• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.44-45
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• 2020

Korea has the fourth highest CO₂ emission among OECD countries in 2018, As of 2019, total greenhouse gas emissions per capita increased by about 98.2% in comparison to 1990. Korea has promised a 37% reduction in greenhouse gas emissions in 2030 from the projected Paris Climate Change Accord. Currently, many countries use the emissions trading system(ETS) for international carbon management. In 2015, ETS has been implemented in Korea, and the importance of calculating CO₂ emissions from construction machinery has increased. So, we require an accurate calculation of the environmental charges through the allocated CERs. Using the CER price and related search keywords, this paper derive about prediction models of CER price and compare and focus on more accurate prediction about CER price. By this method, the budget needed to establish the initial construction process plan can be calculated based on more accurate predicted CER price.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1327-1336
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• 2017

In this study, the problems in the short term stock market forecasting are analyzed and the feasibility of the ARIMA method and the backpropagation neural network is discussed. Neural network and genetic algorithm in short term stock forecasting is also examined. Since the backpropagation algorithm often falls into the local minima trap, we optimized the backpropagation neural network and established a genetic algorithm based on backpropagation neural network for forecasting model in order to achieve high forecasting accuracy. The experiments adopted the korea composite stock price index series to make prediction and provided corresponding error analysis. The results show that the genetic algorithm based on backpropagation neural network model proposed in this study has a significant improvement in stock price index series forecasting accuracy.

• Journal of The Korean Society of Integrative Medicine
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• v.6 no.4
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• pp.149-169
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• 2018

Purpose : This study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the South Korea into 2030. Methods : In-and-out movement model was used to project the supply of physical therapists. The demand was projected according to the demand-based method which consists of four-stages such as estimation of the utilization rate of the base year, forecasting of health care utilization of the target years, forecasting of the requirements of clinical physical therapists and non-clinical physical therapists based on the projected physical therapists. Results : Based on the current productivity standards, there will be oversupply of 39,007 to 40,875 physical therapists under the demand scenario of average rate in 2030, undersupply of 44,663 to 49,885 under the demand scenario of logistic model, oversupply of 16,378 to 19,100 under the demand scenario of logarithm, and oversupply of 18,185 to 20,839 under the demand scenario of auto-regressive moving average (ARIMA) model in 2030. Conclusion : The result of this projection suggests that the direction and degree of supply of and demand for physical therapists varied depending on physical therapists productivity and utilization growth scenarios. However, the need for introduction of a professional physical therapist system and the need to provide long-term care rehabilitation services are actively being discussed in entering the aging society. If community rehabilitation programs for rehabilitation of disabled people and the elderly are activated, the demand of physical therapists will increase, especially for elderly people. Therefore, healthcare policy should focus on establishing rehabilitation service infrastructure suitable for an aging society, providing high-quality physical therapy services, and effective utilization of physical therapists.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.365-373
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• 2016

Much of the data used in the analysis of environmental ecological data is being obtained over time. If the number of time points is small, the data will not be given enough information, so repeated measurements or multiple survey points data should be used to perform a comprehensive analysis. The method used for that case is longitudinal data analysis or mixed model analysis. However, if the amount of information is sufficient due to the large number of time points, repetitive data are not needed and these data are analyzed using time series analysis technique. In particular, with a large number of data points in the current situation, when we want to predict how each variable affects each other, or what trends will be expected in the future, we should analyze the data using time series analysis techniques. In this study, we introduce univariate time series analysis, intervention time series model, transfer function model, and multivariate time series model and review research papers studied in Korea. We also introduce an error correction model, which can be used to analyze environmental ecological data.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• The Journal of Fisheries Business Administration
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• v.38 no.2
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• pp.41-62
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• 2007

The Fish aquacultural Industry has been developed rapidly since 1990s in Korea. The total production of fish aquaculture was 5,000ton in the beginning of 1990s, but it was an excess of 80,000ton in 2005. In the beginning of 1990s, the percentage of flatfish yield was 80% of the fish aquaculture in the respect of production. And it has been maintained 50% level in 2005. In this point of view, flatfish aquaculture played the role of leader in the development of fish aquaculture. Rapid increasing of production was not only caused to decreasing in price basically, but also it threatened the management of producer into insecure price for aquacultural flatfish. Therefore, it needs the policy for stabilizing in price, but it is difficult to choose the method because the basic study was not accomplished plentifully. This study analyzed about price structure of aquacultural flatfish. A period of analysis was from January 2000 to December 2005, and a data was used monthly data for price. The principal result of this study is substantially as follows. 1) The price of producing and consuming district is closely connected. 2) A gap between producing district price and consuming district price is decreasing recently, It seems to be correlated with outlook business of aquacultural flatfish. 3) Trend line of the price was declining until 2002, but it turned up after that. The other side, circulated fluctuation was being showed typically. 4) The circle of circulated fluctuation was growing longer, so it seems that the producer was doing a sensible productive activity to cope with changing price. As a result, government's policy needs to be turned into price policy from policy of increased production for aquacultural flatfish. It seems that the best policy is price stabilization polices. And also, government needs to invest in outlook business for aquaculture constantly.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.81-98
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• 2018

Port logistics is essential for Korea's economy which heavily rely on international trade. Vast amounts of capital and time are consumed for the operation and development of ports to improve their competitiveness. Therefore, it is important to forecast cargo volume in order to establish the optimum level of construction and development plan. Itemized forecasting is necessary for appropriate port planning, since disaggregate approach is able to provides more realistic solution than aggregate forecasting. We introduce a new time series model which is Two-way Seasonality Multiplied Regressive Model (TSMR) to forecast oil cargo volume, which accounts for a large portion of total cargo volume in Korea. The TSMR model is designed to take into account the characteristics of oil cargo volume which exhibits trends with short and long-term seasonality. To verify the TSMR model, existing forecasting models are also used for a comparison reason. The results shows that the TSMR excels the existing models in terms of forecasting accuracy whereas the TSMR displays weakness in short-term forecasting. In addition, it was shown that the TSMR can be applied to other cargoes that have trends with short- and long-term seasonality through testing applicability of the TSMR.