• IE interfaces
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• v.23 no.2
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• pp.139-146
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• 2010

In this paper, we present the study of a real passenger transportation system. Passenger transportation problem aims to transport passengers from bus stops to their destinations by a fleet of vehicles while satisfying various constraints such as vehicle capacity, maximum allowable riding time in a bus, and time windows at destinations. Our problem also has special issues such as mixed loading, consideration of afternoon problem together with morning problem, and transferring passengers between vehicles. Our solution approach consists of three serial procedures: bus route generation, bus scheduling, and post optimization. Efficient heuristic algorithms were developed and implemented for the procedures. The proposed solution approach has been successfully applied to several real world problem instances and could reduce about 10% to 15% of buses.

• Structural Engineering and Mechanics
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• v.8 no.1
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• pp.1-18
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• 1999

Procedures are investigated by which nonlinear finite element shell analysis algorithms can be simplified to provide more cost effective approximate analyses of orthogonally-reinforced concrete flat plate structures. Two alternative effective stiffness formulations, and an unbalanced force formulation, are described. These are then implemented into a nonlinear shell analysis algorithm. Nonlinear geometry, three-dimensional layered stress analyses, and other general formulations are bypassed to reduce the computational burden. In application to standard patch test problems, these simplified approximate analysis procedures are shown to provide reasonable accuracy while significantly reducing the computational effort. Corroboration studies using various simple and complex test specimens provide an indication of the relative accuracy of the constitutive models utilized. The studies also point to the limitations of the approximate formulations, and identify situations where one should revert back to full nonlinear shell analyses.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.6
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• pp.53-65
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• 2001

Authentication mechanism for W-CMDA IMT-2000 system is developed by 3GPP TSG SA WG3. We simulated the mechanism and algorithms. In this paper, we overview 3GPP authentication procedures and present results of our simulation. We validate the mechanism and parameters transmitted during authentication procedures and we also discuss parameters which are unclear in specification.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.79-84
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• 1992

An integrated design system has as its aim the incoporation of all the design processes, such as, planning, preliminary design, analysis, detailed design (mamber design), evaluation, and drafting into an unified software system. Successful implementation such a system could lead to major improvements in efficiency by eliminating duplication of data and efforts. reducing errors, saving design time, providing management support, and so on. This study presents a methodology for an computer-integrated design system for building structures, synthesizing algorithmic procedures and knowledge based expert systems on the network database. Network database, which was designed to store all information systematically during the design processes, provides centeral communication area between algorithms and expert systems. The conventional procedural codes automate the routine design phases such as structural analysis, whereas knowledge-based expert systems support designer's decisions at the creative design phases such as preliminary design etc. The user interface with interactive and batch modes controls the design phases and manages design information and activates the algorithms and the expert systems. The concept presented in this paper will contribute to the formulation of automated design systems for building structures.

• Korean Journal of Computational Design and Engineering
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• v.12 no.4
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• pp.245-254
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• 2007

This research proposed feature extraction algorithms as an input of STEP Ap214 data, and feature parameterization process to simplify further design change and maintenance. The procedure starts with suppression of blend faces of an input solid model to generate its simplified model, where both constant and variable-radius blends are considered. Most existing cell decomposition algorithms utilize concave edges, and they usually require complex procedures and computing time in recomposing the cells. The proposed algorithm using reference features, however, was found to be more efficient through testing with a few sample cases. In addition, the algorithm is able to recognize depression features, which is another strong point compared to the existing cell decomposition approaches. The proposed algorithm was implemented on a commercial CAD system and tested with selected industrial product models, along with parameterization of recognized features for further design change.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.79-85
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• 2017

In this paper, several optimizations are proposed to enhance the quality of lane detection algorithms in automotive applications. Considering the diagonal directions of lanes, the proposed limited Hough transform newly introduces image-splitting and angle-limiting schemes that relax the number of possible angles at the line voting process. In addition, unnecessary edges along the horizontal and vertical directions are pre-defined and removed during the edge detection procedures, increasing the detecting accuracy remarkably. Simulation results shows that the proposed lane recognition algorithm achieves an accuracy of more than 90% and a computing speed of 92 frame/sec, which are superior to the results from the previous algorithms.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.834-836
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• 2005

This paper describes algorithms for enhancing the reliability of measurements to verify the performance of energy efficiency programs with an simple method. Fundamentally, measurements contain erroneous data because of the various causes. and so proper procedures or schemes are prepared before the performance is evaluated. In this paper, we propose an approach for detecting and correcting an adulterate data, such as missing and bad data. Erroneous data are detected or corrected by pre-described measuring conditions, ensemble average, and standard deviation of measurements at measuring time. The proposed algorithms are tested by field test measurements. From case studies we drew the promising results.