• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.53-65
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• 2022

Most of the reservoirs managed by the city and county are small and it is difficult to respond to climate change because the drainage area is small and the inflow increases rapidly when a heavy rain occurs. In this study, the current status of reservoirs managed by city and county in Gyeonggi-do was reviewed and flood vulnerability due to climate change was analyzed. In order to analyze the impact of climate change, CMIP6-based future climate scenario provided by IPCC was used, and future rainfall data was established through downscaling of climate scenario (SSP8-8.5). The flood vulnerability of reservoirs due to climate change was evaluated using the concept provided by the IPCC. The future annual precipitation at six weather stations appeared a gradual increase and the fluctuation range of the annual precipitation was also found to increase. As a result of calculating the flood vulnerability index, it was analyzed that the flood vulnerability was the largest in the 2055s period and the lowest in the 2025s period. In the past period (2000s), the number of D and E grade reservoirs was 58, but it was found to increase to 107 in the 2055s period. In 2085s, there were 17 E grade reservoirs, which was more than in the past. Therefore, it is necessary to take measures against the increasing risk of flooding in the future.

• Smart Structures and Systems
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• v.31 no.2
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.41-47
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• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• Journal of Korea Trade
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• v.27 no.4
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• pp.45-60
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• 2023

Purpose - This research focused on women who enjoy watching Korean dramas and K-pop, as well as how their desire to imitate are influenced by their viewing habits. Due to the influence of their idols, women who aspire to copy and are influenced by their idols will desire to purchase Korean products. This cultural export strategy has effectively persuaded the global community, particularly women. Indonesia with a large population can be a reference for the industry to increase sales of South Korean beauty products, especially in the ASEAN region. Design/methodology - This research used a quantitative approach with an online questionnaire. This questionnaire had two steps: the pre-questionnaire and the questionnaire itself. The different measuring tools that were already in use when the data were being collected helped to determine how much each variable meant. As a part of this research project, 410 Indonesian women filled out the questionnaire in order to share their thoughts as they were the focus of the study. SMART PLS was used to analyze the data. Findings - One of the most essential findings from establishing the Korean Wave effect on purchase intention was the imitation intention variable. Someone who has the aspiration to be just like their idol will be willing to give anything in order to achieve that goal. One strategy is to buy things that are similar to the ones you want to imitate in order to stimulate demand for Korean products. People's imitation intention and attitude toward Korean products will increase as a result of Korean drama and K-pop elements that display one's idols with fashionable appearances and good-looking faces, which will lead to purchase intentions. Originality/value - The Korean Wave has had a beneficial impact on the intention to imitate and the attitude toward Korean items, both of which will favorably boost the intention to acquire Korean goods. In order to boost sales in international markets, particularly in Indonesia, the Korean business sector needs to increase the number of artists and singers it employs for product promotion. . Mutualism effect between the government, the entertainment industry, and the beauty product industry to increase sales of South Korean beauty products.

• Physical Therapy Korea
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• v.30 no.3
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• pp.194-201
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• 2023

Background: Falls are a common and serious problem in the elderly population. Muscle strength and balance are important factors in the prevention of falls. The Y-balance test (YBT) is used to assess dynamic postural control and shows excellent test-retest reliability. However, no studies have examined the relationship between lower-limb strength and YBT scores in elderly women. Objects: This study aimed to examine the relationship between lower-limb strength and YBT scores in elderly women. Methods: Thirty community-dwelling elderly women participated in the study. Lower-limb strength including hip flexor, hip extensor, hip abductor (HAB), hip adductor (HAD), knee flexor, knee extensor, ankle dorsiflexor, and ankle plantar flexor (PF) muscles was examined using a smart KEMA strength sensor (KOREATECH Inc.), and the YBT was used to assess dynamic balance. Relationship between lower-limb strength and YBT was demonstrated using a Pearson's correlation coefficient. Results: HAB strength (r = 0.388, p < 0.05), HAD strength (r = 0.362, p < 0.05), and ankle PF strength (r = 0.391, p < 0.05) positively correlated with the YBT-anterior direction distance. Ankle PF strength was positively correlated with the YBT-posteromedial direction distance (r = 0.396, p < 0.05) and composite score (r = 0.376, p < 0.05). Conclusion: The results of this study suggest that HAB, HAD, and ankle PF strengths should be considered for dynamic postural control in elderly women.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.47-53
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• 2023

With the advent of the 4th Industrial Revolution, diverse and a large amount of data has been accumulated now. The agricultural community has also collected environmental data that affects the growth of crops in smart farms or open fields with sensors. Environmental data has different features depending on where and when they are measured. Studies have been conducted using collected agricultural data to predict growth and yield with statistics and artificial intelligence. The results of these studies vary greatly depending on the data on which they are based. So, studies to enhance data quality have also been continuously conducted for performance improvement. A lot of data is required for high performance, but if there are outlier or missing values in the data, it can greatly affect the results even if the amount is sufficient. So, adjustment of outlier and missing values is essential in the data preprocessing. Therefore, this paper integrates data collected from actual farms and proposes a adjustment system for outlier and missing values based on it.

• Journal of Digital Contents Society
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• v.12 no.4
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• pp.601-609
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• 2011

In developing countries, economically under-privileged are mostly consisted of women, therefore supporting those women signify supporting the local society and the family. Advancement of women's economic status not only contributes to her family but also to the local society, nation, and to the global world as a whole. This paper is a research on local informatization and successful model in e-business for Indonesia, which established interactive research model networking with Korea-Indonesia Research Institution and policy-makers for two years and susggested practical research model through visiting Pekalongan. Through activated interaction between women enterprises and policy makers from Korea and Indonesia, the research paper seeks to create research based network and provide opportunities of information access and business matching to local informatized and e-business enterprises. In research adopted regions, city development project has been accomplished in human, business and environmental field since 2005, and have selected Pekalongan region where infra is settled to certain extent. With the information about Indonesia's city development project, investigation on Pekalongan's current geographical, humanistic status quo, the paper aims to and create Indonesian female e-business professionals, e-business user, e-business producers and provide successful model on Pekalongan's local informatization and e-business.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.94-106
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• 2018

The epidemiological model is useful for creating simulation and associated preventive measures for disease spread, and provides a detailed understanding of the spread of disease space through contact with individuals. In this study, propose an agent-based spatial model(ABM) integrated with spatial big data to simulate the spread of MERS-CoV infections in real time as a result of the interaction between individuals in space. The model described direct contact between individuals and hospitals, taking into account three factors : population, time, and space. The dynamic relationship of the population was based on the MERS-CoV case in Seoul Metropolitan Government in 2015. The model was used to predict the occurrence of MERS, compare the actual spread of MERS with the results of this model by time series, and verify the validity of the model by applying various scenarios. Testing various preventive measures using the measures proposed to select a quarantine strategy in the event of MERS-CoV outbreaks is expected to play an important role in controlling the spread of MERS-CoV.