• Journal of Digital Convergence
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• v.7 no.3
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• pp.25-33
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• 2009

Historically, performance prediction and sizing of server systems have been the key purchasing argument for customer. To accurate server's sizing and performance prediction, it is necessary to correctness guideline for sizing and performance prediction. But existing guidelines have many errors. So, we examine the benchmarks of performance organization such as SPEC and TPC. And then we consider to TPC-C and TPC-E benchmarks for OLTP server's sizing and performance prediction that is a basic concept of guidelines. Eventually, we propose improvement of errors in guidelines.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.347-356
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• 2009

Precise branch prediction is a critical factor in the IPC Improvement of modern microprocessor architectures. In addition to the branch prediction accuracy, branch prediction delay have a profound impact on overall system performance as well. However, it tends to be overlooked when the architects design the branch predictor. To tolerate branch prediction delay, this paper proposes Early Start Prediction (ESP) technique. The proposed solution dynamically identifies the start instruction of basic block, called as Basic Block Start Address (BB_SA), and the solution uses BB_SA when predicting the branch direction, instead of branch instruction address itself. The performance of the proposed scheme can be further improved by combining short interval hiding technique between BB_SA and branch instruction. The simulation result shows that the proposed solution hides prediction latency, with providing same level of prediction accuracy compared to the conventional predictors. Furthermore, the combination with short interval hiding technique provides a substantial IPC improvement of up to 10.1%, and the IPC is actually same with ideal branch predictor, regardless of branch predictor configurations, such as clock frequency, delay model, and PHT size.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.30-41
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• 2023

Aviation turbulence, caused by atmospheric eddies, is a disruptive phenomenon that leads to abrupt aircraft movements during flight. To minimize the damages caused by such aviation turbulence, the Aviation Meteorological Office provides turbulence information through the Korea aviation Turbulence Guidance (KTG) and the Global-Korean aviation Turbulence Guidance (GKTG). In this study, we evaluated the performance of the KTG and GKTG models by comparing the in-situ EDR observation data and the generated aviation turbulence prediction data collected from the mid-level Korean Peninsula region from January 2019 to December 2021. Through objective validation, we confirmed the level of prediction performance and proposed improvement measures based on it. As a result of the improvements, the KTG model showed minimal difference in performance before and after the changes, while the GKTG model exhibited an increase of TSS after the improvements.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1652-1659
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• 1998

To provide performance gains by reducing the operand referencing latency and data cache bandwidth requirements, we present an operand reference prediction cache (ORPC) which predicts operand value and address translation during the instruction fetch stage. The prediction is verified in the early stage, and thus it minimizes the performance penalty caused by the misprediction. Through the trace-driven simulation of six benchmark programs, the performance improvement by proposed three aRPC stmctures (OfiPC1, OfiPC2. ORPC3)is analysed and validated.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.501-506
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• 2009

In the H.264/ AVC video coding standard, the intra-prediction coding with various block sizes offers a considerably high improvement in coding efficiency compared to previous standards. In order to achieve this, H.264/AVC uses the Rate-distortion optimization (RDO) technique to select the best intraprediction mode for a macroblock, and it brings about the drastic increase of the computation complexity of H.264 encoder. To reduce the computation complexity and stabilize the coding performance on visual quality, this paper proposed a fast intra-prediction mode decision algorithm using non-parametric thresholds and simplified directional masks. The use of nonparametric thresholds makes the intra-coding performance not be dependent on types of video sequences and simplified directional masks reduces the compuation loads needed by the calculation of local edge information. Experiment results show that the proposed algorithm is able to reduce more than 55% of the whole encoding time with a negligible loss in PSNR and bitrates and provides the stable performance regardless types of video sequences.

• Information Systems Review
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• v.24 no.1
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• pp.39-57
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• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.19 no.1
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• pp.24-28
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• 2011

This paper reviews a ceiling prediction method based on a mesoscale meteorological modeling system in South Korea. The study was motivated by the tendency of higher model ceiling height than the observed in daily operational forecasts. The goal of the paper is to report an effort to improve the operational ceiling prediction skill by conducting numerical experiments controlling a model parameter. In a case experiment, increasing constant values used in the relationship between extinction coefficients and concentration showed better performance, indicating a short-term strategy for operational local ceiling forecast improvement.

• The KIPS Transactions:PartA
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• v.15A no.4
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• pp.217-226
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• 2008

Branch prediction accuracy is critical for system performance in modern microprocessor architectures. The use of speculative update branch history provides substantial accuracy improvement in branch prediction. However, speculative update branch history is the information about uncommitted branch instruction and thus it may hurts program correctness, in case of miss-speculative execution. Therefore, speculative update branch history requires suitable recovery mechanisms to provide program correctness as well as performance improvement. In this paper, we propose recovery logics for speculative update branch history. The proposed solutions are recovery logics for both global history and local history. In simulation results, our solution provides performance improvement up to 5.64%. In addition, it guarantees the program correctness and almost 90% of additional hardware overhead is reduced, compared to previous works.