• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.590-592
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• 2003

본 논문에서는 대역폭 예약을 통한 무선 단말의 이동성 예측기법, 무선 단말의 이동 패턴을 이용한 이동성 예측기법과 무선 단말의 이동 패턴과 체류시간을 이용한 이동성 예측기법에 대하여 살펴본다. 대역폭 예약을 통한 이동성 예측기법에서의 자원 낭비 해결 방안과 이동 패턴 및 단말의 체류시간을 이용한 이동성 예측기법에서의 무선 단말의 셀에 장기간 체류시 대역폭의 낭비의 해결방안 그리고 이동성 예측 실패시 패킷손실을 없앨 수 있는 방안에 대해서 제안하였다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.8
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• pp.708-719
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• 2007

In case of indoor gymnastics training floor, in view of its characteristics, since it is simultaneously required the related smooth communication between the coach and the player, also the acoustic performance regarding to the clearness of music, besides the sport activity, the consideration about the acoustic character has entered the stage as an indispensable element. On such viewpoint, recently constructed dome-typed gymnastic training floor was optimized acoustic design with remodeling through acoustic simulation test. And acoustic satisfaction degree and reaction was attempted to investigate about the gymnastics training floor estimating value of human's psychological(sensual) degree using auralization that enables to experience the virtual sound field at the stage of design. As the result of investigation about the research on the space of object, it could be known that the valuation regarding to the acoustic performance of 'after-improvement' was distinctly more refined than that of 'before-improvement'. It is now considering that such result of the study can be utilized as the useful data which enables to improve the retrenchment effect of the construction cost as well as the acoustic capability, by means of the prediction control on the acoustic problem from the stage of design, for the occasion when the similar indoor sport gymnasium is planning to build for the near future.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.353-361
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• 2020

VVC (Versatile Video Coding) is a new video compression technique that is being standardized, and it supports HD / UHD / 8K video, and High Dynamic Range (HDR) video with a goal of approximately 2 times higher coding efficiency than the conventional HEVC. It also aims to support a variety of functionalities such as screen content coding, adaptive resolution changes, and independent sub-pictures. In this paper, we investigate the signaling process of intra prediction mode first, and develop an effective coding method of the chroma intra prediction mode. In case of the DM mode, the proposed method simplifies the prediction mode of the chorma intra prediction mode when referring to the angular mode of the luminance block. It can improve coding efficiency of the chroma intra prediction mode, and the proposed process can also consider the size of the block in order to further improve its coding efficiency.

• Asian-Australasian Journal of Animal Sciences
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• v.7 no.3
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• pp.441-448
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• 1994

Postweaning performance data were obtained on 187 group fed purebred Angus calves from 12 selected sires (six high and six low feed conversion sires) in 1985 and 1986. The objective of this portion of the study was to develop prediction equations for feed conversion from a stepwise regression analysis. Variables measured were on-test weight (ONTSTWT), on-test age (ONTSTAG), five weights by 28-d periods, seven linear body measurements: heart girth (HG), hip height (HH), head width (HDW), head length (HDL), muzzle circumference (MC), length between hooks and pins (HOPIN) and length between shoulder and hooks (SHHO), and backfat thickness (BF). Stepwise regressions for maintenance adjusted feed conversion (ADJFC) and unadjusted feed conversion (UNADFC) over the first 140 d of the test, and total feed conversion (FC) until progeny reached 8.89 mm of back fat were obtained separately by conversion groups and sexes and for combined feed conversion groups and sexes. In general, weights were more important than linear body measurements in prediction of feed utilization. To some extent this was expected as weight is related directly to gain which is a component of feed conversion. Weight at 112 d was the most important variable in prediction of feed conversion when data from both feed conversion groups and sexes were combined. Weights at 84 and 140 d were important variables in prediction of UNADFC and FC, respectively, of bulls. ONTSTWT and weight at 140 d had the highest standardized partial regression coefficients for UNADFC and ADJFC, respectively, of heifers. Results indicated that linear measurements, such as MC, HDL and HOPIN, are useful in prediction of feed conversion when feed in takes are unavailable.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• Korean Business Review
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• v.20 no.1
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• pp.145-168
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• 2007

Recommender system helps customers to find easily items and helps the e-biz companies to set easily their target customer by automated recommending process. Recommender systems are being adopted by several e-biz companies and from these systems, both of customers and companies take some benefits. This study sets several thresholds to the similarity weight, which indicates a degree of similarity of two customers' preference, to improve the performance of prediction accuracy. According to the threshold, the accuracy of prediction is being improved but some threshold setting shows the reduction of the prediction rate, which is the coverage. This coverage reduction has male effect on the prediction accuracy of customers, so more study on the prediction accuracy of recommender system and to maximize the coverage are needed.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.223-231
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• 1997

In this paper, an interframe coding method for volumetric medical images is proposed. By treating interslice variations as the motion of bones or tissues, we use the motion compensation (MC) technique to predict the current frame from the previous frame. Instead of a block matching algorithm (BMA), which is the most common motion estimation (ME) algorithm in video coding, image warping with biolinear transformation has been suggested to predict complex interslice object variation in medical images. When an object disappears between slices, however, warping prediction has poor performance. In order to overcome this drawback, an overlapped block motion compensation (OBMC) technique is combined with carping prediction. Motion compensated residual images are then encoded by using an embedded zerotree wavelet (EZW) coder with small modification for consistent quality of reconstructed images. The experimental results show that the interframe coding suing warping prediction provides better performance compared with interframe coding, and the OBMC scheme gives some additional improvement over the warping-only MC method.

• Computers and Concrete
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• v.24 no.5
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.62-74
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• 2001

In this study, the prediction of performances and emissions of the gasoline engine of a light passenger car has been accomplished. The method of characteristics including friction, heat transfer, area change and entropy gradients was used to analyze the flow in the intake and exhaust systems. For in-cylinder calculation, the single-zone model was adopted for the periods of the intake, exhaust, compression and the expansion of the burnt gas and the 2-zone expansion model was applied to the period of combustion process. The simulation program was verified by comparison with the experimental values both for the naturally aspirated engine and the turbocharged engine showing good agreements. Using the simulation program, multi-valve system and turbocharging were examined as a means of increasing engine Performances.