• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.181-190
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• 2021

In this study, a large-scale levee breach experiment from lateral overflow was conducted to verify the effect of the new reinforcement method applied to the levee's surface. The new method could prevent levee failure and minimize damage caused by overflow in rivers. The levee was designed at the height of 2.5 m, a length of 12 m, and a slope of 1:2. A new material mixed with biopolymer powder, water, weathered granite, and loess in an appropriate ratio was sprayed on the levee body's surface at a thickness of about 5 cm, and vegetation recruitment was also monitored. At the Andong River Experiment Center, a flow (4 ㎥/s) was introduced from the upstream of the A3 channel to induce the lateral overflow. The change of lateral overflow was measured using an acoustic doppler current profiler in the upstream and downstream. Additionally, cameras and drones were used to analyze the process of the levee breach. Also, a new method using 3D point cloud for calculating the surface loss rate of the levee over time was suggested to evaluate the performance of the levee reinforcement method. It was compared to existing method based on image analysis and the result was reasonable. The proposed 3D point cloud methodology could be a solution for evaluating the performance of levee reinforcement methods.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.4
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• pp.53-61
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• 2022

Growing agricultural products in greenhouses controlled by creating suitable climatic conditions and root zone of crop has been an important research and application subject. Appropriate environmental conditions in greenhouse are necessary for optimum plant growth improved crop yields. This study aimed to establish web-based remote monitoring system which monitors crops growth environment and status of crop on a real-time basis by applying to greenhouses IT technology connecting greenhouse equipment such as temperature sensors, soil sensors, crop sensors and camera. The measuring items were air temperature, relative humidity, solar radiation, CO₂ concentration, EC and pH of nutrient solution, medium temperature, EC of medium, water content of medium, leaf temperature, sap flow, stem diameter, fruit diameter, etc. The developed greenhouse monitoring system was composed of the network system, the data collecting device with sensors, and cameras. Remote monitoring system was implemented in a server/client environment. Information on greenhouse environment and crops is stored in a database. Items on growth and environment is extracted from stored information, could be compared and analyzed. So, A integrated monitoring system for smart greenhouse would be use in application practice and understanding the environment and crop growth for smart greenhouse management. sap flow, stem diameter and pant-water relations

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.164-173
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• 2021

Recently, as underground space safety issues such as sink hole, ground subsidence and damage to old underground facilities have been increasing in urban areas, the precise management of underground facilities ins more required. Thus, this study developed a function to that, visualize on Integration Map of Underground Geospatial Information a real-time survey data of underground facilities acquired on site or underground facility survey data acquired through on-site survey after underground facility exploration and developed a function convert to surveying-results. In addition, using the on-site survey performance utilization function in connection with the Integration Map of Underground Geospatial Information developed through this study, the surveying -results obtained with the Total-station at the water pipeline burial construction site in Eunpyeong-gu, Seoul are visualized on the Integration Map of Underground Geospatial Information and On-site verification was performed by converting spatial-information performance files and transmitting the Integration Map of Underground Geospatial Information to the mobile center. Based on this, it was possible to verify the work procedure using the surveying-results in the area where the Integration Map of Underground Geospatial Information was built, and to review the direction of future improvement directions.

• Knowledge Management Research
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• v.23 no.2
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• pp.15-35
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• 2022

With the development of virtual and augmented reality technologies, the metaverse, a digital world that provides an immersive feeling like the real world, is overgrowing. Many IT companies such as Naver, Facebook (Meta), and NVIDIA are developing innovative technologies and launching the Metaverse platform and related products on the market. However, even though it is a new business in which many global big tech companies are aggressively investing, the results are not yet precise compared to the market expectations, and the rate of increase in the number of users is gradually slowing down. This can be attributed to the lack of consideration and understanding about how to grow the metaverse ecosystem and operate & harmonize various users/components from the time the metaverse platform was designed. In order to propose a better solution to these problems, this study adopts the yin-yang and five elements theory, which was created to understand the operation logic and logic of the human world for thousands of years. This research would like to propose a theory of double lines-five elements by defining two essential spaces of the metaverse platform, online and offline, and five essential elements constituting the metaverse platform. This study intends to provide a theoretical lens on how to design and operate a platform through the double lines and five elements theory and the concept of coexistence and polarity between the five elements.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.187-192
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• 2022

