• Journal of Life Science
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• v.32 no.7
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• pp.578-587
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• 2022

Korea, as the world's 7^th largest emitter of greenhouse gases, has raised the national greenhouse gas reduction target as international regulations have been strengthened. As it is possible to utilize coastal and marine ecosystems as important nature-based solutions (NbS) for implementing climate change mitigation or adaptation plans, the blue carbon ecosystem is now receiving attention. Blue carbon refers to carbon that is deposited and stored for a long period after carbon dioxide (CO₂) is absorbed as biomass by coastal ecosystems or oceanic ecosystems through photosynthesis. Currently, there are only three blue carbon ecosystems officially recognized by the Intergovernmental Panel on Climate Change (IPCC): mangroves, salt marshes, and seagrasses. However, the results of new research on the high CO₂ sequestration and storage capacity of various new blue carbon sinks, such as seaweeds, microalgae, coral reefs, and non-vegetated tidal flats, have been continuously reported to the academic community recently. The possibility of IPCC international accreditation is gradually increasing through scientific verification related to calculations. In this review, the current status and potential value of seaweeds, seagrass fields, and non-vegetated tidal flats, which are sources of blue carbon on the east coast, are discussed. This paper confirms that seaweed resources are the most effective NbS in the East Sea of Korea. In addition, we would like to suggest the direction of research and development (R&D) and utilization so that new blue carbon sinks can obtain international IPCC certification in the near future.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.401-408
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• 2022

As research on a controlled environment system based on crop growth environment sensing for sustainable production of horticultural crops and its industrial use has been important, research on how to properly utilize soil moisture sensors for outdoor cultivation is being actively conducted. This experiment was conducted to suggest the proper method of utilizing the TEROS 12, an FDR (frequency domain reflectometry) sensor, which is frequently used in industry and research fields, for each orchard soil in three regions in Korea. We collected soils from each orchard where fruit trees were grown, investigated the soil characteristics and soil water retention curve, and compared TEROS 12 sensor calibration equations to correlate the sensor output to the corresponding soil volumetric water content through linear and cubic regressions for each soil sample. The estimated value from the calibration equation provided by the manufacturer was also compared. The soil collected from all three orchards showed different soil characteristics and volumetric water content values by each soil water retention level across the soil samples. In addition, the cubic calibration equation for TEROS 12 sensor showed the highest coefficient of determination higher than 0.95, and the lowest RMSE for all soil samples. When estimating volumetric water contents from TEROS 12 sensor output using the calibration equation provided by the manufacturer, their calculated volumetric water contents were lower than the actual volumetric water contents, with the difference up to 0.09-0.17 m³·m^-3 depending on the soil samples, indicating an appropriate calibration for each soil should be preceded before FDR sensor utilization. Also, there was a difference in the range of soil volumetric water content corresponding to the soil water retention levels across the soil samples, suggesting that the soil water retention information should be required to properly interpret the volumetric water content value of the soil. Moreover, soil with a high content of sand had a relatively narrow range of volumetric water contents for irrigation, thus reducing the accuracy of an FDR sensor measurement. In conclusion, analyzing soil water retention characteristics of the target soil and the soil-specific calibration would be necessary to properly quantify the soil water status and determine their adequate irrigation point using an FDR sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.69-77
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• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.694-709
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• 2021

Purpose: In this study, earthquake-induced landslide risk assessment was conducted to provide basic data for efficient and preemptive damage prevention by selecting the erosion control work before the earthquake and the prediction and restoration priorities of the damaged area after the earthquake. Method: The study analyzed the previous studies abroad to examine the evaluation methodology and to derive the evaluation factors, and examine the utilization of the landslide hazard map currently used in Korea. In addition, the earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Result: The earthquake-induced landslide risk assessment study showed that China ranked 44%, Italy 16%, the U.S. 15%, Japan 10%, and Taiwan 8%. As for the evaluation method, the statistical model was the most common at 59%, and the physical model was found at 23%. The factors frequently used in the statistical model were altitude, distance from the fault, gradient, slope aspect, country rock, and topographic curvature. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. Conclusion: It is effective to use the landslide hazard map to evaluate the risk of earthquake-induced landslides at the regional scale. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The Journal of Natural Sciences
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• v.14 no.1
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• pp.83-121
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• 2004

