• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.1
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• pp.84-97
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• 2017

Water vapor and thermal transmission properties of high emotional garments are important to evaluate wear comfort; in addition, the measuring methods of these properties are also critical for breathable and warm suit fabrics. In this study, the water vapor and thermal properties of composite yarn fabrics made of CoolMax, Tencel, and Bamboo fibers with filaments were measured and compared according to the measuring method. Water Vapor Transmittance (WVT) of the fabric woven by the sheath/core composite yarn in the warp direction was the highest due to the small staple fiber volume in the sheath/core yarn structure and high air voids in the sheath/core yarn fabrics. This property was also the highest in fabrics woven by bamboo staple yarns in the weft direction, and was the lowest on hi-multi filament fabrics. However, water vapor resistance ($R_{ef}$) of these fabrics by KSK ISO 11092 showed the opposite results to the water vapor transmittance method ($CaCl_2$ method); in addition, its correlation coefficient was low. The correlation coefficient between $R_{ef}$ and the drying rate was 0.719; therefore, the measurement mechanism of $R_{ef}$ is analogous to the drying property measurement. The thermal conductivity of the fabrics woven with compact staple yarn showed a high value; however, the hi-multi filament fabric showed low thermal conductivity. Therefore, fiber characteristics affect thermal properties more than yarn structure. The correlation between thermal property and moisture transport was also low. This study showed that: water vapor transmittance was active at the loose yarn structure, dry heat transport was vigorous at the compact yarn structure, and heat transport was affected more by fiber characteristics than yarn structure. In conclusion, sheath/core composite yarns were relevant to the high absorptive cool suit along with siro-fil and CoolMax/Bamboo staple yarns that were relevant to the heat diffusive cool suit.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.349-356
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• 2005

Artificial Organism-used application areas are expanding at a break-neck speed with a view to getting things done in a dynamic and Informal environment. A use of general programming or traditional hi methods as the representation of Artificial Organism behavior knowledge in these areas can cause problems related to frequent modifications and bad response in an unpredictable situation. Strategies aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But the learning method of Artificial-Organism is not good yet, and can't represent life in the environment. With this in mind, this research is designed to come up with a new behavior evolution model. The model represents behavior knowledge with Classification Rules and Enhanced Backpropation Neural Networks and discriminate the denomination. To evaluate the model, the researcher applied it to problems with the competition of Artificial-Organism in the Simulator and compared with other system. The survey shows that the model prevails in terms of the speed and Qualify of learning. The model is characterized by the simultaneous learning of classification rules and neural networks represented on chromosomes with the help of Genetic Algorithm and the consolidation of learning ability caused by the hybrid processing of the classification rules and Enhanced Backpropagation Neural Network.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.117-128
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• 2011

In this paper, we present a novel approach for contents-based medication image retrieval from a medication image database using the shape classification and color information of the medication. One major problem in developing a contents-based drug image retrieval system is there are too many similar images in shape and color and it makes difficult to identify any specific medication by a single feature of the drug image. To resolve such difficulty in identifying images, we propose a hybrid approach to retrieve a medication image based on shape and color features of the medication. In the first phase of the proposed method we classify the medications by shape of the images. In the second phase, we identify them by color matching between a query image and preclassified images in the first phase. For the shape classification, the shape signature, which is unique shape descriptor of the medication, is extracted from the boundary of the medication. Once images are classified by the shape signature, Hue and Saturation(HS) color model is used to retrieve a most similarly matched medication image from the classified database images with the query image. The proposed system is designed and developed especially for specific population- seniors to browse medication images by using visual information of the medication in a feasible fashion. The experiment shows the proposed automatic image retrieval system is reliable and convenient to identify the medication images.