• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.67-71
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• 2013

Purpose: The Change of CT exposure condition have a effect on image quality and patient exposure dose. In this study, we evaluated effect CT image quality and SUV when CT parameters (Pitch, Rotation time) were changed. Materials and Methods: Discovery Ste (GE, USA) was used as a PET/CT scanner. Using GE QA Phantom and AAPM CT Performance Phantom for evaluate Noise of CT image. Images are acquired by using 24 combinations that four stages pitch (0.562, 0.938, 1.375, 1.75:1) and six stages X-ray tube rotation time (0.5s-1.0s). PET images are acquired using 1994 NEMA PET Phantom ($^{18}F-FDG$ 5.3 kBq/mL, 2.5 min/frame). For noise test, noise are evaluated by standard deviation of each image's CT numbers. And then we used expectation noise according to change of DLP (Dose Length Product) to experimental noise ratio for index of effectiveness. For spatial resolution test, we confirmed that it is possible to identify to 1.0 mm size of the holes at the AAPM CT Performance Phantom. Finally we evaluated each 24 image's SUV. Results: Noise efficiency were 1.00, 1.03, 1.01, 0.96 and 1.00, 1.04, 1.02, 0.97 when pitch changes at the QA Phantom and AAPM Phantom. In case of X-ray tube rotation time changes, 0.99, 1.02, 1.00, 1.00, 0.99, 0.99 and 1.01, 1.01, 0.99, 1.01, 1.01, 1.01 at the QA Phantom and AAPM Phantom. We could identify 1.0 mm size of the holes all 24 images. Also, there were no significant change of SUV and all image's average SUV were 1.1. Conclusion: 1.75:1 pitch is the most effective value at the CT image evaluation according to pitch change and It doesn't affect to the spatial resolution and SUV. However, the change of rotation time doesn't affect anything. So, we recommend to use the effective pitch like 1.75:1 and adequate X-ray tube rotation time according to patient size.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.67-76
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• 2022

This study aimed to determine the photosynthesis and growth characteristics of Peucedanum japonicum T. grown under aquaponics in a plant factory (AP) by comparing those grown under hydroponic cultivation system (HP). The AP system raised 30 fishes at a density of 10.6 kg·m^-3 in a 367.5 L tank, and at HP, nutrient solution was controlled with EC 1.3 dS·m^-1 and pH 6.5. The pH level ranged from 4.0 to 7.1 for the AP system and 4.0 to 7.4 for the HP system. The pH level in the AP began to decrease with an increase in nitrate nitrogen (NO₃-N) and lasted bellower than pH 5.5 for 15-67 DAT. It was found that ammonium nitrogen (NH₄-N) continued to increase even under low pH conditions. EC was maintained at 1.3 to 1.5 dS·m^-1 in both systems. The concentration of major mineral elements in the fish tank was higher than that of the hydroponics, except for K and Mg. There was no significant difference in the photosynthesis characteristics, but the PIABS parameters were 30.4% lower in the AP compared to the HP at the 34DAT and 12.0% lower at the 74DAT. There was no significant difference in the growth characteristics, but the petiole length was 56% longer in the leaf grown under the AP system. While there was no significant difference in the fresh and dry weights of leaf and root, the leaf area ratio was 36.43% higher in the AP system. All the integrated results suggest that aquaponics is a highly-sustainable farming to safely produce food by recycling agricultural by-products, and to produce Peucedanum japonicum as much as hydroponics under a proper fish density and pH level.

• Journal of Bio-Environment Control
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• v.33 no.2
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• pp.88-98
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• 2024

This study provides basic data on the growth and production of seedlings produced in plant factories with artificial lighting by comparing seedling quality, growth and fruit characteristics, and production after transplanting cucumber seedlings according to environmental differences between plant factories with artificial lighting and conventional nurseries in greenhouse. The control group consisted of greenhouse seedlings (GH) grown in the conventional nursery before transplanting. Plant factory to greenhouse seedlings (PG) were grown for 9 days in a plant factory with artificial lighting and for 13 days in an conventional nursery. Plant factory seedlings (PF) were grown in a plant factory with artificial lighting for 22 days until planting. In terms of seedling quality, PFs had the highest relative growth rate and compactness and the best root zone development. After transplanting PFs tended to grow faster, the first harvest date was 2 days earlier than that of GHs, and the growing season ended 1 day earlier. The female flower flowering rate of the PFs was high, and the fruit set rate was of PF the lowest. The production per unit area was highest for PFs at 10.23kg Performance index on the absorption basis, the most sensitive chlorophyll fluorescence parameter, was highest at 4.14 for PFs at 4 weeks after transplantation. By comparing the maximum quantum yield of primary PS II photochemistry and dissipated energy flux per PS II reaction center electron at 4 weeks after transplantation, PFs tended to be the least stressed. PFs had the best seedling quality, growth, and production after planting, and fruit quality was consistent with that of greenhouse seedlings. Therefore, plant factory seedlings can be used in the field.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.59-80
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• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Distribution Research
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• v.17 no.2
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• pp.1-28
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• 2012

