• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Progress in Medical Physics
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• v.27 no.2
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• pp.64-71
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• 2016

We develop a manufacture procedure for the production of a patient specific customized bolus (PSCB) using a 3D printer (3DP). The dosimetric accuracy of the 3D-PSCB is evaluated for electron beam therapy. In order to cover the required planning target volume (PTV), we select the proper electron beam energy and the field size through initial dose calculation using a treatment planning system. The PSCB is delineated based on the initial dose distribution. The dose calculation is repeated after applying the PSCB. We iteratively fine-tune the PSCB shape until the plan quality is sufficient to meet the required clinical criteria. Then the contour data of the PSCB is transferred to an in-house conversion software through the DICOMRT protocol. This contour data is converted into the 3DP data format, STereoLithography data format and then printed using a 3DP. Two virtual patients, having concave and convex shapes, were generated with a virtual PTV and an organ at risk (OAR). Then, two corresponding electron treatment plans with and without a PSCB were generated to evaluate the dosimetric effect of the PSCB. The dosimetric characteristics and dose volume histograms for the PTV and OAR are compared in both plans. Film dosimetry is performed to verify the dosimetric accuracy of the 3D-PSCB. The calculated planar dose distribution is compared to that measured using film dosimetry taken from the beam central axis. We compare the percent depth dose curve and gamma analysis (the dose difference is 3%, and the distance to agreement is 3 mm) results. No significant difference in the PTV dose is observed in the plan with the PSCB compared to that without the PSCB. The maximum, minimum, and mean doses of the OAR in the plan with the PSCB were significantly reduced by 9.7%, 36.6%, and 28.3%, respectively, compared to those in the plan without the PSCB. By applying the PSCB, the OAR volumes receiving 90% and 80% of the prescribed dose were reduced from $14.40cm^3$ to $0.1cm^3$ and from $42.6cm^3$ to $3.7cm^3$, respectively, in comparison to that without using the PSCB. The gamma pass rates of the concave and convex plans were 95% and 98%, respectively. A new procedure of the fabrication of a PSCB is developed using a 3DP. We confirm the usefulness and dosimetric accuracy of the 3D-PSCB for the clinical use. Thus, rapidly advancing 3DP technology is able to ease and expand clinical implementation of the PSCB.

• The Journal of Dong Guk Oriental Medicine
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• v.4
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• pp.109-140
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• 1995

As we considered in the main subjects, investigations on the theory of 'Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' through 'Wang Bing's Commentary(王氷 注本)' of 'The seven great chapters in The Yellow Emperor's Internal Classic Su Wen' ("黃帝內經素問 七篇大論") are as follows. (1) In The seven great chapters("七篇大論")' Wang Bing supplement theory and in the academic aspects as a interpreter, judging from 'forget(亡)' character. expressed in the 'The missing chapters("素問遺篇")', 'Bonbyung-ron("本病論")' and 'Jabeob-ron(刺法論)', 'The seven great chapters("七篇大論")' must be supplementary work by Wang Bing. Besides, he quoted such forty books as medical books, taoist books, confucianist books, miscellaneous books, etc in the commentary and the contents quoted in the 'Su Wen(素問)' and 'Ling Shu("靈樞")' scripture nearly occupy in the book. As a method of interpreting scripiure as scripture, he edited the order of 'Internal Classic("內經")' ascended from the ancient time and when he compensated for commentary, with exhaustive scholarly mind and by observing the natural phenomena practically and writing the pathology and the methods of treatment. We knew that the book is combined with the study of 'Doctrine on five elements motion and six kinds of natural factors(運氣學說)' (2) When we compare, analyze the similar phrase of 'The seven great chapters in The Yellow Emperor's Internal Classic Su Wen'("黃帝內經素問ㆍ七篇大論") through 'Wang Bing's Commentary(王氷 注本)', he tells abouts organized 'five elements(五行)' and 'heaven's regularly movement(天道運行)' rather than 'Emyangengsangdae-ron("陰陽應象大論")' in 'The seven great chapters("七篇大論")'. Also the 'Ohanunhangdae-ron("五運行大論")' because the repeated sentences with 'Emyangengsangdae-ron("陰陽應象大論")' is long they are omitted. And in the 'Youkmijidae-ron("六微旨大論")', 'Cheonjin ideology(天眞四象)' based on the 'Sanggocheonjin- ron("上古天眞論")', 'Sagijosindae-ron("四氣調神大論")' is written and in the 'Gigoupyondae-ron("氣交變大論")', the syndrome and symptom are explained in detail rather than 'Janggibeobsi-ron("藏氣法時論")', 'Okgijinjang-ron ("玉機眞藏論")' and in the 'Osangieongdae-ron("五常政大論")', the concept of 'five element(五行)' of the 'Gemgwejineon-ron("金櫃眞言論")' is expanded to 'the five elements' motion concept(五運槪念)' and in the 'Youkwonjeonggidae-ron("六元正紀大論")', explanations of 'The five elements' motion