• Health Policy and Management
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• v.26 no.3
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• pp.233-241
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• 2016

Background: This study purposed to analyze and understand how spatial accessibility of patients influenced the number of outpatient visits for the internal medicine of a hospital. Methods: A hospital with 100 beds in Seoul, South Korea provided data from 2013 January 1 to 2013 June 30. Euclidean distance and road ares were used to represent the spatial accessibility. Patient level data and dong level data were collected and used in spatial analysis. Dong level data was converted into grid level ($500{\times}500m$) for the multivariate analysis. Hot-spot analysis and generalized linear model were applied to the data collected. Results: Hot-spots of outpatient visits were found around the study hospital, and cold-spots were not found. Number of outpatient visits was varied by the distance between patient resident and hospitals, and about 80% of total outpatient visits was occurred in within the 5 km from study hospital, and 50% was occurred in within 1.6 km. Spatial accessibility had significant influences on the outpatient visits. Conclusion: Findings provide evidences that spatial accessibility had influences on the patients' behaviors in utilizing the outpatient care of internal medicine in a hospital. Results can provide useful information to health policy makers as well as hospital managers for their decision making.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• International Journal of Industrial Entomology and Biomaterials
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• v.31 no.2
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• pp.115-126
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• 2015

The tropical climate prevailing in India adversely affects temperate bivoltine silkworm rearing and causes crop loss especially during summer. Identification of high temperature tolerant bivoltine breeds by screening for thermotolerance in the silkworm, Bombyx mori (Lepidoptera: Bombycidae) is an essential prerequisite for the development of thermotolerant bivoltine breeds / hybrids. Therefore, in this study, 20 silkworm breeds were reared at different temperatures (25 ± 1℃,32 ± 1℃, 34 ± 1℃ and 36 ± 1℃) for 6 h every day from 3^rd d of 5^th instar to till spinning. Significant differences (p < 0.01) were found among all the rearing traits over temperature. Based on pupation percentage, SK4C and BHR3 were identified as thermotolerant bivoltine breeds. Hierarchical clustering analysis based on rearing traits at tested temperatures grouped 20 silkworm breeds in four clusters which included one cluster each of susceptible and tolerant, and two clusters of moderately tolerant silkworm breeds. This suggests that clustering based on rearing data at high temperatures by using Euclidean distance can be an effective approach in classifying the silkworm breeds on their thermotolerance capacity. The identified breeds would be used for development of thermo tolerant bivoltine silkworm breeds / hybrids.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.586-589
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• 2013

In this paper, the proposed scheme is STBC system combined with convolutional code to ensure the reliability of data transmission for a high rate wireless communication. In addition, this scheme uses a modified viterbi algorithm in order to get a high system gain when data is transmitted. Because we combine STBC and comvolutional code, the proposed scheme can get a diversity gain of STBC and coding gain of convolutional code at the same time. Unlike existing viterbi docoding algorithm using Hamming distance in order to calculate branch matrix, the modified viterbi algorithm uses Euclidean distance value between received symbol and reference symbol. To analyze the system proposed, it was simulated by changing the constraint length of the convolutional code and the number of transmit and receive antennas of STBC.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.146-159
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• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• Phonetics and Speech Sciences
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• v.5 no.2
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• pp.25-31
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• 2013

This study examined cross-generational changes in the vowel system of Seoul Korean. Acoustic analyses of the vowel formants of /o/ and /u/, and their Euclidean distances in the vowel space were undertaken to explore an on-going merger of these two vowels as proposed in previous acoustic studies and a phonological analysis by Chae (1999). A robust cross-generational change of /o/ and /u/ was found, more evident for female speakers than for male speakers. For female speakers, with each successive generation, /o/ became increasingly approximated with /u/, regardless of the syllable positions that the target vowels were posited, whereas the cross-generational differences in the Euclidean distances were only shown in the second syllable position for the male speakers. These results demonstrate that 1) women are more advanced than men in the on-going approximation of /o/ and /u/; 2) the approximation of /o/ and /u/ is common in the non-initial position. Taken together, the merger of /o/ and /u/ appears to be in progress in Seoul Korean.

• Development and Reproduction
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• v.23 no.2
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• pp.193-198
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• 2019

Genomic DNA samples were obtained from cultured penaeid shrimp (Penaeus chinensis) individuals such as fresh shrimp population (FSP) and deceased shrimp population (DSP) from Shinan regions in the Korean peninsula. In this study, 233 loci were identified in the FSP shrimp population and 162 in the DSP shrimp population: 33 specific loci (14.2%) in the FSP shrimp population and 42 (25.9%) in the DSP population. A total of 66 (an average of 9.4 per primer) were observed in DSP shrimp population, whereas 55 unique loci to each population (an average of 7.9 per primer) in the FSP shrimp population. The Hierarchical dendrogram extended by the seven oligonucleotides primers indicates three genetic clusters: cluster 1 (FRESH 01, 02, and DECEASED 12, 13, 15, 16, 17, 19, 20, 22) and cluster 2 (FRESH 03, 04, 05, 06, 07, 08, 09, 10, 11, and DECEASED 14, 18, 21). Among the twenty-two shrimp, the shortest genetic distance that exposed significant molecular differences was between individuals 20 and 16 from the DSP shrimp population (genetic distance=0.071), while the longest genetic distance among the twenty-two individuals that established significant molecular differences was between individuals FRESH no. 02 and FRESH no. 04 (genetic distance=0.477). In due course, PCR analysis has revealed the significant genetic distance among two penaeid shrimp populations.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.40-45
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• 2000

In speech corpus generation and speech recognition, it is sometimes needed to segment the input speech data without any prior knowledge. A method to accomplish this kind of segmentation, often called as blind segmentation, or acoustic segmentation, is to find boundaries which minimize the Euclidean distances among the feature vectors of each segments. However, the use of this metric alone is prone to errors because of the fluctuations or variations of the feature vectors within a segment. In this paper, we introduce the principal component analysis method to take the trend of feature vectors into consideration, so that the proposed distance measure be the distance between feature vectors and their projected points on the principal components. The proposed distance measure is applied in the LBDP(level building dynamic programming) algorithm for an experimentation of continuous speech segmentation. The result was rather promising, resulting in 3-6% reduction in deletion rate compared to the pure Euclidean measure.

• Korean Journal of Optics and Photonics
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• v.19 no.4
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• pp.270-275
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• 2008

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.