• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.4
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• pp.491-505
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• 2018

As an alternative to deep geological disposal technology, which is considered as a reference concept, the domestic applicability of deep borehole disposal technology for high level radioactive waste, including spent fuel, has been preliminarily evaluated. Usually, the environment of deep borehole disposal, at a depth of 3 to 5 km, has more stable geological and geo-hydrological conditions. For this purpose, the characteristics of rock distribution in the domestic area were analyzed and drilling and investigation technologies for deep boreholes with large diameter were evaluated. Based on the results of these analyses, design criteria and requirements for the deep borehole disposal system were reviewed, and preliminary reference concept for a deep borehole disposal system, including disposal container and sealing system meeting the criteria and requirements, was developed. Subsequently, various performance assessments, including thermal stability analysis of the system and simulation of the disposal process, were performed in a 3D graphic disposal environment. With these analysis results, the preliminary evaluation of the domestic applicability of the deep borehole disposal system was performed from various points of view. In summary, due to disposal depth and simplicity, the deep borehole disposal system should bring many safety and economic benefits. However, to reduce uncertainty and to obtain the assent of the regulatory authority, an in-situ demonstration of this technology should be carried out. The current results can be used as input to establish a national high-level radioactive waste management policy. In addition, they may be provided as basic information necessary for stakeholders interested in deep borehole disposal technology.

• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.380-387
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• 2019

Of great importance in food safety culture are the values of an organization regarding food safety that combine the human and material requirements needed to produce safe and hygienic foods. In recent years, efforts have been made to improve the level of implementation of food safety management systems by improving certain cultural elements of food safety. This study investigated the current state of food safety culture in the HMR manufacturing sector. An anonymous survey of 46 HMR manufacturers of various sizes was conducted to evaluate the implementation status of HACCP prerequisite program and food safety culture. The perceived importance of food safety culture factors and their performance were also surveyed. Employees of HMR manufacturers who participated in this survey recognized that the participation of employees and leadership was the most important factor in ensuring food safety. Smaller enterprises are less aware of the importance of such organizational culture. The survey shows that food safety culture indicators in large companies are generally higher than those of small and medium enterprises. Especially, the manager's level of commitment to food safety, resources input, and education and training was significantly higher than that found at small companies (p=0.005). Among the food safety culture evaluation factors, it was found that education and training had significant influence on HACCP prerequisite program performance. Continued employee education and training on food safety and hygiene are important for HMR manufacturers to achieve HACCP certification standards.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.565-568
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• 2014

This paper presents a 0.2V DC/DC boost converter with regulated output for thermoelectric energy harvesting. To use low voltages from a thermoelectric device, a start-up circuit consisting of native NMOS transistors and resistors boosts an internal VDD, and the boosted VDD is used to operate the internal control block. When the VDD reaches a predefined value, a detector circuit makes the start-up block turn off to minimize current consumption. The final boosted VSTO is achieved by alternately operating the sub-boost converter for VDD and the main boost converter for VSTO according to the comparator outputs. When the VSTO reaches 2.4V, a buck converter starts to operate to generate a stabilized output VOUT. Simulation results shows that the designed converter generates a regulated 1.8V output from an input voltage of 0.2V, and its maximum power efficiency is 60%. The chip designed using a $0.35{\mu}m$ CMOS process occupies $1.1mm{\times}1.0mm$ including pads.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.41-46
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• 2021

As data acquisition using unmanned aerial vehicles is widely used, as one of the ways to increase the accuracy of photogrammetry using unmanned aerial vehicles, a method of inputting both vertical and oblique images in bundle adjustment of aerial triangulation has been proposed. In this study, in order to find a suitable method for increasing the accuracy of photogrammetry, the accuracy of the case of adjusting the oblique images taken at different shooting angles and the case of adjusting the oblique images with different shooting angles at the same time with the vertical images were compared. As a result of the study, it was found that the error of the checkpoint decreases as the angle of the input oblique images increases. In particular, when the vertical images and oblique images are used together, the height error decreases significantly as the angle of the oblique images increases. The current 『Aerial Photogrammetry Work Regulation』 requires RMSE (Root Mean Square Error), which is the same as GSD (Ground Spatial Distance) of a vertical image. When using an oblique images with a shooting angle of 50°, a result close to this standard is obtained. If the vertical images and the 50° oblique images were adjusted at the same time, the work regulations could be satisfied. Using the results of this study, it is expected that photogrammetry using low-cost cameras mounted on unmanned aerial vehicles will become more active.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.1
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• pp.17-32
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• 2022

The buffer capacity of southern Korean waters in summer was quantified using data set of temperature, salinity, dissolved inorganic carbon, total alkalinity obtained from August 2020 cruise. The geographical distribution and variability of six buffer factors, which amended the existing Revelle factor, are discussed their relationship with the hydrological parameters of temperature and salinity. The calculated results of six buffer factors showed the spatial variations according to the distributions of various water masses. The buffer capacity was low in the East Sea Surface Mixed Water (ESMW) and South Sea Surface Mixed Water (SSMW) where upwelling occurred, and showed an intermediate value in the Yellow Sea Surface Water (YSSW). In addition, the buffer capacity increased in the order of high temperature Tsushima Warm Current (TWC) and Changjiang Diluted Water (CDW). This means that the Changjiang discharge water in summer strengthens the buffer capacity of the study area. The highest buffer capacity of CDW is due to its relatively higher temperature and biological productivity, and a summer stratification. Temperature showed a good positive correlation (R²=0.79) with buffer capacity in all water masses, whereas salinity exhibited a poor negative correlation (R²=0.30). High temperature strengthens buffer capacity through thermodynamic processes such as gas exchange and distribution of carbonate system species. In the case of salinity, the relationship with buffer capacity is reversed because salinity of the study area is not controlled by precipitation or evaporation but by a local freshwater input and mixing with upwelled water.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.