• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.91-101
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• 2016

The automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.867-880
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• 2007

We derive a modified version of cubic convolution interpolation for the enlargement or reduction of digital images by arbitrary scaling factors. The proposed scaling scheme is used to resize various format pictures in the transcoding system, which transforms the bitstream compressed at a bit rate, such as the HD bitstream, into another bit rate stream. In many applications such as the transcoder, the resolution conversion is very important for changing the image size while the scaled image maintains high quality. We focus on the modification of the scaler kernel according to the relation between formats of the original and the resized image. In the modification, various formats defined in MPEG standards are considered. We show experimental results that demonstrate the effectiveness of the proposed interpolation method.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.2
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• pp.1-12
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• 1993

The role of product model in CIM environemt(where such heterogenous application programs as CAD, CAE, CAM, Database, and Expert Systems are included) is system integration. Product model manages all the information related to manufacuring activities. This information includes shapes, operation, process, scheduling, quality, and mangement. Product model architecture includes product model kernel, object schema, model manipulation language, and user interface. Objects to be shared are defined using the model manipulation luage and the defintions are saved in the object schema. In this paper, we present the design and implementation of a prototype. In this prototype, application programs for CAPP(Computer Aided Process Planning) are used.

• Korean Journal of Food Science and Technology
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• v.17 no.5
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• pp.399-402
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• 1985

Polished rice was stored for 24 weeks at $28^{\c수분함량이 16.0% after infestation with rice weevil (Sitophilus oryzae), inoculation with a storage mold, or both treatment. The moisture contents of stored rice with and without rice weevil were 16.6%-17.3% and 13.70%-14.1% respeotively. Fungal numbers were $4.3{\times}10^{6}$/g to $7.2{\times}10^{6}$/g with insects while no fungal growth occurred without them. There were considerable differences between rice weevil infested- and non-infested rice in rice kernel hardness, 1000 kernel weight, maximum viscosity on amylogram, reducing sugar content, fat acidity, and n-hexanal content in head space gas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3000-3005
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• 2015

Reliable monitoring and diagnosis of industrial processes is quite important for in terms of quality and safety. The goal of fault diagnosis is to find process variables responsible for causing specific abnormalities of the process. This work presents a classification-based diagnostic scheme based on nonlinear representation of process data. The use of a nonlinear kernel technique is able to reduce the size of the data considered and provides efficient and reliable representation of the measurement data. As a filtering stage a preprocessing is performed to eliminate unwanted parts of the data with enhanced performance. The case study of an industrial batch process has shown that the performance of the scheme outperformed other methods. In addition, the use of a nonlinear representation technique and filtering improved the diagnosis performance in the case study.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• Korean Journal of Remote Sensing
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• v.5 no.1
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• pp.41-56
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• 1989

Improvement of the multichannel sea surface temperature(MCSST) software, which had been developed for the purpose of operating under mainframe computer system, was seeked in order to operate effectively in a mini computer system. CPU time and processing time, which is not a major factor under mainframe computer system, become a critical factor in real time image processing under mini computer system. Due to fixed kernel size(3$\times$4) of the old MCSST software, high spatial resolution characteristics of the original image received from satellites were apparently degraded when images are transformed into a cartesian coordinate system after geometrical distortions of the image due to earth curvature are removed. CPU and processing time were reduced to 0.13 and 0.15~0.22 comparing with the old MCSST's, respectively, by applying disk block I/O and M/T queue I/O method under VAX-11/750 computer. The high resolution quality (1.1km in AVHRR) of the processed image was guaranted using 2$\times$2 kernel size and applying moving window techniques without sacrificing CPU and processing time much.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.295-302
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• 1997

An ultimate purpose of this study was to develop an automatic system for brown rice quality inspection using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor magnifying the input image and optical fiber for oblique lightening. Primarily, geometical and optical features of images were analyzed with paddy and the various brown rice kernel samples such as a sound, cracked, peen-transparent, green-opaque, colored, white-opaque and brokens. Secondary, geometrical and optical parameters significant for identifying each rice kernels were screened by a statistical analysis(STEPWISE and DISCRIM procedure, SAS wer. 6) and an algorithm fur on- line discrimination of the rice kernels in static state were developed, and finally its performance was evaluated. The results are summarized as follows. 1) It was ascertained that the cracked kernels can be detected when e incident angle of the oblique light is less than 2$0^{\circ}C$ but detectivity was significantly affected by the angle between the direction of the oblique light and the longitudinal axis of the rice kernel and also by the location of the embryo with respect to the oblique light. 2) The most significant Parameters which can discriminate brown rice kernels are area, length and R, B and r values among the several geometrical and optical parameters. 3) Discrimination accuracies of the algorithm were ranged from 90% to 96% for a sound, cracked, colored, broken and unhulled, about 81 % for green-transparent and white-opaque and 75 % for green-opaque, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.3
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• pp.249-259
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• 2009

Puroindoline alleles, grain characteristics, milling performances and physical properties of flour of 22 Korean wheat cultivars were evaluated to determine the influence of puroindolines genotypes on grain and flour characteristics and to provide useful information for improving milling and end-use quality in Korean wheat breeding programs. Nine Korean wheat cultivars carried with Pina-D1a/Pinb-D1a, 11 cultivars had Pina-D1a/Pinb-D1b and 2 cultivars were Pina-D1b/Pinb-D1a. Korean wheats carrying with Pina-D1a/Pinb-D1a genotypes showed lower test weight and thousand kernel weight, area and roundness of grain and hardness index in grain characteristics, lower flour yield and higher proportion of break flour in milling performances and lower average particle size of flour, ash and damaged starch content, water retention capacity, yellowness-blueness and higher lightness of flour than wheats with Pina-D1a/Pinb-D1b or Pina-D1b/Pina-D1a genotypes. Pina-D1a/Pinb-D1b genotypes showed lower 1000-kernel weight, grain area, higher average of particle size of flour, higher ash and damaged starch content than Pina-D1b/Pina-D1a genotypes. There was no difference in hardness index of grain, milling performances, flour color between Pina-D1a/Pinb-D1b and Pina-D1b/Pina-D1a genotypes. These results could present the information to improve milling quality and physical properties of flour in Korean wheat breeding programs.