This study was carried out to examine how solid the production foundation of rice in Chung-Nam Province is, and, if not, to probe alternative measures through the size of farms specializing in rice, of which direction would be a pivot of rice industry-oriented policy. The results obtained can be summarized as follows : 1. The amount of rice production in Chung-Nam Province is highest in Korea and the size of paddy field area is the second largest : This implying that the probability that rice production in Chung-Nam Province would be severely influenced by a global trend of market conditions. The number of farms specializing in rice becoming the core group of rice farming account for 7.7 percent of the total number of farm household in Korea. Average field area financial support which had been input to farm household by Government had a noticeable effect on the improvement of the policy of farm-size program. 2. Farm-size program in Chung-Nam Province established from 1980 to 2002 in creased the cultivation size of paddy field to 19,484 hectares, and this program enhanced the buying and selling of farmland and the number of farmland bargain reached 6,431 household and 16,517 hectares, respectively, in 1995-2002. Meanwhile, long-term letting and hiring of farmland appeared so active that the bargain acreage reached 6,970 hectares, and farm involved was 7,059 households, however, the farm-exchange-and-unity program did not satisfy our expectation, because the retirement farm operators reluctantly participated to sell their farms. Another reason that had delayed the bargain of farms rested on the general category of social complication attendant upon the exchange and unity operation for scattered farm. Such difficulties would work negative effects out to carry on the target of farm-size work in general. 3. The following measures were presented to propel the farm-size promotion program : a. Occupation shift project, followed by the social security program for retirement and elderly farm operators, should be promptly established and also a number of types of incentives for promoting the letting and hiring work and farm-exchange-and-unity program would also be set up. b. To establish the effective key system of rice production, all the farm operators should increase the unit area yield of rice and lower the production cost. To do so, a great deal of production teams of rice equipped with managerial techniques and capabilities need to be organized. And, also, there should be appropriate arrays of facilities including information system. This plan is desirable to be in line with a diversity of the structural implement of regional integration based on farm system building. c. To extend the size of farm and to improve farm management, we have to devise the enlargement of individual size of farm for maximized management and the utilization of farm-size grouping method. In conclusion, it can be said that the farm-size project in Chung-Nam Province which has continued since the 1980s was satisfactorily achieved. However, we still have a lot of problems to be solved to break down the barrier for attainment of the desirable farm-size operation work.. Farm-size project has fairly close relation with farm specialization in rice and, thus, the positive support for farm household including the integrated program for both retirement farmers and off-farm operators should be considered to pursue the progressive development of the farm-size program, which is key means to successful achievement of rice farming enforcement in Chung-Nam Province.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• The Korean Journal of Archival Studies
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• no.29
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• pp.175-223
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• 2011

A university is an organization charged with publicity and has accountability to the community for the operating process. Students account for a majority of members in a university. In universities, numerous creatures are pouring out every year and university students are major producers of these records. However, roles and functions of university students producing enormous amount of records as main agents of universities and focused concentration on produced records have not been made yet. It is reality that from the archival point of view, the importance of produced records of which main agents are university students has been relatively underestimated. In this background, this study attempted approach in archival point of view on records produced by university students, main agents. There are various types of records that university students produce such as records produced in the process of research and teaching as well as records produced in the process of various autonomy activities like clubs, students' associations. This study especially focused on university student autonomy activity process and placed emphasis on accountability securing measures on autonomy activity process of university students. To secure accountability of activities, records management should be based. Therefore, as a way to ensure accountability of unversity students autonomy activity, we tried to present records management systematization and records utilization measures. For this, a student body, a university student autonomy organization was analyzed and a student body of Myongji University Humanities Campus was selected as a specific target. First, to identify records management status, activities and organization and functions of the student body, we conducted an interview with the president of the student body. Through this, we analyzed the activities of the university student body and examined the necessity of accountability accordingly. Also, we derived the types and characteristics of records to be produced at each stage by analyzing the organization and functions of the student body of Myongji University. Like this, after deriving the types of production records according to the necessity, organization and functions of accountability and activities of the student body, we analyzed records management status of the present student body. First, to identify the general process status of activities of the student body, we analyzed activity process by stage of the student body of Myongji University. And we analyzed records management method of the student body and responsibility principal and conducted real condition analysis. Through this analysis, we presented the measures to ensure accountability of a university student body in three categories such as systematization of records management process, establishment of records management infrastructure, accountability guarantee measures. This study discussed accountability on society by analyzing activities and functions of a student body, targeting a student body, an autonomy organization of university students. And as a measure to secure accountability of a student body, we proposed a model for records management environment settlement. But in terms that a student body is an organization operated in one year basis, there is a limit that records management environment is hard to settle. This study pointed out this limit and was to provide clues when more active researches were carried out in the field of student records management in the future through presentation of student body records management model. Also, it is expected that the analysis results derived from this research will have significance in terms of school history arrangement and conservation.