• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1078-1081
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• 2002

In microcell or picocell mobile communication using cellular method, we suggested propagation prediction model which can accurately and rapidly interpret mobile communication propagation environment in urban, when subscriber service is done based on the main road in urban. Further, we simulated suggested propagation prediction model under the hypothesis of urban propagation environment of PCS mobile communication, analyzed receiving field strength by area within a cell, and finally suggested the optimal transmitting power and location condition of microcell or picocell mobile communication base station

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.136-151
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• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.16-21
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• 2015

Most video services are transmitted in wireless networks. In a network environment, a packet of video is likely to be lost during transmission. For this reason, numerous error concealment (EC) algorithms have been proposed to combat channel errors. On the other hand, most existing algorithms cannot conceal the whole missing frame effectively. To resolve this problem, this paper proposes a new Adaptive Prediction Unit-based Motion Vector Extrapolation (APMVE) algorithm to restore the entire missing frame encoded by High Efficiency Video Coding (HEVC). In each missing HEVC frame, it uses the prediction unit (PU) information of the previous frame to adaptively decide the size of a basic unit for error concealment and to provide a more accurate estimation for the motion vector in that basic unit than can be achieved by any other conventional method. The simulation results showed that it is highly effective and significantly outperforms other existing frame recovery methods in terms of both objective and subjective quality.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.619-622
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• 2000

This research presents an analytical model to predict the instruction fetch rate on superscalar Processors. The proposed model is also able to analyze the performance relationship between cache miss and branch prediction miss. The proposed model takes into account various kind of architectural parameters such as branch instruction probability, cache miss rate, branch prediction miss rate, and etc.. To prove the correctness of the proposed model, we performed extensive simulations and compared the results with those of the analytical models. Simulation results showed that the pro-posed model can estimate the instruction fetch rate accurately within 10% error in most cases. The model is also able to show the effects of the cache miss and branch prediction miss on the performance of instruction fetch rate, which can provide an valuable information in designing a balanced system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.97-104
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• 2006

The state-of-the-art H.264/AVC standard was designed for the lossy video coding so that it could not yield the best performance for lossless video coding. In this paper, we propose two efficient intra lossless coding methods by embedding a pixel-wise prediction into the H.264/AVC. One is based on the pixel-wise prediction for the residual signal of the H.264/AVC intra Prediction and the other suggests a newly additional intra prediction mode for the conventional intra prediction. We found that the proposed lossless coding algorithms could achieve approximately $12%{\sim}25%$ more bit saving compared to the H.264/AVC FRExt high profile for several test sequences in terms of a compression ratio.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.959-974
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• 2020

High efficiency video coding (HEVC) employs quadtree coding tree unit (CTU) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search processes for an optimal coding unit (CU) partition. With the aim of solving the problem, a fast CU size decision optimal algorithm based on neighborhood prediction is presented for HEVC in this paper. The contribution of this paper lies in the fact that we successfully use the partition information of neighborhood CUs in different depth to quickly determine the optimal partition mode for the current CU by neighborhood prediction technology, which can save much computational complexity for HEVC with negligible RD-rate (rate-distortion rate) performance loss. Specifically, in our scheme, we use the partition information of left, up, and left-up CUs to quickly predict the optimal partition mode for the current CU by neighborhood prediction technology, as a result, our proposed algorithm can effectively solve the problem above by reducing many unnecessary prediction and partition operations for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on neighborhood prediction in this paper can reduce about 19.0% coding time, and only increase 0.102% BD-rate (Bjontegaard delta rate) compared with the standard reference software of HM16.1, thus improving the coding performance of HEVC.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.420-430
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• 2006

In this paper, we propose a flexible GGOP prediction structure to improve coding efficiency for multi-view video coding. In general, reference software used for MVC uses the fixed GGOP prediction structure. However, the performance of MVC depends on the base view and numbers of B-pictures between I-picture(or P-picture) and P-picture. In order to implement the flexible GGOP prediction structure, the location of base view is decided according to the global disparities among the adjacent sequences. Numbers of B-pictures between I-picture(or P-picture) and P-picture are decided by camera arrangement such as the baseline distance among the cameras. The proposed method shows better result than the reference software of MVC. The proposed prediction structure shows considerable reduction of coded bits by 7.1%.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1173-1178
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• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.