• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.75-80
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• 2008

Measurement of network traffic have shown that the self-similarity is a ubiquitous phenomenon spanning across diverse network environments. In previous work, we have explored the feasibility of exploiting the long-range correlation structure in a self-similar traffic for the congestion control. We have advanced the framework of the multiple time scale congestion control and showed its effectiveness at enhancing performance for the rate-based feedback control. Our contribution is threefold. First, we define a modular extension of the TCP-a function called with a simple interface-that applies to various flavours of the TCP-e.g., Tahoe, Reno, Vegas and show that it significantly improves performance. Second, we show that a multiple time scale TCP endows the underlying feedback control with proactivity by bridging the uncertainty gap associated with reactive controls which is exacerbated by the high delay-bandwidth product in broadband wide area networks. Third, we investigate the influence of the three traffic control dimensions-tracking ability, connection duration, and fairness-on performance.

• Archives of design research
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• v.14 no.4
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• pp.5-14
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• 2001

Among Human Sensibility-oriented products a gap between the images that designers try to express through that product and users emotional evaluation becomes an issue. The data on the correlation between image words used for design evaluation and images used in the design process are especially significant. This study based on these correlations suggests a Fuzzy retrieval system supporting styling design with images and image words. In the system, the relational data are demonstrated by Fuzzy thesaurus as correlation coefficient from the degree of similarity among image words. And the degree of similarity is produced based on image evaluation. Image retrieval is conducted by the algorithm of Fuzzy thesaurus development, 1) among image words, 2) images to image words, 3) image words to images and 4) among images: 4 different modes are provided as retrieval modes. Also transfer between modes is carried by direct operating interface, therefore divergent thinking and convergent thinking is supported well. The system consists of operation for the gap and the measurement unit of emotional evaluation, and visualization units. Under unified interface environments are set in order for consistency of the operation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.147-155
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• 2014

While variety of content on the internet has increased with the development of IT and person's needs about suitable information are increasing rapidly, studies for personalized service have been actively performed. In the study, we proposed the Hypertension and Diabetes risk analysis system according to personal food intake preference using the analysis method of buying preferences in product recommendation system. For the analysis of food intake preference, the Pearson correlation coefficient is used to calculate similarity weights between each reference analysis data and sample data and then reference data should be grouping into the similarity weights and calculating risk of hypertension and diabetes each group. To evaluate the significance of this system, 1,021 subjects are applied the system. Hypertension and diabetes groups' risk is significant higher than normal group statistically so, it is confirmed that food intake preference and the diseases were relevant. In this paper, we verify the validity of hypertension and diabetes risk analysis system using a personal food intake preference.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.11
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• pp.2082-2089
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• 1992

An experimental study on forging an axi-symmetric dome type of AISI4130 was carried out using modeling material. In order to verify the validity of the experimental data, a similarity study between plasticine and AISI4130 has been made. Friction conditions were characterized by ring test for the various lubricants. For the closed-die forging experiments of an axi-symmetric dome type of AISI4130 using the plasticine, various cylindrical billets with different aspect ratios were forged and different flash width to thickness(W/T) ratios were used in order to determine the optimum forging conditions. As W/T ratios decrease forging loads decrease while excess volumes increase. It was found out that the experimental results reproduce the similiar results available in the literature. As a result of these experiments, it was construed physical modeling is an excellent tool for forging process simulation at a practical level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.756-760
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• 2017

Meeting the growing demand deadlines, reducing the production cost and upgrading the quality control measurements are the reasons why the automotive part manufacturers are venturing into automation. Attaining these objectives is impossible with human inspection for many reasons. Accordingly, the introduction of inspection system purposely for door hinge bracket inspection is presented in this study as an alternative for human inspection. This proposal is designed to meet the demands, features and specifications of door hinge bracket manufacturing companies in striving for increased throughput of better quality. To improve demerits of this manual operation, inspection system is introduced. As the inspection algorithm, template matching algorithm is applied to distinguish the articles of good quality and the poorly made articles. Through the verification test of the inspection process algorithm and the similarity metric matching algorithm, the detection accuracy was 98%, and it was applied to the production site to contribute to the improvement of the productivity due to the decrease of the defective product.

