• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3139-3145
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• 2016

Cancer is the leading cause of morbidity and mortality worldwide, characterized by irregular cell growth. Cytotoxicity or killing tumor cells that divide rapidly is the basic function of chemotherapeutic drugs. However, these agents can damage normal dividing cells, leading to adverse effects in the body. In view of great advances in cancer therapy, which are increasingly reported each year, we quantitatively and qualitatively evaluated the papers published between 1981 and December 2015, with a closer look at the highly cited papers (HCPs), for a better understanding of literature related to cytotoxicity in cancer therapy. Online documents in the Web of Science (WOS) database were analyzed based on the publication year, the number of times they were cited, research area, source, language, document type, countries, organization-enhanced and funding agencies. A total of 3,473 publications relevant to the target key words were found in the WOS database over 35 years and 86% of them (n=2,993) were published between 2000-2015. These papers had been cited 54,330 times without self-citation from 1981 to 2015. Of the 3,473 publications, 17 (3,557citations) were the most frequently cited ones between 2005 and 2015. The topmost HCP was about generating a comprehensive preclinical database (CCLE) with 825 (23.2%) citations. One third of the remaining HCPs had focused on drug discovery through improving conventional therapeutic agents such as metformin and ginseng. Another 33% of the HCPs concerned engineered nanoparticles (NPs) such as polyamidoamine (PAMAM) dendritic polymers, PTX/SPIO-loaded PLGAs and cell-derived NPs to increase drug effectiveness and decrease drug toxicity in cancer therapy. The remaining HCPs reported novel factors such as miR-205, Nrf2 and p27 suggesting their interference with development of cancer in targeted cancer therapy. In conclusion, analysis of 35-year publications and HCPs on cytotoxicity in cancer in the present report provides opportunities for a better understanding the extent of topics published and may help future research in this area.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.439-447
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• 2011

In this paper, we present a novel method to use metadata based on the eXtensible Markup Language (XML) for efficient data transfer between visual effects (VFX) and film editing. For transferring data to the VFX process, image sequences such as Cineon DPX or TGA are currently used in Korean post productions. The use of image sequences tends to increase rendering time and the amount of data for repetitive file format conversions, and as a result causes inefficiency in the entire production process. On the other hand, the use of metadata on the XML can reduce time and data because the repetitive rendering processes are not needed. This method can also be used at a variety of editing and VFX programs, and provides content information for combining online contents. The development of XML-base methods on the web enables flexible combination with other types of media.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.258-266
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• 2011

AR(Augmented Reality) technology is now easily shown around us with respect to its applicable areas' being spreaded into various shapes since the usage is simply generalized and many-sided. Currently existing camera vision based AR used marker based methods rather than using real world's informations. For the marker based AR technology, there are limitations on applicable areas and its environmental properties that a user could immerse into the usage of application program. In this paper, we proposed a novel AR method which users could recognize objects from the real world's data and the related 3-dimensional contents are also displayed. Those are done using image processing skills and a smart mobile embedded camera for terminal based AR implementations without any markers. Object recognition is done from the comparison of pre-registered and referenced images. In this process, we tried to minimize the amount of computations of similarity measurements for improving working speed by considering features of smart mobile devices. Additionally, the proposed method is designed to perform reciprocal interactions through touch events using smart mobile devices after the 3-dimensional contents are displayed on the screen. Since then, a user is able to acquire object related informations through a web browser with respect to the user's choice. With the system described in this paper, we analyzed and compared a degree of object recognition, working speed, recognition error for functional differences to the existing AR technologies. The experimental results are presented and verified in smart mobile environments to be considered as an alternate and appropriate AR technology.

• International Journal of Contents
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• v.11 no.2
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• pp.37-49
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• 2015

Sensors, equipment, ICT facilities and their corresponding software have a relatively short lifetime relative to that of constructional structure, so these devices have to be continuously fixed or exchanged during maintenance and management. Furthermore, software or analysis tools should be periodically upgraded according to advances in ICT and analysis technology. Conventional monitoring systems have serious problems in that it is difficult for site engineers to modify or upgrade hardware and analysis algorithms. Moreover, we depend on the original system developer when we want to modify or upgrade inner program structures. In this paper, we propose a novel design for integrated maintenance and management of a monitoring system by applying the mobile cloud concept. The system is intended for use in disaster prevention of constructional structures, including bridges, tunnels, and in traditional buildings in a local heritage village, we analyze the status of these structures over a long term or a short-term period as well as in disaster situations. Data are collected over a mobile cloud and future expectations are analyzed according to probabilistic and statistical techniques. We implement our integrated monitoring system to solve the existing problems mentioned above. The final goal of this study is to design and implement a monitoring system for more than 10,000 structures spread within Korea. Furthermore, we can specifically apply the monitoring system presented here to a bridge made from timber in Asan Oeam Village and a traditional house in Andong Hahoe Village to monitor for possible disasters. The entire system design and implementation can be developed on the LinkSaaS platform and the monitoring services can also be implemented on the platform. We prove that the proposed system has good performance by performing a TTA authentication test, web accommodation test, and operation test using emulated data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.11-21
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• 2010

