• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3851-3866
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• 2015

The dynamic nature of opportunistic networks results in long delays, low rates of success for deliveries, etc. As such user experience is limited, and the further development of opportunistic networks is constrained. This paper proposes a prediction-based routing method for opportunistic networks (PB-OppNet). Firstly, using an ARIMA model, PB-OppNet describes the historical contact information between a node pair as a time series to predict the average encounter time interval of the node pair. Secondly, using an optimal stopping rule, PB-OppNet obtains a threshold for encounter time intervals as forwarding utility. Based on this threshold, a node can easily make decisions of stopping observing, or delivering messages when potential forwarding nodes enter its communication range. It can also report different encounter time intervals to the destination node. With the threshold, PB-OppNet can achieve a better compromise of forwarding utility and waiting delay, so that delivery delay is minimized. The simulation experiment result presented here shows that PB-OppNet is better than existing methods in prediction accuracy for links, delivery delays, delivery success rates, etc.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.4-146
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• 2001

Various methods have been proposed for the time series prediction. Most of the conventional methods only optimize parameters of mathematical models, but to construct an appropriate functional form of the model is more difficult in the first place. We employ the Genetic Programming (GP) to construct the functional form of prediction models. Our method is distinguished because the model parameters are optimized by using Back-Propagation (BP)-like method and the prediction model includes discontinuous functions, such as if and max, as node functions for describing complicated phenomena. The above-mentioned functions are non-differentiable, but the BP method requires derivative. To solve this problem, we develop ...

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.244-247
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• 1986

A revised two-level costate prediction algorithm is developed for the optimization of nonlinear nuclear power plant. The algorithm is proved to converge very well, and appears to require substantially small computation time and storage than previous nonlinear optimization algorithm. To cope with unknown external disturbances, we construct a closed loop control system. In order to get a smaller sampling time, this paper proposes the two-level Kalman filter.

• Journal of the Korean Solar Energy Society
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• v.32 no.2
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• pp.74-86
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• 2012

For the improvement of environmental comfort in the buildings with the blind control, the objective of this study is to prevent the direct glare caused by the daylight inlet. During the process of solar profile prediction, time are significant factors that may cause error and glare during the blind control. This research proposes and evaluates the correction and control method to minimize prediction error. For the local areas with different longitude and local standard meridian, error occurred in the process of the time conversion from local standard time to apparent solar time. In order to correct error in time conversion, apparent solar time should be recalculated after adjusting the day of year and the equation of time. To solve the problems by the potential time errors, control method is suggested to divide the control sections using the calibrated fitting-curve and this method is verified through simulations. The proposed correction and control method, which considered potential time errors by loop lop leap years, could solve the problems about direct glare caused by daylight inlet on the work-plane according to the prediction errors of solar profile. And also these methods could maximize daylight inlet and solar heat gain, because the blocked area on windows could be minimized.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.1-10
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• 2007

The capabilities ofBusiness Process Management Systems (BPMS's) are continuously extended to increase theeffectiveness of the management and enactment of business processes. This paper identifies the challenge ofcase prediction, which for a specific case under the control of a BPMS deals with the estimation of the remaining time until it is completed. An accurate case prediction facility is a valuable tool for the operationalcontrol of business processes, as it enables the pre-active monitoring of time violations. Little research has beencarried out in this area and few commercial tools support case prediction. This paper lists the requirements onsuch a facility and sketches sonae directions to reach a solution. To illustrate the depth of the problem, a smallaspect of the problem is treated in more detail. It involves the complex relations between tasks and resources inbusiness processes, which makes an exact analytical approach mfeasible.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.258-263
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• 2001

In this paper, the real-time prediction of high temperature creep life was carried out for the friction welded joints of dissimilar heat resistintg steels (CulCr0.5Zr-STS316L). Various life prediction methods such as LMP (Larson-Miller Parameter) and ISM (initial strain method) were applied. The creep behaviors of those steels and the welds under static load were examined by ISM combined with LMP at 300, 400 and 50$0^{\circ}C$, and the relationship between these two methods was investigated. A real-time creep life (tsub/r/, hr) prediction equation by initial strain ($\varepsilon_0$, %) under any creep stress ($\sigma$, MP$\alpha$) at any high temperature (T, K) was developed

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.143-150
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• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.44-52
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• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.