• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.40-48
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• 2023

In this paper, a sensor-based monitoring system was established to analyze the long-term behavioral characteristics of the caisson quay wall, a representative structural type in port facilities. Data was collected over a period of approximately 10 months. Based on existing literature, anomalous behaviors of port facilities were classified, and a measurement system was selected to detect them. Monitoring systems were installed on-site to periodically collect data. The collected data was transmitted and stored on a server through LTE network. Considering the site conditions, inclinometers for measuring slope and crack meters for measuring spacing and settlement were installed. They were attached to two caissons for comparison between different caissons. The correlation among measured data, temperature, and tidal level was examined. The temperature dominated the spacing and settlement data. When the temperature changed by approximately 50 degrees, the spacing changed by 10 mm, the settlement by 2 mm, and the slope by 0.1 degrees. On the other hand, there was no clear relationship with tidal level, indicating a need for more in-depth analysis in the future. Based on the characteristics of these collected database, it will be possible to develop algorithms for detecting abnormal states in gravity-type quay walls. The acquisition and analysis of long-term data enable to evaluate the safety and usability of structures in the event of disasters and emergencies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Korean Journal of Ichthyology
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• v.35 no.4
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• pp.217-223
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• 2023

The 886-bp sequence of the mitochondrial region encoding the cytb gene was used to identify the origin of the Goryeong (GR) population of Pungitius kaibarae and to characterize genetic diversity and structure among wild populations. The GR population showed the lowest haplotype diversity (H_d=0.000), while the highest haplotype diversity was confirmed at 0.755 among the Goseoung (GS) population. Nucleotide diversity ranged was the highest diversity at 0.00291 in the GS population and the lowest diversity at 0.00000 in the GR population. The GR population was genetically closest to the Pohang (PH) population. The haplotype network confirmed that the GR population was most similar to the PH population. The GR population also clustered with the PH population with high bootstrap support (98%) in a phylogenetic tree. We thus conclude that the GR population is derived from a population similar to the PH population.

• Journal of the Economic Geographical Society of Korea
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• v.27 no.1
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• pp.38-52
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• 2024

Digital transformation industry contributes to the improvement of productivity in overall industrial production, the smart green industry for carbon neutrality and sustainable growth is growing as a future industry. The purpose of this paper is to explore the status and role of the industry in the future industry innovation ecosystem through the analysis of the growth drivers and location pattern changes of the smart green industry. The industry is on the rise in both metropolitan and non-metropolitan areas, and the growth of the industry can be seen in non-metropolitan and non-urban areas. In particular, due to the smart green industrial complex pilot project, the creation of Gwangju Jeonnam Innovation City, and the promotion of new and renewable energy policies, the emergence of core aggregation areas (HH type) in the coastal areas of Honam and Chungcheongnam-do, and the formation of isolated centers (HL type) in the Gyeongsang region, new and renewable energy production companies are being accumulated in non-metropolitan areas. Therefore, the smart green industry is expected to promote the formation of various specialized spokes in non-urban areas in the future industrial innovation ecosystem that forms a multipolar hub-spoke network structure, where policy factors are the triggers for growth.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.

• Research in Community and Public Health Nursing
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• v.7 no.2
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• pp.384-399
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• 1996

The concept of parish nursing began in the late 1960s in the United States when increasing numbers of churches employed registered nurses (RNs) to provide holistic, preventive health care to the members of their congregations. Parish nursing role was developed in 1983 by Lutheran chaplain Granger Westberg, and provides care to a variety of church congregation of various denominations. The parish nurse functions as health educator, counselor, group facilitator, client advocate, and liaison to community resources. Since these activities are complementary to the population-focused practice of community health' CNSs, parish nurses either have a strong public health background or work directly with both baccalaureate-prepared public health nurses and CNSs. In a Midwest community in U.S.A., the Healthy People 2000(1991) objectives are being addressed in health ministries through a coalition between public health nurses and parish nurses. Parish nursing is in the beginning state in Korea and up untill now, there has been no research was conducted on concrete role of korean parish nurses. The main purpose of this study was to identify, classify and analyze activities of parish nurses. The other important objective of this study was to establish an effective approach and direction for parish nursing and provide a database for korean parish nursing model through analysis and' classification of the content of the nursing record which included nursing activities. This study was a descriptive survey research. The parish nurses were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were working in churches where the demonstration project developed on parish nursing. The study was done on all nursing records which were documented by parish nurses in three churches from March, 1995 to February, 1996. Namsan, Taegu Jeei and Nedang presbyterian churches in Taegu and Keimyung nursing college incooperated together for the parish nursing demonstration project. The data analysis procedure was as follows: First, a record analysis tool was developed and second, the data was collected, coded and analyzed, the classification for nursing activities was developed through a literature review, from which the basic analysis tool was produced and cotent validity review was also done. The classification of the activities of parish nurses showed 7 activitity categories. 7 activity categories consisted of visitation nursing, health check-ups, health education, referring, attending staff meetings, attending inservices and seminar, volunteers coordinating. The percentage of activities were as follows: Visitation nursing(A: 51.6%, B: 55%, C: 42.6%) Health check-ups(A: 13.5%, B: 12.1%, C: 22.3%) Health education(A: 13.5%, B: 13.2%, C: 18.2%) Referring(A: 1.4%, B: 4.2%, C: 2.4%) Attending staff meeting(A: 18.8%, B: 13.0%, C: 12.2%) Attending inservices and seminar(A: 1.5%, B: 2.2%, C: 2.1%) Volunteers coordinating(A: 0.3%, B: 0.4%, C: 0.0%) To establish and develope parish nursing delivery network in Korea, parish nurses role, activities and boundaries of practice should be continuously monitored and refined every 2 years. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members. role, activities and boundaries of practice and arrangement of the working structure, continuing education, cooperation with community resources and structuring and organizing parish nursing delivery network. Also, It is needed to develope effective nursing recording system based on the need assessment research data of various congregation members.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.139-151
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• 2018

