• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11B
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• pp.980-990
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• 2004

In this paper, we propose the dynamic bandwidth allocation algorithm for the IEEE802.3ah Ethernet Passive Optical Network(EPON) system to provide the fairness among terminals, and evaluate the delay-throughput performance by simulation. For the conventional EPON systems, an Optical Line Termination (OLT) schedules the upstream bandwidth for each Optical Network Unit (ONU), based on its buffer state. This scheme can provide a fair bandwidth allocation for each ONU. However, it has a critical problem that it does not guarantee the fair bandwidth among terminals which are connected to ONUs. For an example, we assume that the traffic from a greedy terminal increases at a time. Then, the buffer state of its ONU is instantly reported to the OLT, and finally the OW can get more bandwidth. As a result, the less bandwidth is allocated to the other ONUs, and thus the transfer delay of terminals connected to the ONUs gets inevitably increased. Noting that this unfairness problem exists in the conventional EPON systems, we propose a fair bandwidth allocation scheme by OLT with considering the buffer state of ONU as welt as the number of terminals connected it. For the performance evaluation, we develop the EPON simulation model with SIMULA simulation language. From the result of the throughput-delay performance and the dynamics of buffer state along time for each terminal and ONU, respectively, one can see that the proposed scheme can provide the fairness among not ONUs but terminals. Finally, it is worthwhile to note that the proposed scheme for the public EPON systems might be an attractive solution for providing the fairness among subscriber terminals.

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

• Tuberculosis and Respiratory Diseases
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• v.79 no.1
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• pp.14-21
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• 2016

Cough is one of the most common symptom of many respiratory diseases. The Korean Academy of Tuberculosis and Respiratory Diseases organized cough guideline committee and cough guideline was developed by this committee. The purpose of this guideline is to help clinicians to diagnose correctly and treat efficiently patients with cough. In this article, we have stated recommendation and summary of Korean cough guideline. We also provided algorithm for acute, subacute, and chronic cough. For chronic cough, upper airway cough syndrome (UACS), cough variant asthma (CVA), and gastroesophageal reflux disease (GERD) should be considered. If UACS is suspicious, first generation anti-histamine and nasal decongestant can be used empirically. In CVA, inhaled corticosteroid is recommended in order to improve cough. In GERD, proton pump inhibitor is recommended in order to improve cough. Chronic bronchitis, bronchiectasis, bronchiolitis, lung cancer, aspiration, angiotensin converting enzyme inhibitor, habit, psychogenic cough, interstitial lung disease, environmental and occupational factor, tuberculosis, obstructive sleep apnea, peritoneal dialysis, and idiopathic cough can be also considered as cause of chronic cough. Level of evidence for treatment is mostly low. Thus, in this guideline, many recommendations are based on expert opinion. Further study regarding treatment for cough is mandatory.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Tuberculosis and Respiratory Diseases
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• v.84 no.4
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• pp.263-273
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• 2021

Cough is the most common respiratory symptom that can have various causes. It is a major clinical problem that can reduce a patient's quality of life. Thus, clinical guidelines for the treatment of cough were established in 2014 by the cough guideline committee under the Korean Academy of Tuberculosis and Respiratory Diseases. From October 2018 to July 2020, cough guidelines were revised by members of the committee based on the first guidelines. The purpose of these guidelines is to help clinicians efficiently diagnose and treat patients with cough. This article highlights the recommendations and summary of the revised Korean cough guidelines. It includes a revised algorithm for the evaluation of acute, subacute, and chronic cough. For a chronic cough, upper airway cough syndrome (UACS), cough variant asthma (CVA), and gastroesophageal reflux disease (GERD) should be considered in differential diagnoses. If UACS is suspected, first-generation antihistamines and nasal decongestants can be used empirically. In cases with CVA, inhaled corticosteroids are recommended to improve cough. In patients with suspected chronic cough due to symptomatic GERD, proton pump inhibitors are recommended. Chronic bronchitis, bronchiectasis, bronchiolitis, lung cancer, aspiration, intake of angiotensin-converting enzyme inhibitor, intake of dipeptidyl peptidase-4 inhibitor, habitual cough, psychogenic cough, interstitial lung disease, environmental and occupational factors, tuberculosis, obstructive sleep apnea, peritoneal dialysis, and unexplained cough can also be considered as causes of a chronic cough. Chronic cough due to laryngeal dysfunction syndrome has been newly added to the guidelines.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Korean Journal of Community Nutrition
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• v.10 no.5
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• pp.592-602
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• 2005

