• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.511-518
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• 2009

The surveillance robot has been an important component in the field of service robot industry. In the surveillance robot technology, one of the most important technology is to identify a person. In this paper, we propose a gait recognition method based on contourlet and fuzzy LDA (Linear Discriminant Analysis) for surveillance robots. After decomposing a gait image into directional subband images by contourlet, features are obtained in each subband by the fuzzy LDA. The final gait recognition is performed by a fusion technique that effectively combines similarities calculated respectively in each local subband. To show the effectiveness of the proposed algorithm, various experiments are performed for CBNU and NLPR DB datasets. From these, we obtained better classification rates in comparison with the result produced by previous methods.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.41-51
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• 2008

As the local bus service diagram after the bus route reform is studied by variable analysis based on traffic card data and income adjustment data, the characteristic of the local bus system is revealed in Busan Metropolitan city. The relationship between traveling length and traveling time is influenced by traveling velocity. In order to keep a headway within 10 minutes, bus service number per minute should be over 0.1013 vehicles. The traveling time of afternoon is generally longer than that of forenoon. Compared with the bus used by a lot people, the deviation of that used by a few people is larger in the all cases of length, headway, time and velocity. According to the analysis of the relationship among card trip number, average income and transfer rate, the relationship between card trip number and average income is expressed as linear function in the general bus and as exponential function in the high-grade & rapid bus. The 1% increase of transfer rate is equal to 6.3 trip/vehicle/day decrease and 4.9 trip/vehicle/day decrease in two bus types respectively. The four effective variables are defined by the discriminant analysis between the profitable routes and the unprofitable; According to discriminant size, bus service number per km, bus via suburb, subway meeting number, bus via university. In order to increase the income when the minibus will be included among public transit transfer system in 2008, it should be necessary to settle the bus network and revitalize the public transit better. In order to decrease the cost, it should be necessary to reorganize the hierarchy between the local bus and the minibus better.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• Food Science of Animal Resources
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• v.41 no.6
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• pp.936-949
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• 2021

This study discriminated fatty acid profile and flavor characteristics of Beijing You Chicken (BYC) as a precious local breed and Dwarf Beijing You Chicken (DBYC) eggs. Fatty acid profile and flavor characteristics were analyzed to identify differences between BYC and DBYC eggs. Four classification algorithms were used to build classification models. Arachidic acid, oleic acid (OA), eicosatrienoic acid, docosapentaenoic acid (DPA), hexadecenoic acid, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), unsaturated fatty acids (UFA) and 35 volatile compounds had significant differences in fatty acids and volatile compounds by gas chromatography-mass spectrometry (GC-MS) (p<0.05). For fatty acid data, k-nearest neighbor (KNN) and support vector machine (SVM) got 91.7% classification accuracy. SPME-GC-MS data failed in classification models. For electronic nose data, classification accuracy of KNN, linear discriminant analysis (LDA), SVM and decision tree was all 100%. The overall results indicated that BYC and DBYC eggs could be discriminated based on electronic nose with suitable classification algorithms. This research compared the differentiation of the fatty acid profile and volatile compounds of various egg yolks. The results could be applied to evaluate egg nutrition and distinguish avian eggs.