• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.17-24
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• 2008

Rail clamps are mechanical components installed to fix the container crane to its lower members against wind blast or slip. According to rail clamps should be designed to survive harsh wind loading conditions. In this study, a jaw structure, which is a part of a wedge-typed rail clamp, is optimized with respect to its strength under a severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw is included in the category of shape optimization. Conventional structural optimization methods have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using Kriging interpolation method is introduced to replace the true response by an approximate one. This research presents the shape optimization of a jaw using iterative Kriging interpolation models and a simulated annealing algorithm. The new Kriging models are iteratively constructed by refining the former Kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.4
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• pp.9-14
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• 1985

In this research mass of a plane strain two dimensional elastic concrete dam under gravitational and hydrostatic loads is minimized, through shape optimization of the dam cross section. Cross sectional area of the dam is taken as cost function of the optimization problem while constraints on the principal stress distribution and dam thickness are imposed. Shape of the boundary of the model is chosen as design variable. Variational formulation of the optimization problem, the material derivative idea of continuum mechanics, and an adjoint variable method are employed for the shape design sensitivity calculation. Then the gradient projection algorithm is utilized to obtain an optimum design iteratively. Research results fully demonstrate that the theory and procedure adopted are quite efficient and can be applicable to a wide class of practical elastic structural design problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.333-340
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• 2005

Feedrate is one of the factors that have the significant effects on the productivity, qualify and tool life in the cutting mechanism as well as cutting velocity, depth of cut and width of cut. In this study, in order to realize the high-efficient machining, a new feedrate optimization method is proposed based on the concept that the optimum feedrate can be derived from the allowable cutting power since the cutting power can be predicted from the cutting parameters as feedrate, depth of cut, width of cut, chip thickness, engagement angle, rake angle, specific cutting force and so on. Tool paths are extracted from the original NC program via the reverse post-processing process and converted into the infinitesimal tool paths via the interpolation process. And the novel NC program is reconstructed by optimizing the feedrate of infinitesimal tool paths. Especially, the fast feedrate optimization is realized by using the Boolean operation based on the Goldfeather CSG rendering algorithm, and the simulation results reveal the availability of the proposed optimization method dramatically reducing the cutting time and/or the optimization time. As a result, the proposed optimization method will go far toward improving the productivity and qualify.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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• pp.280-285
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• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.

• Computational Structural Engineering
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• v.1 no.1
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• pp.87-94
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• 1988

The linerization method as one of the recursive quadratic programming method is applied for the optimal design of a large-scale structure supported by Pshenichny's proof of global convergence of the algorithm and convergence rate estimates. The linearization method transforms all constants of the design problem into an equivalent linearized constraint and employs the active-set strategy. This results in substantial computational savings by reducing the number of sate and adjoint to be solved at every design iteration. The illustrative example of plates with beams supported by columns is the typical one of a large-scale structure to give successful optimum solutions with satisfactory convergence criteria. Hopefully, the method may be applicable to all classes of optimization problems.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.93-96
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• 2001

In this paper, we present a vision algorithm and method for input image improvement and preprocessing of dented and raised characters on the sidewall of tires. we define optical condition between reflect coefficient and reflectance by the physical vector calculate. On the contrary this work will recognize the engraved characters using the computer vision technique. Tire input images have all most same grey levels between the characters and backgrounds. The reflectance is little from a tire surface. therefore, it's very difficult segment the characters from the background. Moreover, one side of the character string is raised and the other is dented. So, the captured images are varied with the angle of camera and illumination. For optimum input images, the angle between camera and illumination was found out to be with in 90。 .In addition, We used complex filtering with low-pass and high-pass band filters to improve input images, for clear input images. Finally we define equation reflect coefficient and reflectance. By doing this, we obtained good images of tires for pattern recognition.

• Journal of the Korean Institute of Telematics and Electronics
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• v.15 no.6
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• pp.8-22
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• 1978

Recently developed Computed Topography (CT) reconstruction algorithms are reviewed in a more generalized sense and a few reconstruction examples are given for illustration. The construction of an image function from the physically measured projections of some object is Discussed with reference to the least squares optimum filters, originally derived to enhance the signal-to-noise ratio in communications theory. The computerifed image processing associated with topography is generalized so as to include 3 distinct parts: the construction of an image from the projection, the restoration of a blurred, noisy image, degraded by a known space-invariant impulse response, and the further enhancement of the image, e.g. by edge sharpening. In conjunction with given versions of the popular convolution algorithm, n6t 19 be confused with filtering by a 2-diminsional convolution, we consider the conditions under which a concurrent construction, restoration, and enhancement are possible. Extensive bibliographical limits are given in the references.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1608-1618
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• 2016

In this paper we introduce a graph theory approach to solve the frequency assignment problem(FAP) for military communication network. Prior algorithms are implemented adaptively to the problem, and enhanced algorithms are proposed to show that their results approximately approached the optimum performance. We also proposed polarization assignment algorithms to enhance the FAP performances.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1274-1285
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• 2015

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.