• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.782-782
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• 2012

지금까지 우리나라에서는 도시하수, 공장폐수 등의 점오염원에 국한하여 중점적으로 수질관리를 실행하여 부분적으로 효과를 얻을 수 있었으나, 하천과 호소의 수질은 크게 향상되지 않고 있다. 이는 급속한 도시화와 산업발달로 토지개발이 가속화되고 대지, 도로, 주차장 등 불투수층 면적이 늘어남에 따라 비점오염원에 의한 하천, 호소의 수질영향도가 커지고 있기 때문이다. 인구증가로 인해 물 사용량 뿐만 아니라 이에 따라 배출되는 오염원의 종류 및 오염부하량 역시 함께 증가하고 있다. 장래의 수질관리 성공여부는 비점오염원의 효율적인 관리여부가 큰 변수로 작용할 것으로 본다. 따라서 공공수역의 수질관리를 위해서는 토지이용과 지역특성을 고려한 비점오염원 부하량의 합리적인 조사, 오염 부하량 절감을 위한 관리기술의 개발, 비점오염원 관리정책의 개발 및 수질모형을 이용한 정확한 수질예측 등이 필요하다. 따라서 본 연구에서는 공간정보를 바탕으로 한 낙동강 유역에서의 비점오염원 정량화 분석을 수행하고자 한다. 우선 대상유역으로 낙본 G유역을 선정하여 이에 대한 조사를 통해 점오염원의 실측자료를 구축하고 이를 HSPF의 입력하여 모의를 수행하여 대상유역에 대한 실측치를 이용해 모형의 보정과 검증을 수행한다. 이러한 과정을 통해 도출된 결과는 대상유역의 총 오염량을 의미한다. 따라서 위의 과정에서 도출된 매개변수를 이용하고, 점오염원을 제거한 뒤 모의를 재수행하여 나온 결과가 대상유역의 비점오염원의 양이라 판단하였다. 모의 결과 대상유역인 낙본 G유역에서 약 39% 정도의 비점오염원 비율을 보였다. 그러나 수질 및 유량 관측치를 지금까지는 국립환경과학원 낙동강물환경연구소 유량측정데이타를 사용하고 있는데 이 자료는 8일 이상 간헐적으로 측정이 수행되고 있다. 따라서 검 보정 대상이 되는 실측치의 자료의 부족과 부정확한 유역이 있음이 한계점으로 작용한다. 그러므로 추후의 신경망 모형이나 기타 실측치 보간에 있어서의 신뢰도를 높이는 기법 개발이나 측정제도의 보편적인 기술의 증대도 앞으로의 모델링에 있어서 중요할 것으로 판단된다. 또한 유역수질모형의 모델링 과정에서 좀 더 신뢰도 높은 측정자료와 그 측정자료를 활용하여 PEST 보정기법을 적용한다면 더욱 정확한 예측이 이루어질 수 있을 것이며, 본 연구에서의 평가방법을 바탕으로 유역수질모델링이 이루어진다면 보다 더 정확성 높은 비점오염원 정량화와 수질 예측이 수행될 수 있을 것이며 더 나아가 오염총량제의 수행에 효과적으로 적용될 것으로 판단된다.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.133-158
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• 2018

The role and ratio of national vessels in the global container shipping market have reduced significantly due to the bankruptcy of Hanjin Shipping in early 2017. All import-export companies, as well as container ports in Korea, are facing a crisis. The Trump's tariff and trade battles have had a negative impact on the increase in the North American cargo. However, Chinese and Japanese container shipping companies, which merged with domestic container shipping companies, and mega carriers such as Maersk and CMA CGM have benefited from the decline in shipping supplies due to the collapse of Hanjin Shipping, the world's 10th largest container carrier in Korea. The import/export freight trade in Korea is witnessing the increasing stronghold of foreign carriers. This scenario is expected to weaken Korea's negotiation powers with overseas shipping companies in domestic ports, such as Busan and Kwangyang, thereby making it more challenging to attract shipping carriers. This study compares the global container-shipping network in 2007 and 2017 by combining the network topology of the social network analysis and the economics of the liner shipping connectivity index (LSCI) and the container port connectivity index (CPCI) analysis. The findings of this study are that the role of the ports across the world can be identified, and CPCI has a high correlation with the centrality index and freight volume data. These findings can contribute toward the utilization of the meaning of the necessary centrality index without an additional centrality analysis. This study can be applied not only to the call strategy of container carriers but also to the alliance and development strategy of Korean ports.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.17-27
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• 2021