The National Base Map construction consists of the regular correction work of dividing the whole country into two regions and carrying out the modification Plotting by aerial photographs every two years as well as the real time updating work of correcting the major change feature within two weeks by the field survey and the As-Built Drawing. In the case of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) used for real time updating work of the National base map, the coordinate transformation error is included in the positional error when applied to the National Base Map based on the World Geodetic Reference System as the coordinate system based on the Regional Geodetic Reference System. In addition, National Base Map is registered based on the outline(eaves line) of the building in the Digital Topographic Map, and the Cadastral and Architecture are registered based on the building center line. Therefore, the Building Object management standard is inconsistent. In order to investigate the improvement method, the network RTK survey was conducted directly on a location of the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS) and the problems were analyzed by comparing with the plane plotting position reference in National Base Map. In the case of the general structure with the difference on the Building center line and the eaves line, beside the location information was different also the difference in the ratio of the building object was different between Building center line and the eave. In conclusion, it is necessary to provide the Base data of the double layer of the Building center line and the outline of the building(eaves line) in order to utilize the Building Layout Drawing of Korea Real estate Administration intelligence System(KRAS). In addition, it is necessary to study an organic map update process that can acquire the up-to-dateness and the accuracy at the same time.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.78
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• pp.93-117
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• 2018

In all countries of the world, the development of trade is an important factor for the survival of the national economy. Increased export will lead to national economic growth. Export is directly linked to employment, and the industrial structure will be developed in the direction to produce products of comparative advantages. Therefore, every country around the world is trying to promote export regardless of the size of its economy. Accordingly, this paper focused on the promotion of export of domestic companies. It proposed to cultivate trade experts to promote export of domestic companies. The following five methods were proposed to materialize the proposal. First, it is important to foster trade experts to expand and foster the one-person creative companies. In particular, it is important to develop a professional education curriculum. It is necessary to design and conduct a systematic curriculum throughout the process including follow-up after education such as teaching detailed procedures for establishing a trade business, identification of relevant regulations and related organizations, understanding of special features of each exporting country, and details of exporting procedures through specialist training for the individual industries, helping themto keep their network steady so that they can easily get help from consultants. Second, it is necessary to educate traders working in the field to make them trade experts and utilize themin on-the-job training and consulting. To do this, it is necessary to introduce systematic consultant selection process, and to introduce a systemto educate and manage them. It is because, we must select the most appropriate candidates, educate themto be lecturers and consultants, and dispatch themto the field, in order to make the best achievement in export. Nurturing trading professionals utilizing the current trading workers to activate export of domestic companies can be more efficient through cooperation of trading education agencies and related agencies in various industries. Third, it is also proposed to cultivate female trade experts by educating female trade workers whose career has been disrupted. It is to provide career disrupted women with opportunities to work after training them as trade professionals and to give manpower pool to domestic companies that are preparing for export. Fourth, it is also proposed to educate foreign students living in Korea to be trading experts and to utilize them as trading infra. They can be trading professionals who will contribute to the promotion of export. In the short term, they will be provided with opportunities for employment and start-upin the field of trade, and in the mid- to long-term, they may develop a business network between Korea and their own countries. To this end, we need to improve the visa system, expand free trade education opportunities, and support them so that they can establish small but strong enterprises. Fifth, it is proposed to proactively expand trade education to specialized high school students. Considering that most of domestic companies pursuing activation of export are small but strong companies or small and mediumsized companies, they may prefer high school graduates rather than university graduates because of financial limitations. Besides, the specialized high school students may occupy better position in the job market if they are equipped with expertise in trading. This study can be meaningful, in that it is the first research that focuses on cultivating trading experts to contribute to the export activation of domestic companies. However, it also has a limitation that it has failed to reflect the more specific field voices. It is hoped that detailed plans will be derived from the opinions of the employees of domestic companies making efforts to become an export company in the related researches in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.