The purpose of this study is to examine the effect of herbal medicine complex (HMC) containing Camellia sinensis L., Duchoesna chrysantha, Houttuynia cordata Thunberg, Poncirus trifoliata Rafinesque on skin inflammation and atopic dermatitis. First, we examined the anti-inflammatory effect of HMC in TNF-α induced human keratinocytes (HaCaT cell). Real-time PCR and western blotting were performed to evaluate the expression of inflammatory cytokines (e.g., iNOS, COX-2, IL-6, IL-8) mRNA and protein. Four-weeks old male NC/Nga mice were treated with 1% 2,4-dinitrochlorobenzene (DNCB) solution and used as an atopic dermatitis mice model. And, HMC (200 mg/kg or 400 mg/kg) was administered directly into the stomach of mice for 4 weeks, and blood or serum analysis, tissue staining were performed after oral gavage. As a result HMC inhibited the mRNA expression of iNOS, COX-2, IL-6, and IL-8, which had been increased by TNF-α in HaCaT cells. In addition, the protein expression was also significantly suppressed in the same way as the mRNA expression results. The in vivo experiment results showed that, HMC administration reduced thickening of the epidermis and infiltration of eosinophil into the skin stratum basale compared to DNCB treatment. In addition, HMC administration significantly reduced the inflammatory cytokines (IL-4, IL-5, IL-6, and IL-13) production and immunocyte (white blood cell, lymphocyte, neutrophil, and eosinophil) count compared to DNCB treatment. Moreover, the serum IgE and histamine level was decreased by HMC administration. These results suggest that HMC can be used as effective herbal medicine extract for skin inflammation and atopic dermatitis. And this study may contribute to the development of the herbal medicine-based drug for the treatment of skin inflammation and atopic dermatitis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.935-940
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• 2023

Digital-twin technology is emerging as an innovative solution for all industries, including manufacturing and production lines. Therefore, this paper optimizes all the energy used in a biomass plant based on unused resources. We will then implement a digital-twin prototype for biomass plants and evaluate its performance in order to improve the efficiency of plant operations. The proposed digital-twin prototype applies a standard communication platform between the framework and the gateway and is implemented to enable real-time collaboration. and, define the message sequence between the client server and the gateway. Therefore, an interface is implemented to enable communication with the host server. In order to verify the performance of the proposed prototype, we set up a virtual environment to collect data from the server and perform a data collection evaluation. As a result, it was confirmed that the proposed framework can contribute to energy optimization and improvement of operational efficiency when applied to biomass plants.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.

• Economic and Environmental Geology
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• v.51 no.4
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• pp.359-370
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• 2018

The batch and column experiments were performed to overcome the limitation of the neutralization process using the $scCO_2$-water-recycled aggregate, reducing its treatment time to 3 hour. The waste cement mortar and two kinds of recycled aggregate were used for the experiment. In the extraction batch experiment, three different types of waste mortar were reacted with water and $scCO_2$ for 1 ~ 24 hour and the pH of extracted solution from the treated waste mortar was measured to determine the minimum reaction time maintaining below 9.8 of pH. The continuous column experiment was also performed to identify the pH reduction effect of the neutralization process for the massive recycled aggregate, considering the non-equilibrium reaction in the field. Thirty five gram of waste mortar was mixed with 70 mL of distilled water in a high pressurized stainless steel cell at 100 bar and $50^{\circ}C$ for 1 ~ 24 hour as the neutralization process. The dried waste mortar was mixed with water at 150 rpm for 10 min. and the pH of water was measured for 15 days. The XRD and TG/DTA analyses for the waste mortar before and after the reaction were performed to identify the mineralogical change during the neutralization process. The acryl column (16 cm in diameter, 1 m in length) was packed with 3 hour treated (or untreated) recycled aggregate and 220 liter of distilled water was flushed down into the column. The pH and $Ca^{2+}$ concentration of the effluent from the column were measured at the certain time interval. The pH of extracted water from 3 hour treated waste mortar (10 ~ 13 mm in diameter) maintained below 9.8 (the legal limit). From XRD and TG/DTA analyses, the amount of portlandite in the waste mortar decreased after the neutralization process but the calcite was created as the secondary mineral. From the column experiment, the pH of the effluent from the column packed with 3 hour treated recycled aggregate kept below 9.8 regardless of their sizes, identifying that the recycled aggregate with 3 hour $scCO_2$ treatment can be reused in real construction sites.