and six kinds of natural factors(運氣)' function are mentioned mainly and instead systematic pathology is not revealed rather than 'Emyangengsangdae-ron("陰陽應象大論")'. And in the 'Jijinyodae-ron("至眞要大論")', explanations of the change of atmosphere which correspond to treatment principle by 'The three Yin and Yang(三陰三陽)' as a progressed concepts are revealed. Therefore there are much similarity between the phrase of 'Emyangengsangdae-ron("陰陽應象大論")' and 'chapters of addition(補缺之篇)'. Generally, the doctrine which 'The seven great chapters("七篇大論")' are added by Wang Bing(王氷) is supported because there are more profound concepts rather than the other chapter in 'The seven great chapters("七篇大論")'. (3) When we study Wang Bing's(王氷) 'Pattern on five elements motion and six kinds of natural factors(運氣格局)' in 'The seven great chapter("七篇大論")', in the 'Cheonwongi-dae-ron("天元紀大論")', With 'Cheonjin ideology(天眞思想)' and the concepts of 'Owang(旺)'${\cdot}$'Sang(相)'${\cdot}$'Sa(死)'${\cdot}$'Su(囚)'${\cdot}$'Hu(休)' and 'Cheonbu(天符)'${\cdot}$'Sehwoi(歲會)' are measured time-spacially to the concept of 'Three Sum(三合)' the concept of 'Taeulcheonbu(太乙天符)' is explained. In the 'Ounhangdae-ron("五運行大論")', 'The calender Signs five Sum(天干五合)' is compared to the concepts of 'couples(夫婦)', 'weak-strong(柔强)' and in the 'Youkmijidae-ron("六微旨大論")', 'the relationship of obedience and disobedience(順逆關係)' which conform to the 'energy status(氣位)' change and 'monarch-minister(君相)' position is mentioned. In the 'Gikyobyeondae-ron("氣交變大論")', the concept of 'Sang-duk(相得)', 'Pyungsang(平常)' is emphasized but concrete measurement is mentioned. In the 'Osangieongdae-ron("五常政大論")', the detailed explanation with twenty three 'systemic of the five elements' motion(五運體系)' form and 'rountine-contrary treatment(正治. 反治)' with 'chill-fever-warm-cold(寒${\cdot}$熱${\cdot}$溫${\cdot}$凉)' are mentioned according to the 'analyse and differentiate pathological conditions in accordance with the eight principal syndromes(八綱辨證)'. In the 'Youkwonjeonggidae-ron("六元正紀大論")', Wang Bing of doesn't mention the concepts of 'Jungwun(中運)' that is seen in the original classic. In the new corrective edition, as the concepts of 'Jungwun, Dongcheonbu, Dongsehae and Taeulcheonbu(中運, 同天符, 同歲會, 太乙天符)' is appeared, Wang Bing seems to only use the concepts of 'Daewun, Juwun, and Gaekwun(大運, 主運, 客運)'. In the 'Jijinyodaeron("至眞要大論")', Wang Bing added detailed commentary to pathology and treatment doctrine by explaining the numerous appearances of 'Sebo, sufficiency, deficiency(歲步, 有餘, 不足)' and in the relation of 'victory-defeat(勝復)', he argued clearly that it is not mechanical estimation. (4) When we observe the Wang Bing's originality on the study of 'the theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)', he emphasized 'The idea of Jeongindogi and Health preserving(全眞導氣${\cdot}$養生思想)' by adding 'Wang Bing's Commentary(王氷 注本)' of 'The seven great chapters("七篇大論")' and explained clearly 'The theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' and simpled and expanded the meaning of 'man, as a microcosm, is connected with the macrocosm(天人相應)' and with 'Atmosphere theory(大氣論)' also explained the meaning of 'rising and falling mechanism(升降氣機)'. In the sentence of 'By examining the pathology, take care of your health(審察病機 無失氣宜)'. he explained the meaning of pathology of 'heart-kidney-water-fire(心腎水火)' and suggested the doctrine and management of prescription. In the estimation and treatment, by suggesting 'asthenia and sthenia(虛實)' two method's estimation, 'contrary treatment(反治)' and treatment principals of 'falling heart fire tonifyng kidney water(降心火益腎水)', 'two class of chill and fever(寒熱二綱)' were demonstrated. There are 'inside and outside in the illness and so inner and outer in the treatment(病有中外 治有表囊)'. This sentence suggests concertedly. 'two class of superfies and interior(表囊二綱)' conforming to the position of disease. Therefore Wang Bing as an excellent theorist and introduced 'Cheoniin ideology(天眞思想)' as a clinician and realized the medical science. With these accomplishes mainly written in 'The theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' of 'The seven great chapters("七篇大論")', he interpreted the ancient medical scriptures and expanded the meaning of scriptures and conclusively contributed to the development of the study 'Korean Oriental Medicine(韓醫學)'.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.21-37
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• 2013

