• ETRI Journal
• /
• v.44 no.4
• /
• pp.573-587
• /
• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Journal of Ocean Engineering and Technology
• /
• v.36 no.3
• /
• pp.181-193
• /
• 2022

This study presents a mission management technique that is a key component of underwater docking system used to expand the operating range of autonomous underwater vehicle (AUV). We analyzed the docking scenario and AUV operating environment, defining the feasible initial area (FIA) level, event level, and global path (GP) command to improve the rate of docking success and AUV safety. Non-holonomic constraints, mounted sensor characteristic, AUV and mission state, and AUV behavior were considered. Using AUV and docking station, we conducted experiments on land and at sea. The first test was conducted on land to prevent loss and damage of the AUV and verify stability and interconnection with other algorithms; it performed well in normal and abnormal situations. Subsequently, we attempted to dock under the sea and verified its performance; it also worked well in a sea environment. In this study, we presented the mission management technique and showed its performance. We demonstrated AUV docking with this algorithm and verified that the rate of docking success was higher compared to those obtained in other studies.

• Journal of Korean Society of Disaster and Security
• /
• v.16 no.1
• /
• pp.81-89
• /
• 2023

Recently, due to global warming and urbanization due to the influence of abnormal weather, weather changes are increasing worldwide. Various measures have been proposed to reduce flood damage as flood volume increases due to problems such as an increase in impermeable area due to urbanization and reckless development. In this study, flow characteristics and overflow volume were analyzed using FLOW-3D, a three-dimensional CFD model, in accordance with changes in the cross-flow weir inlet angle installed in the meandering river section, and a basic study was conducted to evaluate the overflow capacity and calculate the flow coefficient. As a result of the analysis, the smaller the inflow angle of the transverse overflow, the lower the water level and flow rate of the main water flow after passing the transverse overflow, and the higher the inflow angle, the higher the water level and the flow rate. In addition, it was confirmed that the direct downstream flow rate decreased compared to the upstream flow rate when the inflow angle of the transverse overflow was 40° or higher.

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.394-394
• /
• 2023

The world's sustainable growth is being severely hampered by the inefficient use of water resources. Despite the widely acknowledged importance of trade in global and regional water and food security, societal reliance on local production as well as international trade remains inadequately assessed. Therefore, using South Korea as a case study, this study fills in this research gap by applying the virtual water concept, the logarithmic mean divisia index (LMDI) method, and the Tapio decoupling model. The virtual water concept was used to estimate South Korea's net virtual water trade for major grain crops from 1992 to 2017. Then, the LMDI method was utilized to assess the driving factors causing changes in net virtual water trade. Lastly, the Tapio decoupling model was used to investigate the decoupling relationships between economic growth and the driving factors of net virtual water trade. Results showed that South Korea remains a net importer of virtual water flows with respect to grain crops, with an average import of 16,559.24 million m³ over the study period. In addition, the change in net virtual water trade could be attributed to water intensity effect, product structure effect, economic effect, and population effect. However, water intensity and economic effects were the major decisive factors for decrease and increase in net virtual water trade respectively, while the population and product structure effects had minor positive influences on the net virtual water trade. Furthermore, water intensity and economic growth showed a strong decoupling in most periods, while the decoupling state between product structure and economic growth was observed as expansive negative decoupling. Likewise, population size and economic growth showed a weak decoupling in most periods. The results reveal South Korea's status as it concerns the virtual water trade of grain crops, thus providing valuable insights into the sustainability of trade activities for the management of local water resources.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.161-161
• /
• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• The Plant Pathology Journal
• /
• v.39 no.5
• /
• pp.486-493
• /
• 2023

Cowpea mild mottle virus (CPMMV) is a global plant virus that poses a threat to the production and quality of legume crops. Early and accurate diagnosis is essential for effective managing CPMMV outbreaks. With the advancement in isothermal recombinase polymerase amplification and lateral flow strips technologies, more rapid and sensitive methods have become available for detecting this pathogen. In this study, we have developed a reverse transcription recombinase polymerase amplification combined with lateral flow strips (RT-RPA-LFS) method for the detection of CPMMV, specifically targeting the CPMMV coat protein (CP) gene. The RT-RPA-LFS assay only requires 20 min at 40℃ and demonstrates high specificity. Its detection limit was 10 copies/µl, which is approximately up to 100 times more sensitive than RT-PCR on agarose gel electrophoresis. The developed RT-RPA-LFS method offers a rapid, convenient, and sensitive approach for field detection of CPMMV, which contribute to controlling the spread of the virus.

• Journal of the Economic Geographical Society of Korea
• /
• v.25 no.4
• /
• pp.514-530
• /
• 2022

Industrial clusters are being promoted in various ways to enhance industrial competitiveness around the world. This study aims to examine the formation and development process of regional industrial clusters in Bavaria, which are strengthening the competitiveness of local industrial enterprises and leading the continuous development of related industries in Germany, which shows stable industrial growth amidst global competition. To this end, this study first theoretically overviews the regional industrial clusters, followed by a case study of the development process and characteristics of cluster promotion policy in Bavaria, Germany. In particular, this study seeks to identify the formation and organization system of industrial clusters in Bavaria. Based on these analysis results, this study examines the main characteristics and success factors of regional industrial clusters in Bavaria, Germany, and tries to derive policy implications for creating and fostering industrial clusters in the future.

• Journal of Information Science Theory and Practice
• /
• v.10 no.spc
• /
• pp.56-65
• /
• 2022

Korea Institute of Science and Technology Information (KISTI) is a Worldwide LHC Computing Grid (WLCG) Tier-1 center mandated to preserve raw data produced from A Large Ion Collider Experiment (ALICE) experiment using the world's largest particle accelerator, the Large Hadron Collider (LHC) at European Organization for Nuclear Research (CERN). Physical medium used widely for long-term data preservation is tape, thanks to its reliability and least price per capacity compared to other media such as optical disk, hard disk, and solid-state disk. However, decreasing numbers of manufacturers for both tape drives and cartridges, and patent disputes among them escalated risk of market. As alternative to tape-based data preservation strategy, we proposed disk-only erasure-coded archival storage system, Custodial Disk Storage (CDS), powered by Exascale Open Storage (EOS), an open-source storage management software developed by CERN. CDS system consists of 18 high density Just-Bunch-Of-Disks (JBOD) enclosures attached to 9 servers through 12 Gbps Serial Attached SCSI (SAS) Host Bus Adapter (HBA) interfaces via multiple paths for redundancy and multiplexing. For data protection, we introduced Reed-Solomon (RS) (16, 4) Erasure Coding (EC) layout, where the number of data and parity blocks are 12 and 4 respectively, which gives the annual data loss probability equivalent to 5×10^-14. In this paper, we discuss CDS system design based on JBOD products, performance limitations, and data protection strategy accommodating EOS EC implementation. We present CDS operations for ALICE experiment and long-term power consumption measurement.