• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.1041-1049
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• 2003

In this paper, a new local two-point approximation method which is based on the exponential intervening variable is proposed. This new algorithm, called the Two-Point Convex Approximation(TPCA), use the function and design sensitivity information from the current and previous design points of the sequential approximate optimization to generate a sequence of convex, separable subproblems. This paper describes the derivation of the parameters associated with the approximation and the numerical solution procedure. In order to show the numerical performance of the proposed method, a sequential approximate optimizer is developed and applied to solve several typical design problems. These optimization results are compared with those of other optimizers. Numerical results obtained from the test examples demonstrate the effectiveness of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1203-1214
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• 2000

In this paper, a failure model for co-cured steel-composite tubular single lap joints has been proposed incorporating the nonlinear mechanical behavior of steel adherends and different failure mode s such as steel adherend failure and composite adherend failure. The characteristics of the co-cured steel-composite tubular single lap joint were investigated with respect to the test temperature, the stacking sequence of composite adherend, the thickness ratio of steel adherend to composite adherend, and the scarf ratio of steel adherend. Thus, the optimum design method for the co-cured steel-composite tubular single lap joint was suggested.

• Research in Plant Disease
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• v.20 no.2
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• pp.129-131
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• 2014

In February 2014, gray mold rotting symptoms were observed in carrots in cold storage at Gangneung, Gangwon province, Korea. The typical symptom of gray mold rot showed abundant blackish gray mycelia and conidia was observed on the infected root. The pathogen was isolated from infected root and cultured on PDA for further fungal morphological observation and confirming its pathogenicity according to Koch's postulates. Results of morphological data, pathogenicity test and rDNA internal transcribed spacer (ITS 1 and 4) sequence showed that the postharvest gray mold rot of carrot was caused by Botyrtis cinerea. This is the first report of postharvest gray mold rot on carrot in Korea.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.95-98
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• 2005

Many utterance verification (UV) algorithms have been studied to reject out-of-vocabulary (OOV) in speech recognition systems. Most of conventional confidence measures for UV algorithms are mainly based on log likelihood ratio test, but these measures take much time to evaluate the alternative hypothesis or anti-model likelihood. We propose a novel confidence measure which makes use of a momentary best scored state sequence during Viterbi search. Our approach is more efficient than conventional LRT-based algorithms because it does not need to build anti-model or to calculate the alternative hypothesis. The proposed confidence measure shows better performance in additive noise-corrupted speech as well as clean speech.

• Journal of Industrial Technology
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• v.30 no.B
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• pp.25-32
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• 2010

In this paper stability about lateral soil movement of bridge abutment constructed on the soft ground, reinforced with the sand compaction pile (SCP) and the preconsolidaton methods, was evaluated by using the centrifuge testing facility which stress conditions in field could be reconstructed in the laboratory. The layouts of model such as ground condition, sand compaction piles and abutment was determined on the basis of similitude law with the reduced scale of 1/200. Construction sequences of installing SCP, preparing reclaimed ground, preconsolidating ground and building the piled bridge abutment were reconstructed during centrifuge modelling and measurements of movement were followed in each sequence. From analyzing the results of measuring movements of the model abutment and the ground, measured lateral movement of model abutment was found to be within the allowable value so that stability of abutment against lateral sliding was secured.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.569-572
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• 1992

This paper describes a new method of pseudorandom testing of a digital circuit by use of correlation method and a neural network. The authors have recently proposed a new method of fault diagnosis of logical circuit by applying a pseudorandom M-sequence to the circuit under test, calculating the crosscorrelation function between the input and the output, and comparing the crosscorrelation functions with the references. This method, called MSEC method, is further extended by using a neural network in order to not only detect the existence of faults but also find the place or location of the faults. An experiment by using a simple digital circuit shows enough applicability of this method to industrial testing of circuit board.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.145-148
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• 1999

It is known that Volterra kernel models can represent a wide variety of nonlinear chemical processes. Also, it is necessary for Volterra model identification to excite the process to be identified with a signal having wide range of frequency spectrum and high enough amplitude of input signals. Kashiwagi[4 ∼ 7] has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. However, in practice, since it is not always possible to apply such input sequences to the actual chemical plants. Even when we can apply such a pseudorandom signal to the process, it takes much time to obtain higher order Volterra kernels. Considering these problems, the authors propose here a new method for practical identification of Volterra kernels by use of approximate open differential equation (ODE) model and simple plant test. Simulation results are shown for verifying the usefulness of our method of identification of nonlinear chemical processes.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.615-618
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• 1999

In this paper, a object detection and tracking algorithm is presented which exhibits robust properties for image sequences with complex background. The proposed algorithm is composed of three parts: moving object detection, object tracking, and motion analysis. The moving object detection algorithm is implemented using a temporal median background method which is suitable for real-time applications. In the motion analysis, we propose a new technique for removing a temporal clutter, such as a swaying plant or a light reflection of a background object. In addition, we design a multiple vehicle tracking system based on Kalman filtering. Computer simulation of the proposed scheme shows its robustness for MPEG-7 test image sequences.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.40-45
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• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Computers and Concrete
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• v.25 no.5
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• pp.433-445
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• 2020

A method is proposed in this paper to estimate the workability of self-compacting concrete (SCC) in different mixing conditions with different mixers and mixing volumes by recording the mixing process based on deep learning (DL). The SCC mixing videos were transformed into a series of image sequences to fit the DL model to predict the SF and VF values of SCC, with four groups in total and approximately thirty thousand image sequence samples. The workability of three groups SCC whose mixing conditions were learned by the DL model, was estimated. One additionally collected group of the SCC whose mixing condition was not learned, was also predicted. The results indicate that whether the SCC mixing condition is included in the training set and learned by the model, the trained model can estimate SCC with different workability effectively at the same time. Our goal to estimate SCC workability in different mixing conditions is achieved.