• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.301-304
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• 2008

Since longer wavelength microwave radiation can penetrate clouds, satellite passive microwave sensors can observe sea ice of the entire polar region on a daily basis. Thus, it is becoming popular to derive sea ice motion vectors from a pair of satellite passive microwave sensor images observed at one or few day interval. Usually, the accuracies of derived vectors are validated by comparing with the position data of drifting buoys. However, the number of buoys for validation is always quite limited compared to a large number of vectors derived from satellite images. In this study, the sea ice motion vectors automatically derived from pairs of AMSR-E 89GHz images (IFOV = 3.5 ${\times}$ 5.9km) by an image-to-image cross correlation were validated by comparing with sea ice motion vectors manually derived from pairs of cloudless MODIS images (IFOV=250 ${\times}$ 250m). Since AMSR-E and MODIS are both on the same Aqua satellite of NASA, the observation time of both sensors are the same. The relative errors of AMSR-E vectors against MODIS vectors were calculated. The accuracy validation has been conducted for 5 scenes. If we accept relative error of less than 30% as correct vectors, 75% to 92% of AMSR-E vectors derived from one scene were correct. On the other hand, the percentage of correct sea ice vectors derived from a pair of SSM/I 85GHz images (IFOV = 15 ${\times}$ 13km) observed nearly simultaneously with one of the AMSR-E images was 46%. The difference of the accuracy between AMSR-E and SSM/I is reflecting the difference of IFOV. The accuracies of H and V polarization were different from scene to scene, which may reflect the difference of sea ice distributions and their snow cover of each scene.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.11-14
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• 1996

Performance of the group cumulative sum(CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control(QC) characteristics than the control chart scheme based on the Hotelling statistics. We examine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the orginal measurement vectors, the scaled residual vectors from the regression of each variable on all others and the principal component vectors respectively to calculating the CUSUM statistics. They are also compared to the multivariate QC charts based on the Hotelling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.

• Management Science and Financial Engineering
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• v.5 no.1
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• pp.51-71
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• 1999

Performance of the group cumulative sum (CUSUM) control scheme using multiple univariate CUSUM charts is more sensitive to the change of quality control (QC) characteristics than the control chart schemes based on the Hotelling statistic We vexamine three group charts for multivariate normal data sets simulated with various correlation structures and shift directions in the mean vector. These group schemes apply the original measurement vectors, the scaled residual vectors from the re-gression of each variable on all others and the principal component vectors respectively to calculat-ing the CUSUM statistics. They are also compared to the multivariate QC charts based on the Ho-telling statistic by estimating average run lengths, coefficients of variation of run length and ranks in signaling order. On the basis of simulation results, we suggest a control chart scheme appropriate for specific quality control environment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.69-76
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• 1999

This paper proposes a new region-oriented coding scheme of still images. The scheme first segments an image into homogeneous regions by making use of Voronoi diagrams. Then luminance components in each Voronoi region are encoded using a transform coding technique which employs the Schmidt's orthogonalization method to produce orthonormal basis-functions for arbitrarily shaped regions. In this technique, coding efficiency depends on the properties of initial vectors to be orthogonalized. Therefore we adaptively select a set of initial vectors as well as the order in which these vectors are orthogonalized. Simulation results indicate that the proposed coding scheme achieves higher coding performance with much less computation than the KLT-based scheme which we reported formerly.

• Parasites, Hosts and Diseases
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• v.55 no.5
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• pp.579-582
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• 2017

To investigate the prevalence of intestinal parasite infections in Yanbian Prefecture, Jilin Province, China, epidemiological surveys were conducted on a collaboration basis between the Korean Centers for Disease Control and Prevention and the Yanbian Center for Disease Control and Prevention. A total of 8,396 (males 3,737 and females 4,659) stool samples were collected from 8 localities and examined with the formalin-ether sedimentation technique, and additionally examined with the cellotape anal swab to detect Enterobius vermicularis eggs. The overall rate of intestinal parasites was 1.57%. The prevalence of Ascaris lumbricoides was the highest (0.80%), followed by Entamoeba spp. (0.23%), heterophyid flukes (0.15%), Clonorchis sinensis (0.08%), Enterobius vermicularis (0.07%), hookworms (0.06%), Trichostrongylus spp. (0.06%), Giardia lamblia (0.04%), Paragonimus spp. (0.02%), Diphyllobothrium spp. (0.02%), Trichuris trichiura (0.02%). The prevalence by sex was similar, 1.58% (n=59) in males and 1.57% (n=73) in females. By the present study, it is partly revealed that the prevalences of intestinal parasite infections are relatively low among the inhabitants of Yanbian Prefecture, Jilin Province, China.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.195-198
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• 2012

The N basis functions are typically chosen so that Surface reflectance functions(SRFs) and spectral power distributions (SPDs) can be accurately reconstructed from their N-dimensional vector codes. Typical rendering applications assume that the resulting mapping is an isomorphism where vector operations of addition, scalar multiplication, component-wise multiplication on the N-vectors can be used to model physical operations such as superposition of lights, light-surface interactions and inter-reflection. The vector operations do not mirror the physical. However, if the choice of basis functions is restricted to characteristic functions then the resulting map between SPDs/SRFs and N-vectors is anisomorphism that preserves the physical operations needed in rendering. This paper will show how to select optimal characteristic function bases of any dimension N (number of basis functions) and also evaluate how accurately a large set of Munsell color chips can approximated as basis functions of dimension N.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.55-62
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• 2017

Non-negative matrix factorization (NMF) is one of the typical parts-based representation in which images are expressed as a linear combination of basis vectors that show the lcoal features or objects in the images. In this paper, we represent face images using various NMF methods and recognize their face identities based on extracted features using a learning vector quantization. We analyzed the various NMF methods by comparing extracted basis vectors. Also we confirmed the availability of NMF to the face recognition by verification of recognition rate of the various NMF methods.