• Journal of the Korean Society of Food Culture
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• v.15 no.4
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• pp.307-312
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• 2000

The purpose of this study is to understand the Chinese noodles(mian tiao). Wheat seems to have been cultivated 3-4,000 years before according to the archaeological evidences from the neolithic sites. The five grains(rice, millet, beans, barley, barnyard millet) already appeared in the period prior to Chin dynasty and were used as whole grain, but it was not until Chun Chu Zhan Guo period that the introduction of the flouring method stimulated the cultivation of wheat. In Chin-Han period, when water power and animal force were put into usage to facilitate the mass production of wheat flour, 'Bing', a designation for all the food made of wheat first appeared in the literature, and it was this 'Bing' that had later developed into noodles. In Wei Chin Nan Bei Chao period, roasted 'Bing', namely 'Kao-Bing' made its first appearance, and in Tang period, various noodles were created with the increase of restaurants specialized in noodles. In Song dynasty, 'La-Mian', the noodles stretched and beat from noodle dough, was first introduced, and in Yuan period, invention of drying method made the appearance of dried noodles, 'Gua-Mian', possible, which was good for easy and long preservation. Qing dynasty developed the noodles with a variety of assorted ingredients. The Chinese noodles are classified by various standards such as main ingredients, cooking methods, kinds of sauce, secondary ingredients, shape, eating method, flavor, and look.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.195-204
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• 2014

This paper presents the depth evaluation for object detection by automated assembling robots. Pattern distortion analysis from a structured light system identifies an object with the greatest depth from its background. An automated assembling robot should prior select and pick an object with the greatest depth to reduce the physical harm during the picking action of the robot arm. Object detection is then combined with a depth evaluation to provide contour, showing the edges of an object with the greatest depth. The contour provides shape information to an automated assembling robot, which equips the laser based proxy sensor, for picking up and placing an object in the intended place. The depth evaluation process using structured light for an automated electronics assembling robot is accelerated for an image frame to be used for computation using the simplest experimental set, which consists of a single camera and projector. The experiments for the depth evaluation process required 31 ms to 32 ms, which were optimized for the robot vision system that equips a 30-frames-per-second camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.60-65
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• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.437-444
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• 2015

In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter ${\gamma}$ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher ${\gamma}$ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low ${\gamma}$ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by $10{\times}$.

• Journal of ILASS-Korea
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• v.11 no.2
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• pp.89-97
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• 2006

An inviscid axisymmetric model capable of predicting droplet bouncing and the detailed pre-impact motion, influenced by the ambient pressure, has been developed using boundary element method (BEM). Because most droplet impact simulations of previous studies assumed that a droplet was already in contact with the impacting substrate at the simulation start, the previous simulations could not accurately describe the effect of the gas compressed between a failing droplet and the impacting substrate. To properly account for the surrounding gas effect, an effect is made to release a droplet from a certain height. High gas pressures are computationally observed in the region between the droplet and the impact surface at instances just prior to impact. The current simulation shows that the droplet retains its spherical shape when the surface tension energy is dominant over the dissipative energy. When increasing the Weber number, the droplet surface structure is highly deformed due to the appearance of the capillary waves and, consequently, a pyramidal surface structure is formed; this phenomenon was verified with our experiment. Parametric studies using our model include the pre-impact behavior which varies as a function of the Weber number and the surrounding gas pressure.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.7
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• pp.806-813
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• 2016

To alleviate the vibration problem or to satisfy the required criteria for manifesting the guaranteed performance of precise equipment, various vibration isolation materials or apparatus, such as viscoelastic material, air and coil spring, have been developed and applied. Among them, a wire mesh material is regarded as one of the good candidate for reducing the vibration in terms of moderate material price, easy shape machining and long life cycle without the property deterioration induced by the aging or environmental effects. In this paper, prior to wire mesh isolator design, the static and dynamic elastic modulus of wire mesh materials are extracted from the experiment by the simple shaped cylindrical specimens and their characteristics for applying to vibration isolator design are examined. The simple shaped specimens were made as considering the design parameters of a wire mesh mount; i.e. the density, wire diameter and wire mesh slope, and the sensitivity analysis were also performed from a view point of the extracted elastic modulus.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.195-214
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• 2016

Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.