• ETRI Journal
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• v.36 no.4
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• pp.528-536
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• 2014

High Efficiency Video Coding (HEVC) is the most recent video coding standard to achieve a higher coding performance than the previous H.264/AVC. In order to accomplish this improved coding performance, HEVC adopted several advanced coding tools; however, these cause heavy computational complexity. Similar to previous video coding standards, motion estimation (ME) of HEVC requires the most computational complexity; this is because ME is conducted for three inter prediction modes - namely, uniprediction in list 0, uniprediction in list 1, and biprediction. In this paper, we propose an efficient inter prediction mode (EIPM) decision method to reduce the complexity of ME. The proposed EIPM method computes the priority of all inter prediction modes and performs ME only on a selected inter prediction mode. Experimental results show that the proposed method reduces computational complexity arising from ME by up to 51.76% and achieves near similar coding performance compared to HEVC test model version 10.1.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.5
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• pp.233-240
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• 2001

This paper presents a time series prediction method using a fuzzy rule-based system. Extracting fuzzy rules by performing a simple one-pass operation on the training data is quite attractive because it is easy to understand, verify, and extend. The simplest method is probably to relate an estimate, x(n+k), with past data such as x(n), x(n-1), ..x(n-m), where k and m are prefixed positive integers. The relation is represented by fuzzy if-then rules, where the past data stand for premise part and the predicted value for consequence part. However, a serious problem of the method is that it cannot handle nonstationary data whose long-term mean is varying. To cope with this, a new training method is proposed, which utilizes the difference of consecutive data in a time series. In this paper, typical previous works relating time series prediction are briefly surveyed and a new method is proposed to overcome the difficulty of prediction nonstationary data. Finally, computer simulations are illustrated to show the improved results for various time series.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.759-765
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• 2010

In this paper, two new inverse regression methods are introduced. One is a distance based method, and the other is a likelihood based method. While a model is fitted by minimizing the sum of squared prediction errors of y's and x's in the classical and inverse methods, respectively. In the new distance based method, we simultaneously minimize the sum of both squared prediction errors. In the likelihood based method, we propose an inverse regression with Arnold-Beaver Skew Normal(ABSN) error distribution. Using the cross validation method with an asymmetric real data set, two new and two existing methods are studied based on the relative prediction bias(RBP) criteria.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.538-543
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• 2003

This study is the developed prediction of residual effective prestress force of prestressed concrete beam bridges. Developed prediction method is based on the center camber of prestressed concrete beam, structural design. report of various PSC beams, construction reference materials of PSC beams. Evaluation of residual effective prestress force by developed method is compared with evaluation by structural design. This comparison results shows that this developed method is very effective method. Therefore prediction of residual effective prestress force by this developed method will be used for evaluation of the rating of various PSC beam bridges(road bridges and railway bridges).

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.06b
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• pp.54-59
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• 1998

In this study, two kinds of life prediction method for hot forming die are developed . One is empirical method requiring some experiment that evaluate thermal softening of die material accoring to operating conditions. The other is analyticl method that calcuate wear quantity of die occuring during the forming process. Wear is a predominant factor as well as plastic deformation and heat checking . And, these methods are applied to prodict tool life real die producting part for automobile. Thus , the applicability and the accuracy of the presented methods are investigated. Using the verified life prediction method above , optimal blocker die design minimizing the finisher die is done.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1452-1460
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• 2001

A new method of parameter determination in the fatigue residual strength degradation model is proposed. The new method and minimization technique is compared experimentally to account for the effect of tension-compression fatigue loading of spheroidal graphite cast iron and graphite/epoxy laminate. It is shown that the correlation between the experimental results and the theoretical prediction on the fatigue life and residual strength distribution using the proposed method is very reasonable. Therefore, the proposed method is more adjustable in the determination of the parameter than minimization technique for the prediction of the fatigue characteristics.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.100-105
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• 2019

In wire drawing, aterial deformation is concentrated on the surface of the drawn wire because of surface contact with the drawing die. Therefore, strain varies from the center to the surface of the drawn wire. In this study, based on the upper bound method, an effective strain prediction method from the center to the surface of a drawn wire was proposed. Using the proposed method, the effective strain of the drawn wire was calculated verify the proposed prediction method, the predicted effective strain was compared with the result of finite element analysis.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1248-1266
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• 2012

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.