Recently, as the advent of the knowledge-based society, there are more people getting interested in the intellectual property. Especially, the ICT companies leading the high-tech industry are working hard to strive for systematic management of intellectual property. As we know, the patent information represents the intellectual capital of the company. Also now the quantitative analysis on the continuously accumulated patent information becomes possible. The analysis at various levels becomes also possible by utilizing the patent information, ranging from the patent level to the enterprise level, industrial level and country level. Through the patent information, we can identify the technology status and analyze the impact of the performance. We are also able to find out the flow of the knowledge through the network analysis. By that, we can not only identify the changes in technology, but also predict the direction of the future research. In the field using the network analysis there are two important analyses which utilize the patent citation information; citation indicator analysis utilizing the frequency of the citation and network analysis based on the citation relationships. Furthermore, this study analyzes whether there are any impacts between the size of the company and patent citation relationships. 74 S&P 500 registered companies that provide IT and communication services are selected for this study. In order to determine the relationship of patent citation between the companies, the patent citation in 2009 and 2010 is collected and sociomatrices which show the patent citation relationship between the companies are created. In addition, the companies' total assets are collected as an index of company size. The distance between companies is defined as the absolute value of the difference between the total assets. And simple differences are considered to be described as the hierarchy of the company. The QAP Correlation analysis and MR-QAP analysis is carried out by using the distance and hierarchy between companies, and also the sociomatrices that shows the patent citation in 2009 and 2010. Through the result of QAP Correlation analysis, the patent citation relationship between companies in the 2009's company's patent citation network and the 2010's company's patent citation network shows the highest correlation. In addition, positive correlation is shown in the patent citation relationships between companies and the distance between companies. This is because the patent citation relationship is increased when there is a difference of size between companies. Not only that, negative correlation is found through the analysis using the patent citation relationship between companies and the hierarchy between companies. Relatively it is indicated that there is a high evaluation about the patent of the higher tier companies influenced toward the lower tier companies. MR-QAP analysis is carried out as follow. The sociomatrix that is generated by using the year 2010 patent citation relationship is used as the dependent variable. Additionally the 2009's company's patent citation network and the distance and hierarchy networks between the companies are used as the independent variables. This study performed MR-QAP analysis to find the main factors influencing the patent citation relationship between the companies in 2010. The analysis results show that all independent variables have positively influenced the 2010's patent citation relationship between the companies. In particular, the 2009's patent citation relationship between the companies has the most significant impact on the 2010's, which means that there is consecutiveness regarding the patent citation relationships. Through the result of QAP correlation analysis and MR-QAP analysis, the patent citation relationship between companies is affected by the size of the companies. But the most significant impact is the patent citation relationships that had been done in the past. The reason why we need to maintain the patent citation relationship between companies is it might be important in the use of strategic aspect of the companies to look into relationships to share intellectual property between each other, also seen as an important auxiliary of the partner companies to cooperate with.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.147-157
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• 2013

With the advent of the digital environment that can be accessed anytime, anywhere with the introduction of high-speed network, the free distribution and use of digital content were made possible. Ironically this environment is raising a variety of copyright infringement, and product images used in the online shopping mall are pirated frequently. There are many controversial issues whether shopping mall images are creative works or not. According to Supreme Court's decision in 2001, to ad pictures taken with ham products is simply a clone of the appearance of objects to deliver nothing but the decision was not only creative expression. But for the photographer's losses recognized in the advertising photo shoot takes the typical cost was estimated damages. According to Seoul District Court precedents in 2003, if there are the photographer's personality and creativity in the selection of the subject, the composition of the set, the direction and amount of light control, set the angle of the camera, shutter speed, shutter chance, other shooting methods for capturing, developing and printing process, the works should be protected by copyright law by the Court's sentence. In order to receive copyright protection of the shopping mall images by the law, it is simply not to convey the status of the product, the photographer's personality and creativity can be recognized that it requires effort. Accordingly, the cost of making the mall image increases, and the necessity for copyright protection becomes higher. The product images