To aim at improving performance and reflecting user's needs of retrieval, the number of researches has been actively conducted in recent year as the quantity of information and generation of the web pages exceedingly increase. One of alternative approaches can be a tagging system. It makes users be able to provide a representation of metadata including writings, pictures, and movies etc. called tag and be convenient in use of retrieval of internet resources. Tags similar to keywords play a critical role in maintaining target pages. However, they still needs time consuming labors to annotate tags, which sometimes are found to be a hinderance caused by overuse of tagging. In this paper, we present an automatic tagging scheme for a solution of current tagging system conveying drawbacks and inconveniences. To realize the approach, face recognition-based tagging system on SNS is proposed by building a face area detection procedure, linear-based classification and boosting algorithm. The proposed novel approach of tagging service can increase possibilities that utilized SNS more efficiently. Experimental results and performance analysis are shown as well.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.25-38
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• 2013

Future wireless systems will be required to support the increasingly nomadic lifestyle of people. This support will be provided through the use of multiple overlaid networks which have very different characteristics. Moreover, these networks will be required to support the seamless delivery of today's popular desktop services, such as web browsing, interactive multimedia and video conferencing to the mobile devices. Thus one of the major challenges in the design of these mobile systems will be the provision of the quality of service (QoS) guarantees that the applications demand under this diverse networking infrastructure. We believe that it is necessary to use resource reservation and adaptation techniques to deliver these QoS guarantee to applications. However, reservation and pre-configuration in the entire service region is overly aggressive, and results in schemes that are extremely inefficient and unreliable. To overcome this, the mobility pattern of a user can be exploited. If the movement of a user is known, the reservation and configuration procedure can be limited to the regions of the network a user is likely to visit. Our proposed Proxy-UMP is not sensitive to increase of the search cost than other schemes and shows that the increasing rate of total cost is low as the SMR increases.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.99-132
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• 2022

The purpose of this study is to propose AET (Augmented and Extended Topics), a novel method of synthesizing both LDA and BERTopic results, and to analyze the recently published LIS articles as an experimental approach. To achieve the purpose of this study, 55,442 abstracts from 85 LIS journals within the WoS database, which spans from January 2001 to October 2021, were analyzed. AET first constructs a WORD2VEC-based cosine similarity matrix between LDA and BERTopic results, extracts AT (Augmented Topics) by repeating the matrix reordering and segmentation procedures as long as their semantic relations are still valid, and finally determines ET (Extended Topics) by removing any LDA related residual subtopics from the matrix and ordering the rest of them by F₁ (BERTopic topic size rank, Inverse cosine similarity rank). AET, by comparing with the baseline LDA result, shows that AT has effectively concretized the original LDA topic model and ET has discovered new meaningful topics that LDA didn't. When it comes to the qualitative performance evaluation, AT performs better than LDA while ET shows similar performances except in a few cases.

• Clinical and Experimental Pediatrics
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• v.52 no.11
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• pp.1249-1259
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• 2009

Purpose:From a societal perspective, we evaluated the cost-effectiveness of a novel sustained-release injection of recombinant human growth hormone (GH) administered on a weekly basis compared with that of the present daily GH injection for the treatment of children with GH deficiency. Methods:Health-related utility for GH therapy was measured based on the visual analogue scale. During July 2008, caregivers of 149 children receiving GH therapy form 2 study sites participated in a web-based questionnaire survey. The survey required the caregivers to rate their current subjective utility with daily GH injections or expected utility of weekly GH injections. Because there was no difference in the costs of the daily and weekly therapies, for the purposes of this study, only drug acquisition costs were considered. Results:Switching from daily to weekly injection of GH increased the utility from 0.584 to 0.784 and incurred an extra cost of 4,060,811 Korean won (KW) per year. The incremental cost-utility ratio (ICUR) for a base case was 20,305,055 KW per quality-adjusted life year (QALY) gained. Scenario analyses showed that the ICUR ranged from 15,751,198 to 25,489,929 KW per QALY. Conclusion:The ICUR for a base case and worst case scenario analyses ranged from 0.85 to 1.37-times per capita gross domestic product of Korea, which is considered to be within the generally accepted willingness-to-pay threshold. Thus, it is concluded that switching from daily to weekly injection of GH would be cost-effective.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.