• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.135-141
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• 2001

An experimental study to find the effect of processing parameters for self-healing microcapules is performed. These microcapsules can be applied to accomplish the healing of delamination damage in woven E-glass/epoxy composites. This paper introduces the self-healing concept and presents a method for solving the microcapsule size and shape. Additionally, processing parameters are varied during the formation of microcapsules and these capsules are observed through optical microscope. To obtain thermogravimetric(TG) curve for the manufactured microcapsules, TGA tests are executed. From these results, the best processing conditions for the formation of capsules are found as follows: (1) temperature of solution $ 50^{\circ}C$, (2) potential of hytdrogen(pH) 3.5ppm, and (3) agitation 500~600rpm.

• Journal of the Korean Magnetics Society
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• v.5 no.5
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• pp.610-613
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• 1995

The effect of processing parameters such as milling, additives and sintering atmoshpere on the magnetic properties of Mn-Zn ferrite was investigated. The experiment was followed by general ceramic fabrication process and added additives were $CaCO_{3}$, $SiO_{2}$, $V_{2}O_{5}$, $ZrO_{2}$, and $Nb_{2}O_{5}$. The effects of additives could be divided into three categories which were formation ofliquid phase, substitution in lattice and inducing stress. Core loss smong the magnetic properties was dependent mainly on the additives and also correlated with processing parameters. As a result, an optimum condition of preparing process for a high quality Mn-Zn ferrite was suggested by controlling the correlation of each processing parameters.

• Journal of the Korean Society of Visualization
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• v.13 no.3
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• pp.15-19
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• 2015

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.963-969
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• 2008

The wear particles are relative to the failure and the life of machine elements directly. But it is not laid down to calculate shape parameters of wear particle. To analyze a variation of distributed characteristics of wear particles on moving conditions, its shape parameters such as diameter and roundness were calculated the quantitative values by digital image processing, and had to be defined the effective method of using those data. Up to the present, the shape parameters have been used simply into the average values. But these values are not effective to analyze a variation of distributed characteristics of occurred wear particles on moving conditions. In this study, the relative histograms of shape parameters of wear particles were used for the purpose of analyzing the distribution of wear particles in various conditions. The results showed that the relative histogram of shape parameters can be effectively represented to study a wear mechanism.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.201-204
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• 2000

This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

• Food Engineering Progress
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• v.21 no.4
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• pp.303-311
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• 2017

To automate cooking processes, quantitative descriptions are needed on how quality parameters, such as texture change during heating. Understanding mechanical property changes in foods during thermal treatment due to changes in chemical composition or physical structure is important in the context of engineering models and in precise control of quality in general. Texture degradation of food materials has been studied widely and softening kinetic parameters have been reported in many studies. For a better understanding of kinetic parameters, applied kinetic models were investigated, then rate constants at $100^{\circ}C$ and activation energy from previous kinetic studies were compared. The food materials are hardly classified into similar softening kinetics. The range of parameters is wide regardless of food types due to the complexity of food material, different testing methods, sample size, and geometry. Kinetic parameters are essential for optimal process design. For broad and reliable applications, kinetic parameters should be generated by a more consistent manner so that those of foods could be compared or grouped.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.8
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• pp.721-728
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• 2014

In this paper, it is proposed a method of optimizing path parameters for large-area laser processing. On-the-fly system is necessary for large-area laser processing of uniform quality. It is developed a MOTF(Marking On-The-Fly) board for synchronizing the stage and scanner. And it is introduced the change of the error due to the change of parameters and algorithm for large-area laser processing. This algorithm automatically generates stage path and a velocity profile using acceleration and deceleration parameters. Since this method doesn't use a G-code, even if without expert knowledge, it has an advantage that can be accessed easily. Angle of one of the square of $350{\times}350mm$ was changed from $50^{\circ}$ to $80^{\circ}$ and analyzed the error corresponding to the value of Ta. It is calculated the value of Ta of the best with a precision of 20um through measurement of accuracy according to the Ta of each angle near the edge.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.10
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• pp.1508-1515
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.