• Title/Summary/Keyword: forecast performance

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A Study on the Operational Ceiling Forecasting and its Improvement Using a Mesoscale Numerical Prediction Model over the Korean Peninsula (중규모 수치예측 모델을 이용한 한반도 시일링 예보 및 현업 운영 개선에 관한 연구)

  • Lee, Seung-Jae;Kim, Young-Chul
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.19 no.1
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    • pp.24-28
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    • 2011
  • This paper reviews a ceiling prediction method based on a mesoscale meteorological modeling system in South Korea. The study was motivated by the tendency of higher model ceiling height than the observed in daily operational forecasts. The goal of the paper is to report an effort to improve the operational ceiling prediction skill by conducting numerical experiments controlling a model parameter. In a case experiment, increasing constant values used in the relationship between extinction coefficients and concentration showed better performance, indicating a short-term strategy for operational local ceiling forecast improvement.

Batch Sizing Heuristic for Batch Processing Workstations in Semiconductor Manufacturing (반도체 생산 배취공정에서의 배취 크기의 결정)

  • Chun, Kil-Woong;Hong, Yu-Shin
    • Journal of Korean Institute of Industrial Engineers
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    • v.22 no.2
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    • pp.231-245
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    • 1996
  • Semiconductor manufacturing line includes several batch processes which are to be controlled effectively to enhance the productivity of the line. The key problem in batch processes is a dynamic batch sizing problem which determines number of lots processed simultaneously in a single botch. The batch sizing problem in semiconductor manufacturing has to consider delay of lots, setup cost of the process, machine utilization and so on. However, an optimal solution cannot be attainable due to dynamic arrival pattern of lots, and difficulties in forecasting future arrival times of lots of the process. This paper proposes an efficient batch sizing heuristic, which considers delay cost, setup cost, and effect of the forecast errors in determining the botch size dynamically. Extensive numerical experiments through simulation are carried out to investigate the effectiveness of the proposed heuristic in four key performance criteria: average delay, variance of delay, overage lot size and total cost. The results show that the proposed heuristic works effectively and efficiently.

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Forecasting Long-Term Steamflow from a Small Waterhed Using Artificial Neural Network (인공신경망 이론을 이용한 소유역에서의 장기 유출 해석)

  • 강문성;박승우
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.43 no.2
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    • pp.69-77
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    • 2001
  • An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

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Application of Neural Networks For Estimating Evapotranspiration

  • Lee, Nam-Ho
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1993.10a
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    • pp.1273-1281
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    • 1993
  • Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

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Real-time Flood Forecasting Model for the Medium and Small Watershed Using Recursive Parameter Optimization (매개변수 추적에 의한 중.소하천의 실시간 홍수예측모형)

  • Moon, Jong-Pil;Kim, Tai-Cheol
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • 2001.10a
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    • pp.295-299
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    • 2001
  • To protect the flooding damages in Medium and Small watershed, it needs to set up flood warning system and develope Flood forecasting Model in real-time basis for medium and small watershed. In this study, it was able to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance by using simplex method recursively for the determination of the best parameters of RETFLO model. The result of RETFLO performance applied to several storm of Yugu river during 3 past years was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

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한국과 미국간 항공기 탑승객 수 예측을 위한 뉴럴네트웍의 응용

  • 남경두
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1995.09a
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    • pp.334-343
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    • 1995
  • In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.

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A Choice-Based Multi-Product Diffusion Model Incorporating Replacement Demand (대체수요를 고려한 선택관점의 다제품 확산모형)

  • Kim, Jeong-Il;Jeon, Deok-Bin
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2006.11a
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    • pp.161-164
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    • 2006
  • The sales of consumer durables are composed of first time purchases and replacement purchases. Since the sales for most mature durable products are dominated by replacement sales, it is necessary to develop a model incorporating replacement component of sales in order to forecast total sales accurately. Several single product diffusion models incorporating replacement demand have been developed, but research addressing the multi-product diffusion models has not considered replacement sales. In this paper, we propose a model based on consumer choice behavior that simultaneously captures the diffusion and the replacement process for multi-product relationships. The proposed model enables the division of replacement sales into repurchase by previous users and transition purchase by users of different products. As a result, the model allows the partitioning of the total sales according to the customer groups (first-time buyers, repurchase buyers, and transition buyers), which allows companies to develop their production and marketing plans based on their customer mix. We apply the proposed model to the Korean automobile market, and compare the fitting and forecasting performance with other Bass-type multi-product models.

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Route Selection Protocol based on Energy Drain Rates in Mobile Ad Hoc Networks (무선 Ad Hoc 통신망에서 에너지 소모율(Energy Drain Rate)에 기반한 경로선택 프로토콜)

  • Kim, Dong-Kyun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.7A
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    • pp.451-466
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    • 2003
  • Untethered nodes in mobile ad-hoc networks strongly depend on the efficient use of their batteries. In this paper, we propose a new metric, the drain rate, to forecast the lifetime of nodes according to current traffic conditions. This metric is combined with the value of the remaining battery capacity to determine which nodes can be part of an active route. We describe new route selection mechanisms for MANET routing protocols, which we call the Minimum Drain Rate (MDR) and the Conditional Minimum Drain Rate (CMDR). MDR extends nodal battery life and the duration of paths, while CMDR also minimizes the total transmission power consumed per packet. Using the ns-2 simulator and the dynamic source routing (DSR) protocol, we compare MDR and CMDR against prior proposals for power-aware routing and show that using the drain rate for power-aware route selection offers superior performance results.

Nonlinearities and Forecasting in the Economic Time Series

  • Lee, Woo-Rhee
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.931-954
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    • 2003
  • It is widely recognized that economic time series involved not only the linearities but also the non-linearities. In this paper, when the economic time series data have the nonlinear characteristics we propose the forecasts method using combinations of both forecasts from linear and nonlinear models. In empirical study, we compare the forecasting performance of 4 exchange rates models(AR, GARCH, AR+GARCH, Bilinear model) and combination of these forecasts for dairly Won/Dollar exchange rates returns. The combination method is selected by the estimated individual forecast errors using Monte Carlo simulations. And this study shows that the combined forecasts using unrestricted least squares method is performed substantially better than any other combined forecasts or individual forecasts.

Implementation of Active Location Detecting Systemby Using Zigbee Module Technique (Zigbee기반 능동형 위치 검출 시스템 알고리즘 구현)

  • Jo, Hyun-Tae;Kim, Dong-Hyun;Kwon, Young-Bin;Choi, Young-Wan;Lee, Jung-Woo;Park, Ho-Hyun;Park, Jae-Hwa
    • 한국정보통신설비학회:학술대회논문집
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    • 2009.08a
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    • pp.231-234
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    • 2009
  • In this paper the situation requiring emergency rescue team from the endangered person, using the structure of the signal with a transmitter that provides the service. Given real-time map information based on a directional antenna to the transmitter of the received value, and moving the location of the forecast to move the tracker to the location of the transponder, the algorithm offers. Location tracking algorithm implemented in the simulator to actually do the verification report which will show whether the performance of the show.

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