• Korean Journal of Hydrosciences
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• v.2
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• pp.39-59
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• 1991

Scour at bridge pier is a complicated three-dimensional problem involving interaction of fluld force on movable aid nonuniformily distributed sand grains. Although several analytical solution approaches, experimental research and field investigations for scout at piers have been conducted, no comprehensive and universally acceptable solution is so far available. Even though many methods and equations for predicting scour at piers are available in the literature, hydraulic and/or bridge design engineers are often at a loss over which method or equation is applicable for the specific bridge sites. To provide better understanding about scour phenomena and better predicting of scour at piers, intensive research is conducted through comprehensive review of published literature. Based on the research the state-of-the-art of pier scour prediction for design application is provided as a design guide for practicing engineers in this field. Recommendations for applying aggradation and degradation, contraction scour, and local scour prediction methods or equations are suggested. It is hoped that this paper may provide good information for the prediction of scour at piers.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.778-779
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.780-781
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• 2005

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.744-749
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• 2005

This paper proposes the prediction based QoS routing algorithm, PSS(Prediction Safety-Shortest) algorithm that minimizes network state information overhead and presumes more accurate knowledge of the present state of all the links within the network. We apply time series model to the available bandwidth prediction to overcome inaccurate information of the existing QoS routing algorithms. We have evaluated the performance of the proposed model and the existing algorithms on MCI networks, it thus appears that we have verified the performance of this algorithm.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.593-605
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• 2017

In applications which are covered wide range, it is possible that one or more number of Unmanned Aerial Vehicle(UAV) squadrons are used to perform a mission. In this case, it is most important to communicate seamlessly between the UAV squadrons. In this paper, we applied the modified OLSR(OSLR-Pds) which can prediction for state of the link for the communication in UAV squadron, and applied the modified AOMDV which can build multi-path for the communication between UAV Squadrons. The mobility of nodes are modeled using Gauss-Markov algorithm, and relative speed between nodes were calculated by derive equation of movement, and thereby we can predict link state for in a squadron and between squadrons. An experiment for comparing AODV, AOMDV and the proposed routing protocol was conducted by three factors such as packet delivery ratio, end to end delay, and routing overhead. In experiment result, we make sure that the proposed protocol performance are superior in these three factors. However, if the density of the nodes constituting FANET are too low, and if the moving speed of node is very slow, there is no difference to others protocols.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.970-977
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• 2014

In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.3
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• pp.375-381
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• 2012

Two experiments were conducted to establish a digestible energy (DE) content prediction model of rapeseed meal for growing-finishing pig based on rapeseed meal's chemical composition. In experiment 1, observed linear relationships between the determined DE content of 22 rapeseed meal calibration samples and proximate nutrients, gross energy (GE) and neutral detergent fiber (NDF) were used to develop the DE prediction model. In experiment 2, 4 samples of rapeseed meal selected at random from the primary rapeseed growing regions of China were used for testing the accuracy of DE prediction models. The results indicated that the DE was negatively correlated with NDF (r = -0.86) and acid detergent fiber (ADF) (r = -0.73) contents, and moderately correlated with gross energy (GE; r = 0.56) content in rapeseed meal calibration samples. In contrast, no significant correlations were found for crude protein, ether extract, crude fiber and ash contents. According to the regression analysis, NDF or both NDF and GE were found to be useful for the DE prediction models. Two prediction models: DE = 16.775-0.147${\times}$NDF ($R^2$ = 0.73) and DE = 11.848-0.131${\times}$NDF+0.231${\times}$GE ($R^2$ = 0.76) were obtained. The maximum absolute difference between the in vivo DE determinations and the predicted DE values was 0.62 MJ/kg and the relative difference was 5.21%. Therefore, it was concluded that, for growing-finishing pigs, these two prediction models could be used to predict the DE content of rapeseed meal with acceptable accuracy.

• Journal of Communications and Networks
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• v.1 no.3
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• pp.153-157
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• 1999

In this paper, we derive finite-dimensional non-linear fil-ters for optimally reconstructing speech signals in Switched Predic-tion vocoders, Code Excited Linear Prediction(CELP) and Differ-ential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.