• Archives of Pharmacal Research
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• v.24 no.6
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• pp.568-571
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• 2001

The purpose of this work was to study the effect of storage time and temperature on the in vitro release kinetics of a commercial sustained-release dosage form of theophylline, at different pHs of the dissolution medium. The formulation was stored at $35^{\circ}C$ for 16 months and at $45^{\circ}C$ for 8 months, with a relative humidity of 60%. The in vitro release tests were performed at pHs 2, 4, 6 and 7.4. The mean values of the transport coefficient n, were close to 0.5 in all the conditions tested, which indicates that the transport system is not modified after storage of the formulation at $35^{\circ}C$ and $45^{\circ}C$. The mean values of the dissolution rate constant ranged from 0.036 to 0.043 $min^{-n}$, under all the conditions tested. Significant differences (${\alpha}=0.05$) were found between pHs 2, 4 and 6, 7.4 for all the model-independent parameters studied. When the formulation was kept at $35^{\circ}C$ for 16 months, the mean percentage of drug dissolved at 8 hours was 25.61% (pHs 2, 4) and, 36.12% (pHs 6, 7.4), representing a 26% and 24% reduction, respectively. Simitar results were obtained after storing the formulation at $45^{\circ}C$ for 8 months, corresponding to 33.3% (pHs 2, 4) and, 22.5% (pHs 6, 7.4) diminution, respectively. The values of the similarity factory $f_2$, obtained were lower than 50, which indicates the lack of similarity among the dissolution profiles, after storing the formulation under the experimental Conditions tested.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.87-92
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• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.1-27
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• 2012

Many companies have recently become interested in using social networking sites such as Twitter and Facebook as a new channel to communicate with their customers. For example, companies often offer "special deals" (e.g., coupons, discounts, free samples, etc.) to their customers who participate in promotions or events on social networking sites. Companies often make important announcements on their products or services on social networking sites. By doing so, customers are encouraged to continue to have relationships with companies on social networking sites and to recommend the companies' presence on social networking sites to other potential customers. Moreover, customers who keep close relationships with companies on social networking sites often provide the companies with valuable suggestions and feedback. For instance, Starbucks has more than 2 million followers on Twitter, and often receive suggestions and feedback for their product offerings and services from the followers on Twitter. Although companies realize potential benefits of using social networking sites as a channel to communicate with their customers, it appears that many companies have difficulty forging long-lasting relationships with customers on social networking sites. It is often reported that many customers who had followed companies on Twitter later stopped following them for various reasons. Therefore, it is an important issue to understand what motivates customers to continue to keep relationships with companies on social networking sites. Nonetheless, due attention has yet paid to this issue until recently. This study intends to contribute to our understanding on customers' intention to continue to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Specifically, we identify seven potential factors that customers perceive as important in evaluating their experience with companies on Twitter. The seven factors include similarity, receptivity, interactivity, ubiquitous connectivity, enjoyment, usefulness and transparency. We posit that the seven perception factors can affect the two types of satisfaction, emotional and cognitive, which can in turn influence on customers' intention to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Research hypotheses formulated in this study were tested with data collected from a questionnaire survey administered to customers who had been following companies on Twitter. The data was analyzed with the partial least square (PLS) approach to structural equation modeling. The results of data analysis based on 177 usable responses were generally supportive of our predictions for the effects of the seven factors identified and the two types of satisfaction. In particular, out results suggest that emotional satisfaction was strongly influenced by perceived similarity, perceived receptivity, perceived enjoyment, and perceived transparency. Cognitive satisfaction was significantly influenced by perceived similarity, perceived interactivity, perceived enjoyment, and perceived transparency. While cognitive satisfaction was found to have significant and positive effects on both continued following and word-of-mouth intentions, emotional satisfaction had a significant and positive effect only on word-of-mouth intention.