• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.945-950
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• 1990

An automatic visual classification system is introduced which provides for measuring the length and diameter of coilform cores and dividing them into 5 different classed in terms of how far their length be from the desired length. This task is fully automated by controlling two STEP motors and by using image processing techniques. The classification procedure is broken into three logical parts, First, cores in the form of randomly stacked bundle are lined up one by one so as to be well captured by a camera. The second part involves capturing core image. Then, it enters the measuring process. Finally, this machine would retain all the information relating to the length. According to the final result, cores are sent to the corresponding bin. This considerably simplifies the selecting task and facilitates a greatly improved reliablity in precision. The average classifying capability is about 2 pieces per second.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.302-307
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• 1990

An automatic visual classification system is introduced which provides for measuring the length and diameter of coilform cores and dividing them into 5 different classes in terms of how far their length be from the desired length. This task is fully automated by controlling two STEP motors and by using image processing techniques. The classification procedure is broken into three logical parts. Fist, cores in the form of randomly stacked bundle are lined up one by one so as to be well captured by a cameras. The second part involves capturing core image. Then, it enters the measuring process. Finally, this machine would retain all tire information relating to the length. According to the final result, cores are sent to the corresponding bin. This considerably simplifies the selecting task and facilitates a greatly improved reliability in precision. The average classifying capability about 2 pieces per second.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• Journal of Korean Medical classics
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• v.28 no.1
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• pp.35-53
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• 2015

Objectives : In order to find how reference books of Yi Xue Ru Men reflect the classification and criterion for low back pain(LBP). Methods : From reference books of Yi Xue Ru Men, select the texts on classification and criterion for LBP. Results : According to the causes of LBP, Chao Yuan Fang(巢元方) in Sui Dynasty assorted to 5 types of LBP at the very first. Chen Wu Ze(陳無擇) in Song Dynasty made 7 divisions by external, internal, and non-external, non-internal causes. According to the pulse of LBP, Yan Yong He(嚴用和) first categorized 4 groups, Zhu Zhen Heng(朱震亨) added another 4 groups. Aside from this standard, Zhu(朱震亨) adopted the cause standard. Depending on Yunqi(運氣), Lou Ying(樓英) classified 5 types. But his classification had been not adopted by any TCM books. According to symptom of 6 varieties(六變), Zhang Jie Bin(張介賓) assorted external(表), internal(裏), deficiency(虛), sufficiency(實), cold(寒) and heat(熱), add 2 groups besides them. But his categorization did not reflect Yi Xue Ru Men. Li Chan(李梴), the author of this book chose causes and pulse classification standards that Zhu Zhen Heng had adopt. Conclusions : In the side of classification and criterion for LBP, Li Chan first divided 2 group, external and internal injury. After it he subdivided both groups to 10 subgroup. His classification is similar to Chen(陳無擇)'s, but actually followed the classification for external and internal injury that was invented by Li Dong Yuan(李東垣).

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.521-528
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• 2007

The overall objective of this research was to investigate various combination of segmentation parameters and to improve classification accuracy of object-oriented classification. This research presents a method for evaluation of segmentation parameters by calculating Moran's I and Intrasegment Variance. This research used Landsat-7/ETM image of $11{\times}14$ Km developed area in Ansung, Korea. Segmented images are generated by 75 combinations of parameter. Selecting 7 combinations of high, middle and low grade expected classification accuracy was based on calculated Moran's I and Intrasegment Variance. Selected segmentation images are classified 4 classes and analyzed classification accuracy according to method of objected-oriented classification. The research result proved that classification accuracy is related to segmentation parameters. The case of high grade of expected classification accuracy showed more than 85% overall accuracy. On the other hand, low ado showed around 50% overall accuracy.

• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.113-119
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• 2007

This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

• Journal of Smart Tourism
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• v.1 no.1
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• pp.53-64
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• 2021

Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.

• Polymer(Korea)
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• v.39 no.1
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• pp.33-39
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• 2015

Olmesartan affiliated to biopharmaceutics classification system class 2 is a poorly water soluble drug. For this reason, olmesartan showed a low bioavailability and a lot of difficulties in the process of designing the pharmaceutical formulation. We prepared the solid dispersions of olmesartan. We confirmed the dissolution rate of drug which was prepared by manufacturing. The pharmaceutical formulation of solid dispersions was designed by using PVP as water soluble polymer. We analyzed morphological feature of solid dispersion by employing a scanning electron microscope. Then, the crystalline property of solid dispersion was confirmed through X-ray diffraction and differential scanning calorimeter. Also, the chemical change of solid dispersion was confirmed by the Fourier transform infrared spectroscopy. In vitro dissolution test was used to analyze the dissolution rate of solid dispersion. The prepared solid dissolution olmesartan confirmed the dissolution rate in the pH 1.2. It was compared with olmetec and improved dissolution rate through solid dispersion.