• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.679-686
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• 2022

As the occurrences of flash floods have increased due to climate change, faster and more accurate precipitation observation using X-band radar has become important. Therefore, the Ministry of Environment installed two dual-pol X-band radars at Samcheok and Uljin. The radar data used in this study were obtained from two different elevation angles and composed to reduce the shielding effect. To obtain quantitative rainfall, quality control (QC), K_DP retrieval, and Hybrid Surface Rainfall (HSR) methods were sequentially applied. To improve the accuracy of the quantitative precipitation estimation (QPE) of the X-band radar, we retrieved parameters for the relationship between rainfall rate and specific differential phase, which is commonly called the R-K_DP relationship; hence, an empirical approach was developed using multiple rain gauges for those two radars. The newly suggested relationship, R = 27.4K^0.81_DP, slightly increased the correlation coefficient by 1% more than the relationship suggested by the previous study. The root mean square error significantly decreased from 3.88 mm/hr to 3.68 mm/hr, and the bias of the estimated precipitation also decreased from -1.72 mm/hr to -0.92 mm/hr for overall cases, showing the improvement of the new method.

• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Cartoon and Animation Studies
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• s.37
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• pp.509-537
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• 2014

This paper proposes 'postfilimc metamorphosis' and 'reanimation' as two concepts that aim at giving account to the aesthtetic tendencies and genealogies of what Suzanne Buchan calls 'pervasive animation', a category that refers to the unprecedented expansion of animation's formal, technological and experiential boundaries. Buchan's term calls for an interdisciplinary approach to animation by highlighting a range of phenomena that signal the growing embracement of the images and media that transcend the traditional definition of animation, including the lens-based live-action image as the longstanding counterpart of the animation image, and the increasing uses of computer-generated imagery, and the ubiquity of various animated images dispersed across other media and platforms outside the movie theatre. While Buchan's view suggests the impacts of digital technology as a determining factor for opening this interdisciplinary, hybrid fields of 'pervasive animation', I elaborate upon the two concepts in order to argue that the various forms of metamorphorsis and motion found in these fields have their historical roots. That is, 'postfilmic metamorphosis' means that the transformative image in postfimic media such as video and the computer differs from that in traditional celluloid-based animation materially and technically, which demands a refashioned investigation into the history of the 'image-processing' video art which was categorized as experimental animation but largely marginalized. Likewise, 'reanimation' cne be defined as animating the still images (the photographic and the painterly images) or suspending the originally inscribed movement in the moving image and endowing it with a neewly created movement, and both technical procedues, developed in experimental filmmaking and now enabled by a variety of moving image installations in contemporary art, aim at reconsidering the borders between stillness and movement, and between film and photography. By discussing a group of contemporary moving image artworks (including those by Takeshi Murata, David Claerbout, and Ken Jacobs) that present the aesthetic features of 'postfilmic metamorphosis' and 'reanimation' in relation to their precursors, this paper argues that the aesthetic implications of the works that pertain to 'pervasive animation' lie in their challenging the tradition dichotomies of the graphic/the live-action images and stillness/movement. The two concepts, then, respond to a revisionist approach to reconfigure the history and ontology of other media images outside the traditional boundaries of animation as a way of offering a refasioned understanding of 'pervasive animation'.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.109-119
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• 2016

The height datum of Korea is currently separated into land and sea, which makes it difficult to acquire homogeneous and accurate height information throughout the whole nation. In this study, we therefore tried to suggest the more effective way to transform the height information were constructed separately according to each height datum on land and sea to those on the unique height datum using precise geoid models and tidal observations in Korea. For this, Anmyeon island was selected as a study area to develop the precise geoid models based on the height datums land (IMSL) and sea (LMSL), respectively. In order to develop two hybrid geoid models based on each height datum of land an sea, we firstly develop a precise gravimetric geoid model using the remove and restore (R-R) technique with all available gravity observations. The gravimetric geoid model were then fitted to the geometric geoidal heights, each of which is represented as height datum of land or sea respectively, obtained from GPS/Leveling results on 15 TBMs in the study area. Finally, we determined the differences between the two hybrid geoid models to apply the height transformation between IMSL and LMSL. The co-tidal chart model of TideBed system developed by Korea Hydrographic and Oceanographic Agency (KHOA) which was re-gridded to have the same grid size and coverage as the geoid model, in order that this can be used for the height datum transformation from LMSL to local AHHW and/or from LMSL to local DL. The accuracy of height datum transformation based on the strategy suggested in this study was approximately ${\pm}3cm$. It is expected that the results of this study can help minimize not only the confusions on the use of geo-spatial information due to the disagreement caused by different height datum, land and sea, in Korea, but also the economic and time losses in the execution of coastal development and disaster prevention projects in the future.

• Korean Journal of Weed Science
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• v.30 no.4
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• pp.330-339
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• 2010

In order to suggest correct direction of researches on Miscanthus spp. which are promising bioenergy crop, authors had reviewed and summarized various literature about botanical taxonomy, morphology and present condition of breeding, cultivation and utilization of miscanthus. Among the genus of Miscanthus which are known 17 species, the most important species are M. sinensis and M. sacchariflorus which origin are East Asia including Korea, and M. x giganteus which is inter-specific hybrid of tetraploid M. sacchariflorus and diploid M. sinensis. