• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.2
• /
• pp.127-137
• /
• 2009

Hypoid gears are widely used in rear drive and 4WD vehicle axles. Investigation of their sensitivity to deflections is one of the most important aspects of their design and optimization procedures. The deflection test is performed in the actual gear mounting using completely processed gear. This test should cover the full operating range of gear loads from no load to peak load. Under peak load the contact pattern should extend to the tooth boundaries without showing a concentration of the contact pattern at any point on the tooth surface. Transmission error is tested on an axle assembly triaxial real car load condition.

• Smart Structures and Systems
• /
• v.23 no.2
• /
• pp.123-139
• /
• 2019

Impact forces induced by external object collisions can cause serious damages to civil engineering structures. While accurate and prompt identification of such impact forces is a critical task in structural health monitoring, it is not readily feasible for civil structures because the force measurement is extremely challenging and the force location is unpredictable for full-scale field structures. This study proposes a novel approach for identification of impact force including its location and time history using a small number of multi-metric observations. The method combines an augmented Kalman filter (AKF) and Genetic algorithm for accurate identification of impact force. The location of impact force is statistically determined in the way to minimize the AKF response estimate error at measured locations and then time history of the impact force is accurately constructed by optimizing the error co-variances of AKF using Genetic algorithm. The efficacy of proposed approach is numerically demonstrated using a truss and a plate model considering the presence of modelling error and measurement noises.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.485-499
• /
• 2021

Although non-binary low-density parity-check (NB-LDPC) codes have better error-correction capability than that of binary LDPC codes, their decoding complexity is significantly higher. Therefore, it is crucial to reduce the decoding complexity of NB-LDPC while maintaining their error-correction capability to adopt them for various applications. The extended min-sum (EMS) algorithm is widely used for decoding NB-LDPC codes, and it reduces the complexity of check node (CN) operations via message truncation. Herein, we propose a low-cost CN processing method to reduce the complexity of CN operations, which take most of the decoding time. Unlike existing studies on low complexity CN operations, the proposed method employs quick selection algorithm, thereby reducing the hardware complexity and CN operation time. The experimental results show that the proposed selection-based CN operation is more than three times faster and achieves better error-correction performance than the conventional EMS algorithm.

• ETRI Journal
• /
• v.46 no.3
• /
• pp.461-472
• /
• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.19 no.4 s.74
• /
• pp.389-398
• /
• 2006

Structural optimization design has been developed with finite element analysis using effective and fast computational technology. Especially topology optimization design has been recently often used since it yields an optimal topology as well as an optimal shape under satisfied constraints. In general in finite element analysis, it is assumed that the structural material properties such as Young's modulus and Poisson's ratio and the variable of applied loading are fixed with obvious values in structure. However practically these values may take uncertainties because of environmental effect or manufactural error of structures. Therefore static or dynamic analysis of the structures may make an error, then finally it may have an influence on qualify of optimal design. In this study, the topology optimization design of structure is carried out using so called the interval finite element method, and the analysis method Is proposed. The results are also validated by comparing with conventional topology optimization results of density distribution method and finite element analysis results. The present method can be used to predict the optimal topology of linear elastostatic structures with respect to structural uncertainty of behavior.

• Korean Journal of Construction Engineering and Management
• /
• v.14 no.5
• /
• pp.35-43
• /
• 2013

Nowadays the widely used media in architecture include visualizations, animations and three-dimensional models. 3D digital methods using active CAM(Computer Aided Manufacturing) and CNC(Computerized Numerical Control) imaging have been developed for accurate shape and 3D measurements in freeform buildings. In contrast to a conventional building using auto CAD system and others, the proposed digital optimization method is based on a combination of 3D numerical data and parametric 3D model for design and construction. The objective of this paper is therefore to present digital optimization process for constructability of freeform building. The method can be useful in the effective implementation of an error-proofing process of freeform building during design and construction phase. 3D digital coordinate data can be used effectively to identify correct size of structural and finish members and installation location of each members in construction field. In addition, architects, engineers and contractors can evaluate design, materials, constructability and identify error-proofing opportunities. Other project participants can also include representatives from all levels of management, departments as well as workers and key subcontractors' personnel, if necessary. The 3D digital optimization process is therefore appropriate to serious variations in freeform shape. For future study, the developed digital optimization method is necessary to be carried out to verify the robustness and accuracy for constructability in construction field.

• Journal of Korea Multimedia Society
• /
• v.6 no.1
• /
• pp.136-147
• /
• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.6
• /
• pp.1-8
• /
• 2007

In this paper, research on joint optimization of the image spatial registration and the exposure compensation is conducted. The exposure compensation is performed in a frame work of the intensity compensation based on the polynomial approximation of the relationship between images. This compensation is jointly combined with the registration problem employing the Gauss-Newton nonlinear optimization method. In this paper, to perform for a simple and stable optimization, the block-coordinate method is combined with the Gauss-Newton optimization and extensively compared with the traditional approaches. Furthermore, regression analysis is considered in the compensation part for a better stable performance. By combining the block-coordinate method with the Gauss-Newton optimization, we can obtain a compatible performance reducing the computational complexity and stabilizing the performance. In the numerical result for a particular image, we obtain a satisfactory result for 10 repeats of the iteration, which implies a 50% reduction of the computational complexity. The error is also further reduced by 1.5dB compared to the ordinary method.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.558-565
• /
• 2024

This paper presents the development of an optimization framework for road slope design. Recognizing the limitations of current manual stability analysis methods, which are time-consuming, are error-prone, and suffer from data mismatches, this study proposes a systematic approach to improve efficiency, reduce costs, and ensure the safety of infrastructure projects. The framework addresses the subjectivity inherent in engineers' decision-making process by formalizing decision variables, constraints, and objective functions to minimize costs while ensuring safety and environmental considerations. The necessity of this framework is embodied by the review of existing literature, which reveals a trend toward specialization within sub-disciplines of road design; however, a gap remains in addressing the complexities of road slope design through an integrated optimization approach. A genetic algorithm (GA) is employed as a fundamental optimization tool due to its well-established mechanisms of selection, crossover, and mutation, which are suitable for evolving road slope designs toward optimal solutions. An automated batch analysis process supports the GA, demonstrating the potential of the proposed framework. Although the framework focuses on the design optimization of single cross-section road slopes, the implications extend to broader applications in civil engineering practices. Future research directions include refining the GA, expanding the decision variables, and empirically validating the framework in real-world scenarios. Ultimately, this research lays the groundwork for more comprehensive optimization models that could consider multiple cross-sections and contribute to safer and more cost-effective road slope designs.

• Journal of computational fluids engineering
• /
• v.17 no.4
• /
• pp.32-40
• /
• 2012

Efficient Global Optimization (EGO) method is a global optimization technique which can select the next sample point automatically by infill sampling criteria (ISC) and search for the global minimum with less samples than what the conventional global optimization method needs. ISC function consists of the predictor and mean square error (MSE) provided from the kriging model which is a stochastic metamodel. Also the constrained EGO method can minimize the objective function dealing with the constraints under EGO concept. In this study the constrained EGO method applied to the RAE2822 airfoil shape design formulated with the constraint. But the noisy CFD data caused the kriging model to fail to depict the true function. The distorted kriging model would make the EGO deviate from the correct search. This distortion of kriging model can be handled with the interpolation(p=free) kriging model. With the interpolation(p=free) kriging model, however, the search of EGO solution was stalled in the narrow feasible region without the chance to update the objective and constraint functions. Then the accuracy of EGO solution was not good enough. So the three-step search method was proposed to obtain the accurate global minimum as well as prevent from the distortion of kriging model for the noisy constrained CFD problem.