of the online shopping mall have a very unique configuration unlike the general pictures such as portraits and landscape photos and, therefore, the general image watermarking technique can not satisfy the requirements of the image watermarking. Because background of product images commonly used in shopping malls is white or black, or gray scale (gradient) color, it is difficult to utilize the space to embed a watermark and the area is very sensitive even a slight change. In this paper, the characteristics of images used in shopping malls are analyzed and a watermarking technology which is suitable to the shopping mall images is proposed. The proposed image watermarking technology divide a product image into smaller blocks, and the corresponding blocks are transformed by DCT (Discrete Cosine Transform), and then the watermark information was inserted into images using quantization of DCT coefficients. Because uniform treatment of the DCT coefficients for quantization cause visual blocking artifacts, the proposed algorithm used weighted mask which quantizes finely the coefficients located block boundaries and coarsely the coefficients located center area of the block. This mask improves subjective visual quality as well as the objective quality of the images. In addition, in order to improve the safety of the algorithm, the blocks which is embedded the watermark are randomly selected and the turbo code is used to reduce the BER when extracting the watermark. The PSNR(Peak Signal to Noise Ratio) of the shopping mall image watermarked by the proposed algorithm is 40.7~48.5[dB] and BER(Bit Error Rate) after JPEG with QF = 70 is 0. This means the watermarked image is high quality and the algorithm is robust to JPEG compression that is used generally at the online shopping malls. Also, for 40% change in size and 40 degrees of rotation, the BER is 0. In general, the shopping malls are used compressed images with QF which is higher than 90. Because the pirated image is used to replicate from original image, the proposed algorithm can identify the copyright infringement in the most cases. As shown the experimental results, the proposed algorithm is suitable to the shopping mall images with simple background. However, the future study should be carried out to enhance the robustness of the proposed algorithm because the robustness loss is occurred after mask process.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.35 no.2
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• pp.91-101
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• 2009

in the cosmetics and medical supply field as a antioxidant material. The stable nano particle emulsion of skin toner type containing VEA was prepared. To evaluate the skin permeation, experiments on VEA permeation to the skin of the ICR outbred albino mice (12 weeks, about 50 g, female) and on differences of solubility as a function of receptor formulations was performed. The analysis of nano-emulsions containing VEA 0.07 % showed that the higher ethanol contents the larger emulsions were formed, while the higher surfactant contents the size became smaller.In this study, vitamin E acetate (VEA, tocopheryl acetate), a lipid-soluble vitamin which is widely used A certain contents of ethanol in receptor phase increased VEA solubility on the nano-emulsion. When the ethanol contents were 10.0 % and 20.0 %, the VEA solubility was higher than 5.0 % and 40.0 %, respectively. The type of surfactant in receptor solution influenced to VEA solubility. The comparison between three kind surfactants whose chemical structures and HLB values are different, showed that solubility of VEA was increased as order of sorbitan sesquioleate (Arlacel 83; HLB 3.7) > POE (10) hydrogenated castor oil (HCO-10; HLB 6.5) > sorbitan monostearate (Arlacel 60; HLB 4.7). VEA solubility was also shown to be different according to the type of antioxidant. In early time, the solubility of the sample including ascorbic acid was similar to those of other samples including other types of antioxidants. However, the solubility of the sample including ascorbic acid was 2 times higher than others after 24 h. Franz diffusion cell experiment using mouse skin was performed with four nano-emulsion samples which have different VEA contents. The emulsion of 10 wt% ethanol was shown to be the most permeable at the amount of 128.8 ${\mu}g/cm^2$. When the result of 10 % ethanol content was compared with initial input of 220.057 ${\mu}g/cm^2$, the permeated amount was 58.53 % and the permeated amount at 10 % ethanol was higher 45.0 % and 15.0 % than the other results which ethanol contents were 1.0 and 20.0 wt%, respectively. Emulsion particle size used 0.5 % surfactant (HCO-60) was 26.0 nm that is one twentieth time smaller than the size of 0.007 % surfactant (HCO-60) at the same ethanol content. Transepidermal permeation of VEA was 54.848 ${\mu}g/cm^2$ which is smaller than that of particlesize 590.7 nm. Skin permeation of nano-emulsion containing VEA and difference of VEA solubility as a function of receptor phase formulation were determined from the results. Using these results, optimal conditions of transepidermal permeation with VEA were considered to be set up.