irrelevant communication, most are willing to continue their social media groups' membership in the long term. This study provides valuable insight into transitioning students' lived experiences on social media from the group perspective. These insights are valuable conceptually and practically to academic counsellors, librarians and student affairs officers who are expected to provide on-going education on (social) media literacy to first-year students to enhance the adjustment process. The study is the first of its kind in Ghana that investigates social media group participants' exit intentions.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.137-152
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• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.3
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• pp.1-14
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• 2024

Gaining an accurate 3D stream geometry has become feasible with Unmanned Aerial Vehicle (UAV), which is crucial for better understanding stream hydrodynamic processes. The objective of this study was to investigate series of filters to remove stream vegetation and propose the best method for generating Digital Terrain Models (DTMs) using UAV-based point clouds. A stream reach approximately 500 m of the Bokha stream in Icheon city was selected as the study area. Point clouds were obtained in August 1st, 2023, using Phantom 4 multispectral and Zenmuse L1 for Structure from Motion (SfM) and Light Detection And Ranging (LiDAR) respectively. Three vegetation filters, two morphological filters, and six composite filters which combined vegetation and morphological filters were applied in this study. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used to assess each filters comparing with the two cross-sections measured by leveling survey. The vegetation filters performed better in SfM, especially for short vegetation areas, while the morphological filters demonstrated superior performance on LiDAR, particularly for taller vegetation areas. Overall, the composite filters combining advantages of two types of filters performed better than single filter application. The best method was the combination of Progressive TIN (PTIN) and Color Indicies of Vegetation Extraction (CIVE) for SfM, showing the smallest MAE of 0.169 m. The proposed method in this study can be utilized for constructing DTMs of stream and thus contribute to improving the accuracy of stream hydrodynamic simulations.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.281-289
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• 2015

Information on sunshine duration and solar radiation are indispensable to the understanding of crop growth and development. Yet, relevant variables are not available in the Korea Meteorological Administration's (KMA) digital forecast. We proposed the methods of estimating sunshine duration and solar radiation based on the 'sky condition' of digital forecast products and validated using the observed data. The sky condition values (1 for clear, 2 for partly cloudy, 3 for cloudy, and 4 for overcast) were collected from 22 weather stations at 3-hourly intervals from August 2013 to July 2015. According to the observed relationship, these data were converted to the corresponding amount of clouds on the 0 to 10 scale (0 for clear, 4 for partly cloudy, 7 for cloudy, and 10 for overcast). An equation for the cloud amount-sunshine duration conversion was derived from the 3-year observation data at three weather stations with the highest clear day sunshine ratio (i.e., Daegwallyeong, Bukgangneung, and Busan). Then, the estimated sunshine hour data were used to run the Angstrom-Prescott model which was parameterized with the long-term KMA observations, resulting in daily solar radiation for the three weather stations. Comparison of the estimated sunshine duration and solar radiation with the observed at those three stations showed that the root mean square error ranged from 1.5 to 1.7 hours for sunshine duration and from 2.5 to $3.0MJ\;m^{-2}\;day^{-1}$ for solar radiation, respectively.

• Journal of Veterinary Clinics
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• v.29 no.1
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• pp.18-22
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• 2012

This study was performed to evaluate the sensitivity of conventional film-screen radiography (CFSR) and direct digital radiography (DDR) for detection of various amounts of free peritoneal fluid. Ten adult male healthy beagles were used in this study. Radiographic examinations were performed in the right lateral and ventrodorsal positions. Fluid was injected in increments of 2.0 ml/kg of body weight up to 20.0 ml/kg of body weight. The images of CFSR and DDR were evaluated by two veterinary radiologists for evidence of abdominal fluid without knowledge of injected fluid volume. Data were evaluated by using the receiver operation curve (ROC) analysis and the area under the curve (AUC). There was no significant difference in detection of peritoneal fluid between DDR and CFSR in the ROC analysis. The accuracy of CFSR (0.805) was relatively higher than that of DDR (0.733), based on the ROC analysis and AUC. AUC of CFSR was higher in most injection doses. These results suggest that CFSR is more accurate than DDR for the detecting peritoneal fluid. Therefore, for situation in which digital radiographs are equivocal or small amount of fluid is suspected, other imaging modalities, such as ultrasonography would be helpful for determining the presence of fluids.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.568-573
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• 2018

UAV(Unmanned Aerial Vehicle) is widely used in space information construction, agriculture, fisheries, weather observation, communication, and entertainment fields because they are cheaper and easier to operate than manned aircraft. In particular, UAV have attracted much attention due to the speed and cost of data acquisition in the field of spatial information construction. However, ortho image images produced using UAVs are distorted in buildings and forests. It is necessary to solve these problems in order to utilize the geospatial information field. In this study, fixed wing, rotary wing, vertical take off and landing type UAV were used to detect distortions of ortho image of UAV under various conditions, and various object areas such as construction site, urban area, and forest area were captured and analysed. Through the research, it was found that the redundancy of the unmanned aerial vehicle image is the biggest factor of the distortion phenomenon, and the higher the flight altitude, the less the distortion phenomenon. We also proposed a method to reduce distortion of orthoimage by lowering the resolution of original image using DTM (Digital Terrain Model) to improve distortion. Future high-quality unmanned aerial vehicles without distortions will contribute greatly to the application of UAV in the field of precision surveying.

• Journal of Digital Convergence
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• v.12 no.9
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• pp.219-223
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• 2014

In line with the advanced wireless communication technology, M2M (Machine-to-Machine) communication has drawn attention in industry. M2M communication features are installed and operated in the fields where human accessibility is highly limited such as disaster, safety, construction, health and welfare, climate, environment, logistics, culture, defense, medical care, agriculture and stockbreeding. In M2M communication, machine replaces people for automatic communication and countermeasures as part of unmanned information management and machine operation. Wireless M2M inter-device communication is likely to be exposed to intruders' attacks, causing security issues, which warrants proper security measures including cross-authentication of whether devices are legitimate. Therefore, research on multiple security protocols has been conducted. The present study applied SessionKey, HashFunction and Nonce to address security issues in M2M communication and proposed a safe protocol with reinforced security properties. Notably, unlike most previous studies arguing for the security of certain protocols based on mathematical theorem proving, the present study used the formal verification with Casper/FDR to prove the safety of the proposed protocol. In short, the proposed protocol was found to be safe and secure.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.177-183
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• 2016

The purpose of this study is to analyze the affecting factors and debt characteristics with household debt. The method of analysis was used SPSS 22.0. The main results were as follows. First, psychological debt burden group were found to be lower academic background, more household members, job in agriculture, forestry and fisheries and women among demographic characteristics. This suggests the need for the governments who are interested in the risk from household debt. Second, psychological debt burden group showed higher level of debt and average monthly debt repayments. This study implies that it is necessary to do financial education and consulting. Third, psychological debt burden group seemed that the households with more the average monthly interest payments and repayments on income, the more total liabilities compared to total assets. As the ages get higher, it was gradually increased. It suggests that the development of psychological counseling program for debtor with household debt. Other factors(property prices or the financial environments) will be dealt with in the subsequent studies.