• Journal of the Korea Society of Computer and Information
• /
• v.15 no.10
• /
• pp.113-120
• /
• 2010

Video proxy server which is located near clients can store the frequently requested video data in storage space in order to minimize initial latency and network traffic significantly. However, due to the limited storage space in video proxy server, an appropriate video selection method is needed to store the videos which are frequently requested by users. Thus, we present a time constraint multiple access technique to efficiently store the video in video proxy server. If the video is requested by user, it is temporarily stored during the predefined interval and then, delivered to the user. A video which is stored is deleted or moved into the storage space of video proxy server depending on the request condition. In addition, we propose a video deletion method in video proxy server for newly stored video data. The simulation results show that the proposed method performs better than other methods in terms of the block hit rate and the number of block deletion.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.700-705
• /
• 2020

Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively.

• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.127-141
• /
• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.7 no.5
• /
• pp.418-429
• /
• 1996

The number of bits per message and the number of tones in the frequency-hopping sequence are determined by the available bandwidth and the data rate of each user. These parameters in turn determine the tone duration which strongly influences the vulnerability of the system to transmission distortions. In this paper, an address code which is assigned to each individual user was employed in order to reduce the collisions or hit. Also the frequency band is divided into several subbands and each user transmits multitone frequency per subband per chip. And the new system which is to increase the duration of each tone by increasing the total number of system frequencies that has been proposed. It is found that an optimum value bit, tone, number of frequencies per chirp can improve the err performance. This flexibility slightly increases maximum efficiecy and makes the the system less vulnerable to multipath delay. So, It is found that as the nuber of user increased 50%, the efficiency as a tuncion of the bandwidth to user'rate ratio improve 20%.

• Korean Journal of Applied Biomechanics
• /
• v.23 no.2
• /
• pp.117-123
• /
• 2013

The purpose of this study was to provide data to increase the success rate of penalty kicks through quantifying the shape of skilled kicks by performing a kinematic analysis on the change of movement during the kicking phase which the goalkeeper uses as a vital clue. Three high definition video cameras(GR-HD1KR, JVC, Japan) were used for the study and 18 reflective markers were attached to the body joints. Corners of the goal, difficult for goalkeepers to block, were set as aims and 1 m by 1.2 m targets were installed. Each subject had five sets of kicks at random, and the analysis was done on the movements that hit the target. Time, speed of the right lower limb's center of mass, joint angle, and angular velocity were chosen as factors and the results of the analysis showed statistical significance. The player taking a penalty kick should train to avoid leaning one's body towards the kicking direction and change the angle of the right foot right before the impact to decide the direction of the ball. The goalkeeper can increase the save success rate by studying the angle of the kicker's body and the right foot as well as the timing of the kick.

• Smart Media Journal
• /
• v.12 no.8
• /
• pp.18-25
• /
• 2023

The COVID-19 outbreak in 2020 caused continuous damage worldwode, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are colsed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administraion and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of the Korean Society of School Health
• /
• v.22 no.2
• /
• pp.157-167
• /
• 2009

Purpose : The purpose of this study is to explore the students injuries by analyzing the data which has been inputted by the emergency center of the cooperated hospitals and the 119 rescue party through the injury surveillance system in Cheonan city. Method : Students were divided into the elementary, middle, high school students with the 776 cases of children and teenagers(7-19years old) of injury surveillance system in Cheonan area from january to june in 2009. Frequency analysis and $x^2$-test was done to recognize the features of students injuries among the groups. The program to be used for the statistical analysis is SPSS 17.0. Result : Out of the injury incidence rate, the elementary school students(52.1%) are first, the high school students (24.9%) are second, the middle school students appear to be 23.1%. Male is about two times higher than female by 66.6% in the injury incidence. In terms of the injury mechanism, the injury(22.2%) by hit is the first, the traffic accident(21.5%) is the second, the slippery(16.8%) is followed. The injuries were occurred most largely at 16:00-20:00(33.4%), and the 33.6% of injury by daily leisure activity occurs at 16:00-20:00 chiefly. Conclusion : Analysis using the data of the injury surveillance system has some advantages compared to the previous research such as reliability and specification. To prevent the students injuries, not the individual problem but the social dimension should be acknowledged so that we can secure and promote the safety from the risk. Therefore, we must organize the role assignment and the cooperative network in the school, home and community.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.6
• /
• pp.57-64
• /
• 2010

Due to the limited storage space in video proxy server, it is often required to replace the old video data which is not serviced for long time with the newly requested video. This replacement causes the service delay and increase of network traffic. To circumvent this problem, we propose the an efficient replacement scheme in a video proxy server. In addition, we present a video data management technique for decreasing the number of replacement in video proxy server. For this purpose, we employ a memory in video proxy server. If the video segment which is loaded in memory is requested once again by a different user, this segment is resided in memory. The video in the memory is stored in the video proxy server depending on the consuming pattern by users. The simulation results show that the proposed algorithm performs better than other algorithms in terms of packet hit rate and number of packet replacement.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3627-3641
• /
• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.