• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.444-450
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• 2013

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.31-37
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• 2006

To achieve the efficient performance within a mobile grid considering the intermittent network connectivity and non-dedicated heterogeneous mobile devices, this paper suggests the scheduling algorithm of job allocation as a viable solution. The suggested scheduling algorithm has two core functions, the prediction of response time for task processing and the identification of the optimal number of mobile devices to process the mobile grid applications. This scheduling algorithm suggests the numerical formulas to calculate the network latency considering the effects of heterogeneous non-dedicated mobile system in wireless network environments. Also we evaluate the performance of mobile grid system using the processing the distributed applications in implemented mobile grid environments.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.2
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• pp.21-31
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• 1999

This paper presents a new possibility of calculating optimal control for large scale which consist of non-linear dynamic sub-systems using two level hierarchical structures method. And the proposed method is based on the idea of block pulse transformation to simplify the algorithm and its calculation. This algorithm used an expansion around the equilibrium point of the system to fix the second and higher order terms. These terms are compensated for iteratively at the second level by providing a prediction for the states and controls which form of a part of the higher order terms. In this new approach the quadratic penalty terms are not used in the cost function. This allows convergence over a longer time horizon and also provides faster convergence. And the method is applied to the problem of optimization of the synchronous machine. Results show that the new approach is superior to conventional numerical method or other previous algorithm.

• Smart Structures and Systems
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• v.33 no.5
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• pp.365-374
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• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• ETRI Journal
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• v.18 no.2
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• pp.61-74
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• 1996

We propose two new rate control schemes to improve MPEG-2 rate control in view of visual quality when scene changes happen. Two proposed schemes are characterized by real-time and non real-time improvement to reduce the impact of scene changes. We also propose a new target-bit prediction method using spatial activity of pictures and present a simple and efficient scene change detection scheme using signed difference of mean absolute difference (MAD). Computer simulation results show that the proposed real-time algorithm effectively alleviates visual quality degradation after scene changes. The proposed non real-time algorithm gives maximum 2 dB improvement in peak signal-to-noise ratio (PSNR) at a scene-changed picture, compared with MPEG-2 rate control scheme and it shows better quality than the real-time one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1302-1307
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• 2013

MOV(Metal Oxide Varistor) is the most important part of SPD(Surge Protective Device) which can protect electric facilities from an impulse current such as a lightning. So far, the fault of MOVs have decided only by surge count without considering magnitude of surge current and an amount of input energy. This paper proposed the fault prediction algorithm for the MOV using look up table made by surge count and input current data which have non-linear characteristics for input current and are estimated by high voltage experimental results. Proposed algorithm was proved by experiment on verification at a high voltage laboratory.

• Journal of Soil and Groundwater Environment
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• v.17 no.5
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• pp.56-67
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• 2012

In the present study, an enhanced subsurface prediction algorithm based on a non-parametric geostatistical model and a history matching technique through Gibbs sampler is developed and the iterative prediction improvement procedure is proposed. The developed model is applied to a simple two-dimensional synthetic case where domain is composed of three different hydrogeologic media with $500m{\times}40m$ scale. In the application, it is assumed that there are 4 independent pumping tests performed at different vertical interval and the history curves are acquired through numerical modeling. With two hypothetical borehole information and pumping test data, the proposed prediction model is applied iteratively and continuous improvements of the predictions with reduced uncertainties of the media distribution are observed. From the results and the qualitative/quantitative analysis, it is concluded that the proposed model is good for the subsurface prediction improvements where the history data is available as a supportive information. Once the proposed model be a matured technique, it is believed that the model can be applied to many groundwater, geothermal, gas and oil problems with conventional fluid flow simulators. However, the overall development is still in its preliminary step and further considerations needs to be incorporated to be a viable and practical prediction technique including multi-dimensional verifications, global optimization, etc. which have not been resolved in the present study.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.611-612
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In this paper, we compare the electricity charge between industrial power system with ESS - this case's operation is based on Non-prediction operation method. - and without ESS. In addition, we derive the payback period.