• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.145-154
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• 2008

In this paper, we propose the pet robot control system using hand gesture recognition in image sequences acquired from a camera affixed to the pet robot. The proposed system consists of 4 steps; hand detection, feature extraction, gesture recognition and robot control. The hand region is first detected from the input images using the skin color model in HSI color space and connected component analysis. Next, the hand shape and motion features from the image sequences are extracted. Then we consider the hand shape for classification of meaning gestures. Thereafter the hand gesture is recognized by using HMMs (hidden markov models) which have the input as the quantized symbol sequence by the hand motion. Finally the pet robot is controlled by a order corresponding to the recognized hand gesture. We defined four commands of sit down, stand up, lie flat and shake hands for control of pet robot. And we show that user is able to control of pet robot through proposed system in the experiment.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4113-4117
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• 2012

Breast cancer is a prevalent heterogeneous malignant disease. Gene expression profiling by DNA microarray can classify breast tumors into five different molecular subtypes: luminal A, luminal B, HER-2, basal and normal-like which have differing prognosis. Recently it has been shown that immunohistochemistry (IHC) markers including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), can divide tumors to main subtypes: luminal A (ER+; PR+/-; HER-2-), luminal B (ER+;PR+/-; HER-2+), basal-like (ER-;PR-;HER2-) and Her2+ (ER-; PR-; HER-2+). Some subtypes such as basal-like subtype have been characterized by poor prognosis and reduced overall survival. Due to the importance of the ER signaling pathway in mammary cell proliferation; it appears that epigenetic changes in the $ER{\alpha}$ gene as a central component of this pathway, may contribute to prognostic prediction. Thus this study aimed to clarify the correlation of different IHC-based subtypes of breast tumors with $ER{\alpha}$ methylation in Iranian breast cancer patients. For this purpose one hundred fresh breast tumors obtained by surgical resection underwent DNA extraction for assessment of their ER methylation status by methylation specific PCR (MSP). These tumors were classified into main subtypes according to IHC markers and data were collected on pathological features of the patients. $ER{\alpha}$ methylation was found in 25 of 28 (89.3%) basal tumors, 21 of 24 (87.5%) Her2+ tumors, 18 of 34 (52.9%) luminal A tumors and 7 of 14 (50%) luminal B tumors. A strong correlation was found between $ER{\alpha}$ methylation and poor prognosis tumor subtypes (basal and Her2+) in patients (P<0.001). Our findings show that $ER{\alpha}$ methylation is correlated with poor prognosis subtypes of breast tumors in Iranian patients and may play an important role in pathogenesis of the more aggressive breast tumors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.44-52
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• 2010

In this paper, we propose a novel method for face recognition which uses polar coordinate instead of conventional cartesian coordinate. Among the central area of a face, we select a point as a pole and make a polar image of a face by evenly sampling pixels in each direction of 360 degrees around the pole. By applying conventional feature extraction methods to the polar image, the recognition rates are improved. The polar coordinate delineates near-pole area more vividly than the area far from the pole. In a face, important regions such as eyes, nose and mouth are concentrated on the central part of a face. Therefore, the polar coordinate of a face image can achieve more vivid representation of important facial regions compared to the conventional cartesian coordinate. The proposed polar coordinate transform was applied to Yale and FRGC databases and LDA and NLDA were used to extract features afterwards. The experimental results show that the proposed method performs better than the conventional cartesian images.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.77-85
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• 2011

In this paper, we propose an region segmentation and texture based feature extraction method which split the pavement and roadway from the camera which equipped to the visually impaired person during a walk. We perform the hough transformation method for detect the boundary between pavement and roadway, and devide the segmented region into 3-level according to perspective. Next step, split into pavement and roadway according to the extracted texture feature of segmented regions. Our walking assistance system use rotation-invariant LBP and GLCM texture features for compare the characteristic of pavement block with various pattern and uniformity roadway. Our proposed method show that can segment two regions with illumination invariant in day and night image, and split there regions rotation and occlution invariant in complexed outdoor image.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• Journal of the Korean Geophysical Society
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• v.9 no.1
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• pp.1-6
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• 2006

To remove ringing and increase vertical resolution of GRP data, signature deconvolution was applied to GPR data obtained using a 100 MHz antenna in the Soyang Lake. The signature was extracted through stacking reflection signals from the lake bottom. Results of this deterministic deconvolution was compared with those from the conventional Wienner method. Due to increased vertical resolution, both deconvolution methods are able to resolve three or more layers in an apparent single layer on the input data. However, identification of reflection boundaries from ringing is not easy due to poor definition in the output data of the Wienner filter. On the contrary, the signature deconvolution greatly enhances both vertical resolution and definition of reflection boundaries, showing detailed internal stratigraphic features of the three sedimentary layers. Since extraction of signature at various depths is possible, this deconvolution method can be appled effectively to unstationary GPR data.

