• 대한임베디드공학회논문지
• /
• 제5권4호
• /
• pp.225-233
• /
• 2010

In this paper, we try to improve the performance of the demand paging loader suggested to use the demand paging way that is not based on operating system. The demand paging switching strategy used in the existing operating system can know the recently used pages by running multi-processing. Then, based on it, some page switching strategies have been made for the recently used pages or the frequently demanded pages. However, the strategies based on operating system cannot be applied in single processing that is not based on operating system because any context switching never occur on the single processing. So, this paper is trying to suggest the demand paging switching strategies that can be applied in paging loader running in single process. In the Return-Prediction-Algorithm, we saw the improved performance in the program that the function call occurred frequently in a long distance. And then, in the Most-Frequently-Used-Page-Remain-Algorithm, we saw the improved performance in the program that the references frequently occurred for the particular pages. Likewise, it had an enormous effect on keeping the memory reduction performance by the demand paging and reducing the running time delay at the same time.

• 한국수소및신에너지학회논문집
• /
• 제33권5호
• /
• pp.591-597
• /
• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• 문화기술의 융합
• /
• 제9권3호
• /
• pp.763-768
• /
• 2023

본 논문에서는 의약품 유통량 예측을 위해 기존의 통계 방식(ARIMA)과 머신러닝 방식(Informer)을 개발하고 비교하였다. 일별 데이터의 예측에서는 머신러닝 기반의 모델이 유리하며, 월별 예측에서는 ARIMA를 활용하고 데이터가 증가하면서 Informer로 전환하는 것이 효과적임을 발견하였다. 예측 에러율(RMSE)은 기존 방식 대비 26.6% 낮아졌으며, 예측 정확도도 13% 개선되어 86.2%의 결과를 보였다. 본 논문을 통해 통계적 방법과 머신러닝 방법을 앙상블하여 최상의 결과를 얻을 수 있다는 장점을 발견하였다. 또한 머신러닝 기반의 AI 모델은 불규칙한 상황에서도 딥러닝 연산을 통해 최선의 결과를 도출할 수 있으며, 상용화 이후에는 데이터양이 증가함에 따라 성능이 향상될 것으로 기대된다.

• Earthquakes and Structures
• /
• 제7권4호
• /
• pp.485-510
• /
• 2014

In performance-based seismic design procedures Peak Ground Acceleration (PGA) and pseudo-Spectral acceleration ($S_a$) are commonly used to predict the response of structures to earthquake. Recently, research has been carried out to evaluate the predictive capability of these standard Intensity Measures (IMs) with respect to different types of structures and Engineering Demand Parameter (EDP) commonly used to measure damage. Efforts have been also spent to propose alternative IMs that are able to improve the results of the response predictions. However, most of these IMs are not usually employed in probabilistic seismic demand analyses because of the lack of reliable Ground Motion Prediction Equations (GMPEs). In order to define seismic hazard and thus to calculate demand hazard curves it is essential, in fact, to establish a GMPE for the earthquake intensity. In the light of this need, new GMPEs are proposed here for the elastic input energy spectra, energy-based intensity measures that have been shown to be good predictors of both structural and non-structural damage for many types of structures. The proposed GMPEs are developed using mixed-effects models by empirical regressions on a large number of strong-motions selected from the NGA database. Parametric analyses are carried out to show the effect of some properties variation, such as fault mechanism, type of soil, earthquake magnitude and distance, on the considered IMs. Results of comparisons between the proposed GMPEs and other from the literature are finally shown.

• Membrane and Water Treatment
• /
• 제10권1호
• /
• pp.1-11
• /
• 2019

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the $21^{st}$ century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

• Earthquakes and Structures
• /
• 제20권1호
• /
• pp.109-122
• /
• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• 한국항공우주학회지
• /
• 제50권3호
• /
• pp.215-222
• /
• 2022

항공교통흐름관리의 목적은 공항 및 공역의 수용량 안에서 항공교통 수요를 만족시키는 것이다. 그러므로 수용량을 정확하게 예측하는 것은 항공교통흐름관리의 성능에 많은 영향을 준다. 본 논문은 특정 공항의 예상 출·도착 수요, 시각, 기상 및 실제 처리한 항공기 대수 등 과거의 항공기운항 데이터를 기계학습의 한 방법론인 부스팅 앙상블 알고리즘으로 학습하여 시간당 출·도착하는 항공기의 수를 예측하는 회귀모형을 개발하였다. 기계학습을 통해 도출된 모델은 실제 인천국제공항의 출·도착 항공편 데이터를 이용해 검증하였으며, 결정계수가 0.95 이상으로 나타났다. 이 모델을 이용하여 접근관제구역의 수용량을 간접적으로 예측할 수 있었다.

• 인터넷정보학회논문지
• /
• 제24권2호
• /
• pp.57-66
• /
• 2023

현재, 에너지 효율 향상으로 소비감축을 시행하는 새로운 에너지 시스템이 대두되고 있다. 이에 스마트그리드가 확산되면서 계시별 요금제가 확대되고 있다. 계시별 요금제는 계절별 / 시간별로 요금을 다르게 적용해 사용량에 따라 요금을 내는 요금제이다. 본 연구에서는 에너지 전력 사용량 데이터를 예측하기 위해, 온도/요일/시간/계절 등 외부 요인을 고려하고 시계열 예측 모델인 LSTM을 활용한다. 이러한 에너지 사용량 예측 모델을 기반으로 기기별 사용패턴을 분석하여 전력 에너지를 최대부하시간대에서 경부하시간대로 수요이전 함으로써 에너지 사용요금을 절감한다. 기기별 사용패턴을 분석하기 위해서는 시간대별로 기기의 사용량 패턴을 학습 및 분류하는 clustering 기법을 사용한다. 정리하자면, 본 연구에서는 사용자의 전력 데이터 사용량을 기반으로 사용량과 사용 요금을 예측 및 기기별 사용패턴을 분석하고 분석 기반의 맞춤형 수요이전 서비스를 제공함으로써 사용자에게 요금 절감 효과를 가져다 준다.

• 정보통신설비학회논문지
• /
• 제8권4호
• /
• pp.157-162
• /
• 2009

This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

• Journal of information and communication convergence engineering
• /
• 제9권4호
• /
• pp.369-374
• /
• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.