The aim of this research was to identify the issue of the work permit system and consciousness of the people on the system, and to suggest some ideas on the government policies on it. To achieve the aim of research, this research used text mining based on social data. This research collected 1,453,272 texts from 6,217 units of online documents which contained 'work permit system' from January to December, 2020 using Textom, and did text-mining and social network analysis. This research extracted 100 key words frequently mentioned from the analyses of data top-level key word frequency, and degree centrality analysis, and constituted job problem, importance of policy process, competitiveness in the respect of industries, and improvement of living conditions of foreign workers as major key words. In addition, through semantic network analysis, this research figured out major awareness like 'employment policy', and various kinds of ambient awareness like 'international cooperation', 'workers' human rights', 'law', 'recruitment of foreigners', 'corporate competitiveness', 'immigrant culture' and 'foreign workforce management'. Finally, this research suggested some ideas worth considering in establishing government policies on the work permit system and doing related researches.

• Journal of Science Education
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• v.45 no.2
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• pp.156-171
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• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.253-263
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• 2021

Concrete structures are damaged by aging and external environmental factors. This type of damage is to appear in the form of cracks, to proceed in the form of spalling. Such concrete damage can act as the main cause of reducing the original design bearing capacity of the structure, and negatively affect the stability of the structure. If such damage continues, it may lead to a safety accident in the future, thus proper repair and reinforcement are required. To this end, an accurate and objective condition inspection of the structure must be performed, and for this inspection, a sensor technology capable of detecting damage area is required. For this reason, we propose a deep learning-based image processing algorithm that can detect spalling. To develop this, 298 spalling images were obtained, of which 253 images were used for training, and the remaining 45 images were used for testing. In addition, an improved loss function and data augmentation technique were applied to improve the detection performance. As a result, the detection performance of concrete spalling showed a mean intersection over union of 80.19%. In conclusion, we developed an algorithm to detect concrete spalling through a deep learning-based image processing technique, with an improved loss function and data augmentation technique. This technology is expected to be utilized for accurate inspection and diagnosis of structures in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.63-71
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• 2019

The two-fluid model proposed by Herman and Prigogine is useful for analyzing macroscopic traffic flow in a network. The two-fluid model is used for analyzing a network through the relationship between the ratio of stopped vehicles and the average moving speed of the network, and the two-fluid model has also been applied in the urban transportation network where many signalized or unsignalized intersections existed. In general, the average travel speed and moving speed of a network decrease, and the ratio of stopped vehicles and low speed vehicles in network increase as the traffic demand increases. This study proposed the two-fluid model considering congested and uncongested traffic situations. The critical velocity and the weight factor for congested situation are calibrated by minimizing the root mean square error (RMSE). The critical speed of the Seoul network was about 34 kph, and the weight factor of the congestion on the network was about 0.61. In the proposed model, $R^2$ increased from 0.78 to 0.99 when compared to the existing model, suggesting that the proposed model can be applied in evaluating network performances or traffic signal operations.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.42-48
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• 2019

The significance of PNT information in the fourth industrial revolution is viewed differently in relation to the past. Autonomous vehicles, autonomous vessels, smart grids, and national infrastructure require sustainable and reliable services in addition to their high precision service. Satellite navigation system, which is the most representative system for providing PNT information, receive signals from satellites outside the earth so signal reception power is low and signal structures for civilian use are open to the public. Therefore, it is vulnerable to intentional and unintentional interference or hacking. Satellite navigation systems, which can easily acquire high performance of PNT information at low cost, require alternatives due to its vulnerability to the hacking. This paper proposed R-Mode (Ranging Mode) technology that utilizes currently operated navigation and communication infrastructure in terms of Signals of OPportunity (SoOP). For this, the Nationwide Differential Global Navigation Satellite System (NDGNSS), which currently gives a service of Medium Frequency (MF) navigation signal broadcasting, was used to validate the feasibility of a backup infrastructure in domestic maritime areas through simulation analysis.