• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.741-751
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• 2000

To achieve high performance by exploiting instruction level parallelism aggressively in superscalar processors, it is necessary to overcome the limitation imposed by control dependences and data dependences which prevent instructions from executing parallel. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively its data dependent instruction based on the predicted outcome. In this paper, a hybrid value prediction scheme with dynamic classification mechanism is proposed. We design a hybrid predictor by combining the last predictor, a stride predictor and a two-level predictor. The choice of a predictor for each instruction is determined by a dynamic classification mechanism. This makes each predictor utilized more efficiently than the hybrid predictor without dynamic classification mechanism. To show performance improvements of our scheme, we simulate the SPECint95 benchmark set by using execution-driven simulator. The results show that our scheme effect reduce of 45% hardware cost and 16% prediction accuracy improvements comparing with the conventional hybrid prediction scheme and two-level value prediction scheme.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.755-762
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• 2023

The reliability of measuring instruments is essential in measuring cosmic radiation. To demonstrate this importance, this study measured and compared the amount of cosmic radiation using Liulin and TEPC, operated in South Korea, on a flight between Incheon, South Korea and LA, the US. In addition, since prior analysis based on a prediction program is necessary in advance to check the dose of cosmic radiation, this study utilized KREAM developed in Korea and the CARI-6M developed by the FAA to acquire the predicted value. As a result of the verification, the reliability of the two devices falls within the acceptable level of 20%, proving the reliability. Moreover, the differences between the values acquired by each prediction program were only subtle. Nevertheless, the analysis demonstrated that the prediction value obtained by the programs and the measured value had significant differences. Therefore, additional correction of the discrepancies or continuous research for such is required to match the predicted values are similar to the actual measured values.

• The Journal of Information Systems
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• v.14 no.2
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• pp.277-292
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• 2005

This paper uses a data mining approach to develop bankruptcy prediction models suitable for traditional (off-line) companies and electronic (on-line) companies. It observes the differences in the composition prediction models between these two types of companies and provides interpretation of bankruptcy classifications. The bankruptcy prediction models revealed the major variables in predicting bankruptcy to be 'cash flow to total assets' and 'gross value-added to net sales' for traditional off-line companies while 'cash flow to liabilities','gross value-added to net sales', and 'current ratio' for electronic companies. The accuracy rates of final prediction models for traditional off-line and electronic companies were found to be $84.7\%\;and\;82.4\%$, respectively. When the model for traditional off-line companies was applied for electronic companies, prediction accuracy dropped significantly in the case of bankruptcy classification (from $70.4\%\;to\;45.2\%$) at the level of a blind guess ($41.30\%$). Therefore, the need for different models for traditional off-line and electronic companies is justified.

• Food Science and Preservation
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• v.30 no.2
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.84-98
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• 2024

This study proposed a prediction equation for the estimation of blasting vibaration and blasting noise, utilizing 320 datasets for the blasting vibration and blasting noise acquired during urban blasting works in the Incheon, Suwon, Wonju, and Yangsan regions. The proposed blasting vibration prediction equation, derived from regression analysis, indicated correlation coefficients of 0.879 and 0.890 for SRSD and CRSD, respectively, with an R² value exceeding 0.7. In the case of the blasting noise prediction equation, stepwise regression analysis yielded a correlation coefficient of 0.911 between the prediction values and real measurements for the blasting nosie, and further analysis to determine the constant value revealed a correlation coefficient of 0.881, with an R² value also exceeding 0.7. These results suggest the feasibility of applying the proposed prediction equations when environmental impact assessments or education environment evaluation according to urban development or apartment construction projects is performed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.709-714
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• 2008

The various computer programs are used in computer simulation of the traffic noise prediction. But the difference or problem of calculation method used for road traffic noise prediction is not exactly investigated. In this paper, Road traffic noise is predicted on the specific regions by using four prediction methods such as XPS31-133 model(France), RLS-90 model(Germany), ASJ RTN model(Japan) and FHWA model(U.S.A.), which are operated by a program named SoundPLAN, a program to predict road traffic noise. Those prediction values are compared with a measurement value. The results show that four prediction values for taraffic noise are a little different, because of various input factors according to the prediction methods.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• The Journal of Korean Physical Therapy
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• v.19 no.3
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• pp.41-46
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• 2007

Purpose: This study was to investigate how dose the radiography findings are to magnetic resonance (MR) image findings in the L5-S1 instability patients. The subjects of this study were comprised of eleven males and fifteen females, who had Lumbago and agreed with this research. Methods: Radiography and MR images of Lumbar spine were acquired respectively from subjects in conditions of maximum flexion and extension. The horizontal and angular displacements in lumabosacral spine radiography were used to assess the instability of lumbar spine. MR images were also used to evaluate the intervertebral disc abnormalities and change of bone marrow. Results: The results are as follows. 1. In the case of flexion transitional displacement proposed by Dupuis et al, the specificity and negative predictive value were good accuracy ($0.7{\sim}0.8$), and the negative predictive value was in average. In the case of extension displacement, the negative predictive value was about average ($0.6{\sim}0.7$), but the sensitivity, specificity and positive predictive value were below the poor (<0.6). On the other side, the specificity was about average but other things were below in the case of angular displacement. 2. In the case of flexion transitional displacement proposed by Dupuis et al., compared with the intervertebral disc abnormalities, the negative prediction value was excellent, the sensitivity good, and the specificity about average. In the case of extension, the negative prediction value was about average, but the other things were poor. On the other side the specificity and negative predictive value had good accuracy and the sensitivity and positive prediction value were below average in the case of angular displacement. Conclusion: The above results show that the radiography finding is sufficiently helpful to find the lumbar spine instability as an economic point of view.