• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.236-239
• /
• 1988

Prediction errors generated by motion compensated coding are coded with transform coding techniques as DCT. The performance of transform coding techniques are lowered mainly due to the source characteristics with a great deal of zero populations and plus-minus sign changes, i.e., low correlation. In this paper a transform coding scheme which adopts a decomposition of prediciton errors into two sources is proposed and compared its performance with conventional scheme.

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

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.10
• /
• pp.2676-2685
• /
• 1998

In order to improve the branch prediction accuracy and to reduce the BTB miss rate, this paper proposes a two-level BTB structure that adds small-sized victim BTB to the convetional BTB. With small cost, two-level BTB can reduce the BTB miss rate as well as improve the prediction accuracy of the hybrid branch prediction strategy which combines dynamic prediction and static prediction. Through the trace-driven simulation of four bechmark programs, the performance improvement by the proposed two-level BTB structure is analysed and validated. Our proposed BTB structure can improve the BTB miss rate by 26.5% and the misprediction rate by 26.75%

• Atmosphere
• /
• v.31 no.5
• /
• pp.511-523
• /
• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Journal of the Korea Computer Industry Society
• /
• v.7 no.3
• /
• pp.171-178
• /
• 2006

Value prediction of instruction issue width in superscalar processor is a technique to obtain performance gains by supplying earlier source values of its data dependent instructions using predicted value of a instruction. In this paper, the mean performance improvement by predictor as well as prediction accuracy and prediction rate are moaned and assessed by comparison and analysis of value predictor that instruction issue width(4,8,16) in parallel and run by predicting value, which is for performance improvements of ILP[4]. The experiment result show the superiority hight performance of 8-issue.

• Atmosphere
• /
• v.29 no.2
• /
• pp.149-164
• /
• 2019

The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.10
• /
• pp.1-10
• /
• 2009

Precise branch predictor has a profound impact on system performance in modern processor architectures. Recent works show that prediction latency as well as prediction accuracy has a critical impact on overall system performance as well. However, prediction latency tends to be overlooked. In this paper, we propose Branch Pre-Prediction policy to tolerate branch prediction latency. The proposed solution allows that branch predictor can proceed its prediction without any information from the fetch engine, separating the prediction engine from fetch stage. In addition, we propose newly modified BTE structure to support our solution. The simulation result shows that proposed solution can hide most prediction latency with still providing the same level of prediction accuracy. Furthermore, the proposed solution shows even better performance than the ideal case, that is the predictor which always takes a single cycle prediction latency. In our experiments, IPC improvement is up to 11.92% and 5.15% in average, compared to conventional predictor system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1734-1737
• /
• 2019

This paper presents a prediction structure with a Hybrid Dynamic Branch Prediction (HDBP) scheme which decreases the number of stalls. In the application, a branch history register is dynamically adjusted to produce more unique index values of pattern history table (PHT). The number of stalls is also reduced by using the modified gshare predictor with a long history register folding scheme. The aliasing rate decreased to 44.1% and the miss prediction rate decreased to 19.06% on average compared with the gshare branch predictor, one of the most popular two-level branch predictors. Moreover, Compared with the gshare, an average improvement of 1.28% instructions per cycle (IPC) was achieved. Thus, with regard to the accuracy of branch prediction, the HDBP is remarkably useful in boosting the overall performance of the superscalar processor.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.3
• /
• pp.57-64
• /
• 2019

In this paper, an efficient prediction-based parallel simulation method using spatially partial simulation strategy is proposed for improving both the performance of the event-driven gate-level timing simulation and the debugging efficiency. The proposed method quickly generates the prediction data on-the-fly, but still accurately for the input values and output values of parallel event-driven local simulations by applying the strategy to the simulation at the higher abstraction level. For those six designs which had used for the performance evaluation of the proposed strategy, our method had shown about 3.7x improvement over the most general sequential event-driven gate-level timing simulation, 9.7x improvement over the commercial multi-core based parallel event-driven gate-level timing simulation, and 2.7x improvement over the best of previous prediction-based parallel simulation results, on average.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.12
• /
• pp.1748-1755
• /
• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.