• Journal of Ginseng Research
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• v.42 no.1
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• pp.57-67
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• 2018

Background: Ginseng contains many small metabolites such as amino acids, fatty acids, carbohydrates, and ginsenosides. However, little is known about the relationships between microorganisms and metabolites during the entire ginseng fermentation process. We investigated metabolic changes during ginseng fermentation according to the inoculation of food-compatible microorganisms. Methods: Gas chromatography mass spectrometry (GC-MS) datasets coupled with the multivariate statistical method for the purpose of latent-information extraction and sample classification were used for the evaluation of ginseng fermentation. Four different starter cultures (Saccharomyces bayanus, Bacillus subtilis, Lactobacillus plantarum, and Leuconostoc mesenteroide) were used for the ginseng extract fermentation. Results: The principal component analysis score plot and heat map showed a clear separation between ginseng extracts fermented with S. bayanus and other strains. The highest levels of fructose, maltose, and galactose in the ginseng extracts were found in ginseng extracts fermented with B. subtilis. The levels of succinic acid and malic acid in the ginseng extract fermented with S. bayanus as well as the levels of lactic acid, malonic acid, and hydroxypruvic acid in the ginseng extract fermented with lactic acid bacteria (L. plantarum and L. mesenteroide) were the highest. In the results of taste features analysis using an electronic tongue, the ginseng extracts fermented with lactic acid bacteria were significantly distinguished from other groups by a high index of sour taste probably due to high lactic acid contents. Conclusion: These results suggest that a metabolomics approach based on GC-MS can be a useful tool to understand ginseng fermentation and evaluate the fermentative characteristics of starter cultures.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.11-20
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• 2006

This paper presents a method for detecting tumors on chicken carcasses by fusion of hyperspectral fluorescence and reflectance images. Classification of normal skin and tumor is performed by the image obtain 어 from optimal band ratio which minimizes the overlapping area of PDFs for normal skin and tumor. This method yields four feature images, each of them represents the ratio of two intensity values from a pixel. Classification is achieved by applying ISODATA to each pixel from the feature images. For the analysis of reflectance image, band selection method is proposed based on the information quantity, many effective features are acquired for the classification by defining the linear transformation selecting the projection axis, accordingly, accurate interpretation of images is possible in the reflectance image and automatic feature selection method is realized. Feature images from reflectance images are also classified by ISODATA and combined with the result from fluorescence images. Experimental result indicates that improved performance in term of reducing false detection rate is observed.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.6067-6071
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• 2015

Background: Gastric cancer accounts for about 8% of the total cancer cases and 10% of total cancer deaths worldwide. It is the second lethal cancer after esophageal cancer and is considered the fourth most common cancer in north and northwest Iran. The bcl2 family has a key role in the regulation of apoptosis and change in its expression can contribute to cancer. This study initially scheduled to determine the expression of bcl2 gene in tissue samples of adenocarcinoma cancer patients. Materials and Methods: A total of 10 samples of gastric adenocarcinoma and 10 of normal tissues from Sari hospital were selected and after DNA extraction from tissues, bcl2 gene expression assayed by real-time PCR. Results: Our results demonstrated higher expression of the bcl2 gene in control compared with cancer and marginal cancer tissues. Conclusions: On one hand BCL2 plays an important role as an oncogene to inhibit apoptosis; on the other hand, it can initiate cell cycle arrest at G0 stage. Our observed association between its expression and patient survival is quite conflicting and may be tissue-specific. The data suggest expression both tumoural and non-tumoral(marginal) groups have lowered expression than controls (P>0.05). Due to the low number of samples we could not examine the relationship with clinicopathological features. However, bcl-2 expression may be important for prognostic outcome or a useful target for therapeutic intervention.

• The Korean Journal of Community Living Science
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• v.18 no.4
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• pp.655-670
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• 2007

The purpose of this study was to develop a brand naming strategy for rural specialty products of Muju. This study was to apply brand development research to the field. The selected investigation subject, a local farming operation producing a specialty product, was chosen from a New Enterprise Support Project of Farm Women ('06). The selected subject was a farming operation producing bokbunja drink in Muju. The data was derived from 137 female consumers. The major results of this study were as follows. First, the process of brand development strategy was advanced by six steps: Step 1, environment analysis; Step 2, brand case study; Step 3, SWOT analysis (Strength Weakness, Opportunity, Threat factor analysis); Step 4, direction design for brand naming development (Targeting, Brand focus concept extraction); Step 5, brand logo and symbol production; and Step6, Evaluation. Second, to extract brand naming development direction, key concepts in association with natural connection, area features, representative resources, and natural friendship were investigated. Namely, the health function concept was selected on the basis of a consumer recognition investigation where results appeared most highly favorable. Also, it was decided that the direction of brand naming should emphasize a green image and the location name of Muju. Finally, the brand name was decided to be "Saenggichan" bokbunja liquid and the slogan went with "guchondon-native thorn bokbunja from jar maturing."