• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.73-80
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• 2003

The Java programming language is designed for developing effective applications in a heterogeneous network environment. Major problem in Java is its performance. many attractive features of Java make the development of software easy, but also make it expensive to support ; applications written in Java are often much slower than their counterparts written in C or C++. To use Java`s attractive features without the performance penalty, sophisticated optimizations and runtime systems are required. Optimising Java bytecode has several advantages. First, the bytecode is independent of any compiler that is used to generate it. Second, the bytecode optimization can be performed as a pre=pass to Just-In-Time(JIT) compilation. Many attractive features of Java make the development of software easy, but also make it expensive to support. The goal of this work is to develop automatic construction of code optimizer for Java bytecode. We`ve designed and implemented a Bytecode Optimizer that performs the peephole optimization, bytecode-specific optimization, and method-inlining techniques. Using the Classfile optimizer, we see up to 9% improvement in speed and about 20% size reduction in Java class files, when compared to average code using the interpreter alone.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.749-755
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• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.765-776
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• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.905-912
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• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Earthquakes and Structures
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• v.8 no.6
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• pp.1481-1497
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• 2015

Vertical earthquake ground motion may magnify vertical dynamic responses of structures, and thus cause serious damage to bridges. As main support structures, piers and bearings play an important role in vertical seismic response analysis of girder bridges. In this study, the pier and bearing are simplified as a vertical series spring system without mass. Then, based on the assumption of small displacement, the equation of motion governing the simply-supported straight girder bridge under vertical ground motion is established including effects of vertical deformation of support structures. Considering boundary conditions, the differential quadrature method (DQM) is applied to discretize the above equation of motion into a MDOF (multi-degree-of-freedom) system. Then seismic responses of this MDOF system are calculated by a step-by-step integration method. Effects of support structures on vertical dynamic responses of girder bridges are studied under different vertical strong earthquake motions. Results indicate that support structures may remarkably increase or decrease vertical seismic responses of girder bridges. So it is of great importance to consider effects of support structures in structural seismic design of girder bridges in near-fault region. Finally, optimization of support structures to resist vertical strong earthquake motions is discussed.

• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.51-60
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• 2016

The arrangement design of a submarine has been performed based on data of parent ships and experts' experiences. To support such a task, it is necessary to accumulate and use systematically the data, and to optimize the task. The expert system for the first issue and the optimization method for the latter issue can be used. At this time, a suitable data structure to share the data on the arrangement design of the submarine should be used. In this study, the data structure named an arrangement template model (ATM) is proposed. To check the applicability of the ATM, a prototype program which consists of the expert system and the optimization method is developed. Finally, the developed program is applied to a small submarine of US Navy. As a result, it is confirmed that the ATM can be used to share the data between the expert system and the optimization method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1822-1830
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• 2002

The paper describes the development of a decomposition based multidisciplinary design optimization (MDO) method that coordinates each of disciplinary subspace optimization (DSO). A multidisciplinary design system considered in the present study is decomposed into a number of subspaces based on their own design objective and constraints associated with engineering discipline. The coupled relations among subspaces are identified by interdisciplinary design variables. Each of subsystem level optimization, that is DSO would be performed in parallel, and the system level coordination is determined by the first order optimal sensitivities of subspace objective functions with respect to interdisciplinary design variables. The central of the present work resides on the formulation of system level coordination strategy and its capability in decomposition based MDO. A fluid-structure coupled design problem is explored as a test-bed to support the proposed MDO method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1199-1208
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• 2005

Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum. In some recent studies, it is proposed to increase the fidelity of approximate models by applying the sequential sampling. However, because the accuracy and efficiency of an approximate model is directly connected with the design area and the termination criteria are not clear, sequential sampling method has the disadvantages that could support an unreasonable approximate optimum. In this study, the SAO is executed by using trust region, Kriging model and Optimal Latin Hypercube design (OLHD). Trust region is used to guarantee the convergence and Kriging model and OLHD are suitable for computer experiment. finally, this SAO method is applied to various optimization problems of highly nonlinear mathematical functions. As a result, each approximate optimum is acquired and the accuracy and efficiency of this method is verified by comparing with the result by established method.