• Journal of Fashion Business
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• v.7 no.5
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• pp.108-118
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• 2003

Surface properties, including the texture and the luster, of cotton fibers and yarns thereof play an important role in textile technology. The convolutions and the cross-sectional shape of the cotton fiber affect the fabric texture and the luster accordingly. Mercerization of the cotton fabric affects the luster, strength, and other properties of the fabric. In this study, the effect of mercerization was examined on the luster of the cotton fabric, together with the effect of polishing treatment. One of the traditional methods determining the fabric luster is the use of glossmeter or goniometric glossmeter. The use of glossmeter gives successful results in determining the gloss of rather flat and continuous surface such as plastic sheet, painted surface, or paper products. Since the textile fabrics have diverse surface structures and textures, these could be regarded as having three-dimensional surface. Such complexity imposes some difficulties for differentiating subtle surface luster properties of diverse textile fabrics. The advancement in the area of imaging technologies has enabled the micro-scale analysis of the surface textures and the fabric luster recently. Using a CCD camera, the surface luster images were taken at various incident illumination conditions. Microscale analysis, including the blob analysis, of the images could differentiate the subtle luster properties present in a group of cotton fabric samples comprising mercerized cotton fabric, non-mercerized cotton fabric, polished cotton fabric, and a 'standard' cotton fabric. The glossmeter measurement gave satisfactory but limited differentiation among the samples, whose luster differences are easily recognizable with visual observation, except for the mercerized cotton fabric sample and the non-mercerized cotton fabric. The microscale analysis of the fabric luster could, therefore, help understand the nature of diverse textile fabric luster.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.307-313
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• 2021

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.1
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• pp.85-93
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• 1997

In this paper, a novel method for obtaining spectra of short pulses is proposed. This method will be well-applied to perform spectral analyses of ultrashort laser pulses which are known to be difficult to evaluate their exact forms in frequency domain because of their narrow-width characteristics in time domain. It must be noted that the method can be represented by a recursive formula derived from the PRS(partial-response signaling) model, and thus more easily available for numerical solutions than the conventional methods such as consecutive differentiations and convolutions. The pulses whose fundamental frames can be exactly determined or approximately represented by truncated Fourier series have well-behaving conditions for applications of this new method. Moreover, the transversal 9-type PRS model can offer various approximations for spectral analyses of ultrashort pulses currently used in transmission systems.

• Journal of the Korean Society of Food Science and Nutrition
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• v.25 no.4
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• pp.551-559
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• 1996

To examine the effects of dietary fibers on gastrointestinal physiology, rats were fed with diets containing 10% sodium alginate,10% cellulose, or fiber-free diets for 5 weeks. The results obtained were as follows: The chronic consumption of sodium alginate induced a significant decrease in body weight gain and feeding efficiency, but a significant increase in length and weight of small intestine. Fecal bulk and weight were higher in fiber-fed group than fiber-free group. The chronic consumption of dietary fiber induced a significant increase in fecal output, resulting in tile decrease of apparent digestibility of protein and lipid. Pancreatic protease activity was lower in fiber-fed group than fiber-free group, whereas pancreatic amylase and lipase activities were not affected. Scanning electron microscopy(SEM) and light microscopy(LM) studies showed small intestine microvilli with numerous ridges and convolutions and goblet cells in fiber-fed groups. As a result of this study, it is concluded that the chronic consumption of dietary fiber decreases apparent digestibility of nutrients and induces morphological and biochemical adaptation of digestive organs.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.475-487
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• 2011

In this paper, we present a systematic method to derive spectrum of high-order pulse and a novel design of e-Learning system that deals with deriving the spectrum using concept-based branching method. Spectrum of high-order pulse can be derived using conventional methods including 'Consecutive Differentiations' or 'Convolutions', however, their complexity of calculation should be too high to be used as the order of the pulse increase. We develop a recursive algorithm according to the order of pulse, and then derive the formula of spectrum connected to the order with a newly designed look-up table. Moving along, we design an e-Learning content for studying the procedure of deriving high-order pulse spectrum described above. In this authoring, we use the concept-based object branching method including conventional page or title-type branching in sequential playing. We design all four Content-pages divided into 'Modeling', 'Impulse Response and Transfer Function', 'Parameters' and 'Look-up Table' by these conceptual objects. And modules and sub-modules are constructed hierarchically as conceptual elements from the Content-pages. Students can easily approach to the core concepts of the analysis because of the effects of our new teaching method. We offer step-by-step processes of the e-Learning content through unit-based branching scheme for difficult modules and sub-modules in our system. In addition we can offer repetitive learning processes for necessary block of given learning objects. Moreover, this method of constructing content will be considered as an advanced effectiveness of content itself.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.156-162
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• 2007

The parallel beam SPECT system acquires projection data by using collimators in conjunction with photon detectors. The projection data of the parallel beam SPECT system is, however, blurred by the point response function of the collimator that is used to define the range of directions where photons can be detected. By increasing the number of parallel holes per unit area in collimator, one can reduce such blurring effect. This approach also, however, has the blurring problem if the distance between the object and the collimator becomes large. In this paper we consider correction methods for artifacts caused by non-circular orbit of parallel beam SPECT with many parallel holes per detector cell. To do so, we model the relationship between the object and its projection data as a linear system, and propose an iterative reconstruction method including artifacts correction. We compute the projector and the backprojector, which are required in iterative method, as a sum of convolutions with distance-dependent point response functions instead of matrix form, where those functions are analytically computed from a single function. By doing so, we dramatically reduce the computation time and memory required for the generation of the projector and the backprojector. We conducted several simulation studies to compare the performance of the proposed method with that of conventional Fourier method. The result shows that the proposed method outperforms Fourier methods objectively and subjectively.