I. Introduction : The purpose of this study is to test empirically hypothetical causality among constructs used in previous studies to build the model of relational market behavior on customers' participation and consumer-brand relationship after introducing theories of relationship marketing, loyalty program, consumer-brand relationship, customers' participation in service marketing as previous studies with regard to relational market behavior, which Bagozzi(1995) and Peterson(1995) commented on constructs and definition suggested by Sheth and Parvatiyar (1995). For this purpose, loyalty program by the service provider, customers' participation and consumer-brand relationship as preceding variables explain relational market behavior defined by Sheth and Parvatiyar(1995). This study proposes that loyalty program as a tool of relationship marketing will be effective in that consumers' participation in marketing relationship results in a narrow range of choice(Sheth and Parvatiyar, 1995) because consumers think that their participation motive result in benefits(Peterson, 1995). Also, it is proposed that the quality of consumer-brand relationship explain the performance of relationship as well as the intermediary effect because the loyalty program could be evaluated based on relationship with customers. We reviewed the variables with regard to performance of relationship based on relation maintain in marketing literature, and then tested our hypotheses related to several performance variables including loyalty and intention of relation maintain based on the previous studies and constructs(Bendapudi and Berry, 1997 ; Bettencourt, 1997 ; Palmatier, Dant, Grewal and Evans, 2006 ; You Jae Yi and Soo Jin Lee, 2006). II. Study Model : Analyses about hypothetical causality were proceeded. The marketing effect of loyalty program on relational market behavior was empirically tested in study regarding a service provider. The research model in according to the path hypotheses (loyalty program ${\rightarrow}$ customers' participation ${\rightarrow}$ consumer-brand relationship ${\rightarrow}$ relational market behavior and loyalty program ${\rightarrow}$ consumer-brand relationship, and loyalty program ${\rightarrow}$ relational market behavior and customers' participation ${\rightarrow}$ consumer-brand relationship, and customers' participation ${\rightarrow}$ relational market behavior) proceeded as an activity for customer relation management was suggested. The main purpose of study is to see if relational market behavior could be brought as a result of developing relationship between consumers and a corporate into being stronger and more valuable when a corporate or a service provider try aggressively to build the relationship with customers (Bettencourt, 1997; Palmatier, Dant, Grewal and Evans, 2006; Sheth and Parvatiyar, 1995). III. Conclusion : The results of research into the membership fitness club, one of service areas with high level of customer participation (Bitner, Faranda, Hubbert and Zeithaml, 1997; Chase, 1978; Kelley, Donnelly, Jr. and Skinner, 1990) are as follows: First, causalities in according to path hypotheses were tested, after the preceding variables affecting relational market behavior and conceptual frame were suggested. In study, all hypotheses were supported as expected. This result confirms the proposition suggested by Sheth and Parvatiyar(1995), who claimed that intention of consumer and corporate to participate in marketing relationship brings high level of marketing productivity. Also, as a corporate or a service provider try aggressively to build relationship with customers, the relationship between consumers and a corporate can be developed into stronger and more valuable one (Bettencourt, 1997; Palmatier, Dant, Grewal and Evans, 2006). This finding supports the logic of relationship marketing. Second, because the question regarding the path hypothesis of consumer-brand relationship ${\rightarrow}$ relational market behavior are still at issue, the further analyses were conducted. In particular, there existed the mediating effects of consumer-brand relationship toward relational market behavior. Also, multiple regressions were conducted to see if which one strongly influences relational market behavior among specific question items with regard to consumer-brand relationship. As a result, the influence between items composing consumer-brand relationship and ones composing relational market behavior was different. Among items composing consumer-brand relationship, intimacy was an influence of sustaining relationship, word of mouth, and recommendation, intimacy and interdependence were influences of loyalty, intimacy and self-connection were influences of tolerance and advice. Notably, commitment among items measuring consumer-brand relationship had the negative influence with relational market behavior. This means that bringing relational market behavior is not consumer-brand relationship without personal commitment, but effort to build customer relationship like intimacy, interdependence, and self-connection. This finding confirms the results of Breivik and Thorbjornsen(2008). They reported that six variables composing the quality of consumer-brand relationship have higher explanation in regression model directly affecting performance of consumer-brand relationship. As a result of empirical analysis, among the constructs with regard to consumer-brand relationship, intimacy(B=0.512), interdependence(B=0.196), and quality of partner(B=0.153) had the effects on relation maintain. On the contrary, self-connection, love and passion, and commitment had little effect and did not show the statistical significance(p<0.05). On the other hand, intimacy(B=0.668) and interdependence(B=0.181) had the high regression estimates on word of mouth and recommendation. Regarding the effect on loyalty, explanation level of the model was high(R2=0.515), intimacy(0.538), interdependence(0.223), and quality of partner(0.177) showed the statistical significance(p<0.05). Furthermore, intimacy(0.441) had the strong effect as well as self-connection(0.201) and interdependence (0.163) had the effect on tolerance and forgive. And these three variables showed effects even on advice and suggestion, intimacy(0.373), self-connection(0.270), interdependence (0.155) respectively. Third, in study with regard to the positive effect(loyalty program ${\rightarrow}$ customers' participation, loyalty program ${\rightarrow}$ consumer-brand relationship, loyalty program ${\rightarrow}$ relational market behavior, customers' participation ${\rightarrow}$ consumer-brand relationship, customers' participation ${\rightarrow}$ relational market behavior, consumer-brand relationship ${\rightarrow}$ relational market behavior), the path hypothesis of customers' participation ${\rightarrow}$ consumer-brand relationship, was supported. The fact that path hypothesis of customers' participation ${\rightarrow}$ consumer-brand relationship was supported confirms assertion by Bitner(1995), Fournier(1994), Sheth and Parvatiyar(1995) about consumer relationship to participate in marketing relationship.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.145-149
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• 2013