This study was conducted to compare nutrient intakes regarding stages of change in dietary fiber increasing behavior. Subjects were consisted of healthy 383 college students (2S0 females and 133 males) in Kyunggi-Do. Stages of change classified by an algorithm based on 6 items were designed each subjects into one of the 5 stages: precontemplation (PC), contemplation (CO), preparation (PR), action (AC), maintenance (MA). Nutrient intakes were assessed by 24-hr recall method. Regarding the S stages of changes, PR stage comprised the largest group $(39.4\%)$, followed by AC $(33.7\%)$, MA$(14.6\%)$, PC$(7.6\%)$, CO$(34.7\%)$. Female were more belong to either AC or MA. The higher stage of change in dietary fiber increasing behavior, the higher self-efficacy. In all male and female, there were no differences in energy, protein, monounsaturated fatty acids, polyunsaturated fatty acids and cholesterol intakes across the 5 stages. But, fiber, postassuim (K), vitamin A and vitamin C intakes of AC or MA were higer than those of PC, CO and PR $Energy\%$ from fat of $PR(25.4\~26.5\%)$ was higher than $20\%$, and those of AC and MA was lower than the other groups. Dietary P/S and ${\varepsilon}6/{\varepsilon}$ 3 ratios of AC and MA were similar to the recommended ratio. Female of PR had the most total saturated fat and palmitic acid and those of MA had the least. Male of PR had the least $\alpha-LNA\;({\varepsilon}3)$ and total ${\varepsilon}3$ fatty acids and those of MA had the most. In male and female in AC or MA, fiber and K intakes from breakfast, dinner and snack and vitamin C intakes from all meals were higher than those of the other stages. These results of our study confirm differences in stages of change in fiber intake in terms of nutritional status. To have lower $energy\%$ from fat, higher intakes of K, fiber and vitamins, desirable ratio of dietary fatty acids, it needs consistent nutritional education leading to the AC or MA of fiber increasing behavior.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Management & Information Systems Review
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• v.36 no.4
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• pp.129-148
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• 2017

Conventional exhibition management information systems recommend tourist attractions that are close to the place in which an exhibition is held. Some recommended attractions by the location-based recommendation could be meaningless when nothing is related to the exhibition's topic. Our goal is to recommend attractions that are related to the content presented in the exhibition, which can be coined as content-based recommendation. Even though human exhibition curators can do this, the quality is limited to their manual task and knowledge. We propose an automatic way of discovering attractions relevant to an exhibition of interests. Language resources are incorporated to discover attractions that are more meaningful. Because a typical single machine is unable to deal with such large-scale language resources efficiently, we implemented the algorithm on top of Apache Spark, which is a well-known distributed computing framework. As a user interface prototype, a web-based system is implemented that provides users with a list of relevant attractions when users are browsing exhibition information, available at http://bike.snu.ac.kr/WARP. We carried out a case study based on Korean Tourism Organization Linked Open Data with Korean Wikipedia as a language resource. Experimental results are demonstrated to show the efficiency and effectiveness of the proposed system. The effectiveness was evaluated against well-known exhibitions. It is expected that the proposed approach will contribute to the development of both exhibition and tourist industries by motivating exhibition visitors to become active tourists.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.4
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• pp.299-307
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• 2018

Considering a nonlinear high power amplifier(HPA) and a predistorter, we have designed a four-dimensional 8-ary phase shift keying trellis-coded modulation(4D-8PSK-TCM) system, which is recommended for X-band satellite communications. Subsequently, we have evaluated and analyzed the spectrum, constellation characteristics, and BER performance of the system. In satellite communications, owing to the limited power, nonlinear characteristics that determine the operating point of the HPA must be analyzed because the HPA consumes high power. We herein report the design of the 4D-8PSK-TCM system, with efficiencies of 2 and 2.25 bits/channel-symbol. The simulation results confirmed that a 0.35 roll-off value is effective, considering the low peak-to-average power ratio(PAPR) characteristic and the narrow occupation bandwidth of the spectrum. It also confirmed that approximately 15~20 dB of output backoff(OBO) value is required at the HPA when the predistorter is not used, and approximately 1 dB of the OBO value is required when the predistorter is used.