• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.148-155
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• 2024

This study investigated the ecological characteristics of Odontobutis platycephala at Jaho stream from January to December 2022. The riverbed structure of the species' habitat was rich in cobble and pebble. The water was deep, ranging from 22 to 153 cm, with an average of 64 cm, and the stream velocity was rapid at 0.89 (0.42-1.46) m/sec. The ratio of females to males was 1:1.02, and the total length of collected individuals ranged from 38 to 156 mm. The age according to the total length frequency distribution as of May indicated that the group with a total length of 38-69 mm was one year old, the group with 60-99 mm was two years old, the group with 100-139 mm was three years old, and the group 140-156 mm was four years or older. As a secondary gender characteristic, the genital papilla was cylindrical in females and cone-shaped with a pointed tip in males. Some females with a length ranging from 60 to 69 mm and all females 70 mm or longer were sexually mature. Some males with a length ranging from 70 to 79 mm and all males 80 mm or longer were sexually mature. The spawning season was from May to July, and the water temperature was between 17 ℃ and 28 ℃ during that period. The prosperous spawning season was June (24 ℃). The average number of eggs in the ovaries was 988 (284-2,722) per mature female, and the mature eggs were yellowish and spherical with a mean diameter of 1.46 (1.19-1.71) mm. The correlation between total length and body weight is BW=0.00000006TL^3.12 with the constant a as 0.00000006 and the parameter b as 3.12. The mean condition factor (K) was 1.44 (0.96-2.26), and the slope was negative at -0.0007

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.165-172
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• 2012

We investigated the vascular characteristics of tumors and normal tissue using perfusion CT in the rabbit brain tumor model. The VX2 carcinoma concentration of $1{\times}10^7$ cells/ml(0.1ml) was implanted in the brain of nine New Zealand white rabbits (weight: 2.4kg-3.0kg, mean: 2.6kg). The perfusion CT was scanned when the tumors were grown up to 5mm. The tumor volume and perfusion value were quantitatively analyzed by using commercial workstation (advantage windows workstation, AW, version 4.2, GE, USA). The mean volume of implanted tumors was $316{\pm}181mm^3$, and the biggest and smallest volumes of tumor were 497 $mm^3$ and 195 $mm^3$, respectively. All the implanted tumors in rabbits are single-nodular tumors, and intracranial metastasis was not observed. In the perfusion CT, cerebral blood volume (CBV) were $74.40{\pm}9.63$, $16.08{\pm}0.64$, $15.24{\pm}3.23$ ml/100g in the tumor core, ipsilateral normal brain, and contralateral normal brain, respectively ($p{\leqq}0.05$). In the cerebral blood flow (CBF), there were significant differences between the tumor core and both normal brains ($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($962.91{\pm}75.96$ vs. $357.82{\pm}12.82$ vs. $323.19{\pm}83.24$ ml/100g/min). In the mean transit time (MTT), there were significant differences between the tumor core and both normal brains ($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($4.37{\pm}0.19$ vs. $3.02{\pm}0.41$ vs. $2.86{\pm}0.22$ sec). In the permeability surface (PS), there were significant differences among the tumor core, ipsilateral and contralateral normal brains ($47.23{\pm}25.45$ vs. $14.54{\pm}1.60$ vs. $6.81{\pm}4.20$ ml/100g/min)($p{\leqq}0.05$). In the time to peak (TTP) were no significant differences among the tumor core, ipsilateral and contralateral normal brains. In the positive enhancement integral (PEI), there were significant differences among the tumor core, ipsilateral and contralateral brains ($61.56{\pm}16.07$ vs. $12.58{\pm}2.61$ vs. $8.26{\pm}5.55$ ml/100g). ($p{\leqq}0.05$). In the maximum slope of increase (MSI), there were significant differences between the tumor core and both normal brain($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($13.18{\pm}2.81$ vs. $6.99{\pm}1.73$ vs. $6.41{\pm}1.39$ HU/sec). Additionally, in the maximum slope of decrease (MSD), there were significant differences between the tumor core and contralateral normal brain($p{\leqq}0.05$), but no significant differences between the tumor core and ipsilateral normal brain($4.02{\pm}1.37$ vs. $4.66{\pm}0.83$ vs. $6.47{\pm}1.53$ HU/sec). In conclusion, the VX2 tumors were implanted in the rabbit brain successfully, and stereotactic inoculation method make single-nodular type of tumor that was no metastasis in intracranial, suitable for comparative study between tumors and normal tissues. Therefore, perfusion CT would be a useful diagnostic tool capable of reflecting the vascularity of the tumors.

• The Korean Journal of Nuclear Medicine
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• v.39 no.4
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• pp.252-256
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• 2005

Purpose: Thyroglobulin (Tg) is a valuable and sensitive tool as a marker for diagnosis and follow-up for several thyroid disorders, especially, in the follow-up of patients with differentiated thyroid cancer (DTC). Often, clinical decisions rely entirely on the serum Tg concentration. But the Tg assay is one of the most challenging laboratory measurements to perform accurately owing to antithyroglobulin antibody (Anti-Tg). In this study, we have compared the degree of Anti-Tg effects on the measurement of Tg between availale Tg measuring kits. Materials and Methods: Measurement of Tg levels for standard Tg solution was performed with two different kits commercially available (A/B kits) using immunoradiometric assay technique either with absence or presence of three different concentrations of Anti-Tg. Measurement of Tg for patient's serum was also performed with the same kits. Patient's serum samples were prepared with mixtures of a serum containing high Tg levels and a serum containg high Anti-Tg concentrations. Results: In the measurements of standard Tg solution, presence of Anti-Tg resulted in falsely lower Tg level by both A and B kits. Degree of Tg underestimation by h kit was more prominent than B kit. The degree of underestimation by B kit was trivial therefore clinically insignificant, but statistically significant. Addition of Anti-Tg to patient serum resulted in falsely lower Tg levels with only A kit. Conclusion: Tg level could be underestimated in the presence of anti-Tg. Anti-Tg effect on Tg measurement was variable according to assay kit used. Therefore, accuracy test must be performed for individual Tg-assay kit.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.