• Current Industrial and Technological Trends in Aerospace
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• v.8 no.1
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• pp.12-22
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• 2010

The great recession which caused by financial crisis made steep rise of oil price and the serious problems of the aircraft industry. High oil price increases operating cost and the recession decreases air traffic. After a period of high book order and delivery from global economic recovery, the aircraft order fell down suddenly. Also the Aircraft price and lease rate deceased and the MRO market is reduced, too. But, the air cargo and passenger increase again since late of 2009. So, it is difficult to predict the market movement, most of the forecasters agreed that the air traffic and aircraft demand will grow gradually in long term with the growth of emerging markets like China, India and Africa. And more efficient, safe and clean aircraft is needed and will need in the market.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.2
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• pp.1-10
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• 1982

Presently, most of rail stations are situated on the surface of dowl1town and thus result in heavy traffic congestion and inefficient use of land. This paper analyzes the impact of alternative locations of station On urban traffic patterns by simulating transportation systems, of Busan city. Since location of station has long-term effects on land use and transportation, 20 years forecast of land use change, trip generation, trip distribution, modal split and network assignment was performed for each alternative, and aggregate Impacts On passenger-km and passenger-hour were computed. The result indicated that Bujeon is the most desirable location of station in terms of traffic movement, compared to the alternative locations of Sasang and existing station. Relocation of rail station, however, should be decided with broader analysis including other aspects, such as urban development, environment, construction and operating costs, etc.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.2
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• pp.139-149
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• 2014

A new technique, namely the combination of satellite and trajectory analysis (CSTA), for exploring the spatio-temporal distribution information of volcanic ash plume (VAP) from volcanic eruption. CSTA uses the satellite derived ash property data and a matching forward-trajectories, which can generate airmass history pattern for specific VAP. In detail, VAP properties such as ash mask, aerosol optical thickness at 11 ${\mu}m$ ($AOT_{11}$), ash layer height, and effective radius from the Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite were retrieved, and used to estimate the possibility of the ash forecasting in local atmosphere near volcano. The use of CSTA for Iceland's Eyjafjallaj$\ddot{o}$kull volcano erupted in May 2010 reveals remarkable spatial coherence for some VAP source-transport pattern. The CSTA forecasted points of VAP are consistent with the area of MODIS retrieved VAP. The success rate of the 24 hour VAP forecast result was about 77.8% in this study. Finally, the use of CSTA could provide promising results for VAP monitoring and forecasting by satellite observation data and verification with long term measurement dataset.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.421-423
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• 2001

Restructuring of electricity industry is going on for the purpose of introducing competition and after separation of generation and retail business and introduction of competition, substantial change is expected in overall electric power system. In other words, DSM projects are divided with public projects and private projects. Particularly for public project, it is essential to evaluate the DSM volumes by program. This paper tries to derive the ways for achieving the necessary DSM goal in the electricity industry in Korea. First of all, by analyzing the load in Korea, we forecast the standard demand and estimate the technological potentials of each program in considering DSM technological indicators. Moreover, by using economic analysis by program, we estimate economic potentials and finally, we estimate the potentials by program in considering the DSM policy. We estimate the potentials by using random method because application methodology and procedures by program are not established until now, which leads to not obtaining transparency for implementation effect by program. Therefore, this paper estimates the future potentials of DSM projects by using the logical and systematic analytic method and establishing database for DSM basic indicator. The DSM goals estimated by this method will be reflected to mid/long term nation-wide resource planning, which will mitigate anticipated power supply shortage and be applied to derive desirable energy demand/supply structure.

• Journal of the Korean Institute of Navigation
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• v.8 no.1
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• pp.1-16
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• 1984

The economical property of a shipping enterprise, as well as other transportation industries, is determined by the difference between the freight earned and expense paid. This study can be regarded as a division of optimizing ship allocation to routes under the integrated port transport system. Fleet planning and scheduling require complicated allocations of cargoes to ships and ships to routes in order to optimize the given criterion function for a given forecast period. This paper deals with the optimum ship allocation problem minimizing the operating cost of ships in a shipping company. Optimum fleet operating for a shipping enterprise is very important, since the marine transportation is a form of large quantity transport requiring long-term period, and there is a strong possibility to bring about large amount of loss in operation resulting from a faulty ship allocation. Where there are more than one loading and discharging ports, and a variety of ship's ability in speed, capacity, operating cost etc., and when the amount of commodities to be transported between the ports has been determined, then the ship's schedule minimizing the operating cost while satisfying the transport demand within the predetermined period will be made up. First of all a formula of ship allocation problems will be established and then will be constructed to solve an example by the Integer Programming application after consideration of the ship's ability, supply and demand of commodity, amount of commodity to be transported, operating costs of each ship etc. This study will give good information on deciding intention for a ship oprator or owner to meet the computerization current with shiping management.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.97-97
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• 2016

가뭄은 홍수와 더불어 매우 심각한 자연재해이며, 그 특성상 광역적이고 장기간 발생함에 따라 구체적인 발생시점, 규모, 범위 등을 규명하기가 어렵다. 다만, 적시에 경보해야 하는 홍수와 달리 진행속도가 느리고 시간적으로 대처할 여유가 있어 진행중 일지라도 초기에 감지한다면 그 피해를 최소화할 수 있다. 미국 등 수문기상 선진국에서는 수문기상 장기예보자료를 활용한 가뭄전망정보 생산 및 제공하고 있으며, 활용성을 검증한바 있다. 국내의 경우 기상청에서는 대기-해양-해빙 모델을 접합한 GloSea5 (Global Seasonal forecasting system version 5) 모델을 도입하였으며, 가뭄예보를 목적으로 장기예보자료 기반의 가뭄전망정보 생산체계를 구축한 바 있다(기상청, 2012; 손경환 등, 2015). 본 연구에서는 장기예보자료 기반의 수문기상 전망정보를 이용하여 2014-15년 가뭄사례에 대한 가뭄감시 및 전망정보를 생산 및 평가하였다. 수문기상전망 정보는 기상청 현업예보 모델인 GloSea5와 지면모델을 이용하여 생산하였으며, 관측자료와 수문전망정보 기반의 가뭄지수를 산정하였다. 매스컴 및 언론 보도 자료부터 2014-15년 가뭄에 대한 행정구역별 피해사례를 수집하였으며, 이를 기반으로 시계열, 지역별 및 통계적(CC, RMSE) 분석을 이용하여 선행시간별 정확도를 평가하였다. 1개월 및 2개월 전망정보의 정확도가 높음을 확인하였으며, 가뭄심도가 심각한 시기의 가뭄상황을 적절히 재현하는 것으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Environmental Engineering Research
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• v.23 no.4
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• pp.406-419
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• 2018

In planning public service systems such as waterworks, the design population is very important factor. Owing to the limitations of the indirect method, two new models, which take into consideration urban characteristics, were developed to accurately predict external migration rate (EMR), which is an essential component in estimating reliably the design population. The root mean square error (RMSE) between the model values and observed values were 10.12 and 15.58 for the metropolitan cities and counties respectively and were lower compared to RMSE values of 27.31 and 28.79 obtained by the indirect method. Thus, the developed models provide a more accurate estimate of EMR than the indirect method. In addition, the major influencing factors for external migration in counties were development type, ageing index, number of businesses. On the other hand, the major influencing migration factors for cities were project scale, distance to city center, manufacturing size, population growth rate and residential environment. Future medium and long-term studies would be done to identify emerging trends to appropriately inform policy making.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.