Helical Tomotherapy is an innovative means of delivering intensity modulated radiation therapy (IMRT) using a device that merges features of a linear accelerator and helical computed tomography (CT) scanner. Hereat, during helical tomotherapy process, megavoltage computed tomography (MVCT) image are usually used for guiding the precise set-up of patient before/after treatment delivery. But which would certainly increase the total dose for patients, this study was to investigate the imaging dose of MVCT using the cylindrical "Cheese" phantom on a tomotherapy machine. A set of cylindrical "Cheese" phantom was adopted for scanning with respectively pitch value (1, 2, 3 mm) with same number slice (10 slice), same length (approximately 9 cm) and phantom set-ups on the couch of tomotherapy system. The average MVCT imaging dose were measured using A1SL ion chamber inserted in the phantom with preset geometry. The MVCT scanning average dose for the cylindrical "Cheese" phantom was 2.24 cGy, 1.02 cGy, 0.81 cGy during respectively pitch value (pitch 1, 2, 3 mm) with same number slice (10 slice), and same length's average dose was 2.47 cGy, 1.28 cGy, 0.88 cGy respectively (pitch 1, 2, 3 mm). Two major parameters, the assigned pitch numbers and scanning length, where the most important impacts to the dose variation. The MVCT dose was inversely proportional to the CT pitch value. The results may provide a reliable guidance for proper planning design of the scanning region, which is valuable to help minimize the extra dose to patient. Questionnaires were distributed to Radiology departments at hospitals with 300 sickbeds throughout the Pohang region of North Gyeongsang Province concerning awareness and performance levels of infection control. The investigation included measurements of the pollution levels of imaging equipment and assistive apparatuses in order to prepare a plan for the activation of prevention and management of hospital infections. The survey was designed to question respondents in regards to personal data, infection management prevention education, and infection management guidelines.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.49-57
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• 2021

Objectives : This study evaluated the medical communication skills of trainee doctors and analyzed the relationship between medical communication skills, self-efficacy on clinical performance (SECP) and empathy. Methods : A total of 106 trainee doctors from a university hospital participated. The questionnaire comprised self-evaluated medical communication skills, modified SECP and the Korean version of the Jefferson Scale of Empathy-Health Professionals version. The mean difference in medical communication skills scores according to gender, age, division (intern, internal medicine group or surgery group) and position (intern, first-/second- and third-/fourth-year residents) were analyzed. Pearson correlation coefficients were determined between medical communication skills, modified SECP and empathy. The effects of each variable on medical communication skills were verified using the structural equation model. Results : There were no statistically significant mean differences in self-evaluated medical communication skills according to gender, age, division or position. Medical communication skills had a significant positive correlation with modified SECP (r=0.782, p<0.001) and empathy (r=0.210, p=0.038). Empathy had a direct effect on modified SECP (β=0.30, p<0.01) and modified SECP had a direct effect on medical communication skills (β=0.80, p<0.001). Empathy indirectly influenced medical communication skills, mediating modified SECP (β=0.26, p<0.05). Conclusions : Medical communication skills are an important core curriculum of residency programs, as they have a direct correlation with SECP, which is needed for successful treatment. Moreover, the medical communication needs a new understanding that is out of empathy.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• 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.