• Journal of computational fluids engineering
• /
• v.2 no.1
• /
• pp.84-92
• /
• 1997

In this paper a new structured grid generation technique is introduced. This new technique utilizes predictor-corrector approach, and is a marching scheme in the global sense as the hyperbolic scheme is. In the predictor step, one layer of grid cells is obtained by using Modified Advancing Front Method which generates a collection of quadrilateral cells simultaneously. In the corrector step, the layer of grid cells that is calculated in the predictor step is adjusted by solving Laplace equations to prevent grid lines from skewing and overlapping in highly curved configurations. It is shown that the resultant algorithm, named a MAP scheme, which combines the Modified Advancing Front Method as a Predictor with an elliptic scheme as a corrector can be used to generate globally smooth and locally near-orthogonal grids for external flow fields even for highly curved configurations. Examples of grid generations for external flow fields about several configurations by use of the present approach are given, and its applicability and flexibility have been demonstrated and discussed.

• Journal of the Korean Home Economics Association
• /
• v.36 no.2
• /
• pp.47-60
• /
• 1998

The effects of personal(gender, physical growth) and environmental(communication with parent, intimacy of friendship, school performance, and satisfaction of school-life) factors on adolescent's self-esteem were examined in a samlpe of 525 first and second grades in middle school. The subdomains of the self-esteem were peer-related self, home self, teacher-related self, academic self, physical appearance self, physical competence self, personality self, and general self. T-test, Pearson's correlation, and regression were used as statistical analysis. Results were as follows. First, there was evidence of a gender difference in the level of the subsdomains of self-esteem: teacher-related, physical-appearance, physical-competence, and personality. Second, the factor which was the most powerful predictor of each subdomain of the self-esteem was as follows 1) the most powerful predictor of the peer-related self was the intimacy of friendship, 2) the most powerful predictor of the home self was the communication with parent, 3) the most powerful predictor of the teacher-related self was the satisfaction of school-life, 4) the most powerful predictor of the academic self was the school performance, 5)the most powerful predictor of the physical-appearance self, the physical competence self, and the personality self was the satisfaction of school-life, 6) the most powerful predictor of the general self was the school performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.3
• /
• pp.272-279
• /
• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• Communications for Statistical Applications and Methods
• /
• v.20 no.4
• /
• pp.291-300
• /
• 2013

In this paper, we propose method-free permutation predictor hypothesis tests in the context of sufficient dimension reduction. Different from an existing method-free bootstrap approach, predictor hypotheses are evaluated based on p-values; therefore, usual statistical practitioners should have a potential preference. Numerical studies validate the developed theories, and real data application is provided.

• Journal of KIISE
• /
• v.44 no.5
• /
• pp.486-495
• /
• 2017

Collaborative Filtering is limited because of the cost that is required to perform the recommendation (such as the time complexity and space complexity). The RFWBP (Rating Frequency Weight-based Baseline Predictor) that approximates the precision of the existing methods is one of the efficiency methods to reduce the cost. But, the following issues need to be considered regarding the RFWBP: 1) It does not reduce the error because the RFWBP does not learn for the recommendation, and 2) it recommends all of the items because there is no condition for an appropriate recommendation list when only the RFWBP is used for the achievement of efficiency. In this paper, the BBP (Bias-Based Predictor) is proposed to solve these problems. The BBP reduces the error range, and it determines some of the cases to make an appropriate recommendation list, thereby forging a recommendation list for each case.

• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.8
• /
• pp.741-751
• /
• 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 KIISE:Computing Practices and Letters
• /
• v.8 no.1
• /
• pp.107-115
• /
• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.

• Journal of KIISE:Computer Systems and Theory
• /
• v.28 no.11
• /
• pp.601-612
• /
• 2001

In superscalar processors, value prediction is a technique that breaks true data dependences by predicting the outcome of an instruction in order to exploit instruction level parallelism(ILP). A value predictor looks up the prediction table for the prediction value of an instruction in the instruction fetch stage, and updates with the prediction result and the resolved value after the execution of the instruction for the next prediction. However, as the instruction fetch and issue rates are increased, the same instruction is likely to fetch again before is has been updated in the predictor. Hence, the predictor looks up the stale value in the table and this mostly will cause incorrect value predictions. In this paper, a stride value predictor with the capability of speculative updates, which can update the prediction table speculatively without waiting until the instruction has been completed, is proposed. Also, the performance of the scheme is examined using Simplescalar simulator for SPECint95 benchmarks in which our value predictor is added.

• Genomics & Informatics
• /
• v.19 no.1
• /
• pp.10.1-10.7
• /
• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.19 no.7
• /
• pp.15-19
• /
• 2005

The practical control problems for the lime-delay system is considered. The delay-free characteristics of the Smith Predictor is available only when both the process and it's model are exactly matched. So it does not used widely in practical industrial processes. In this paper, using the 2nd-order plus dead time model in place of the plant model of the Smith Predictor, the proposed controller shows the improved performance in case of the very long time delay. And the range of integral constant of the PI controller is also proposed.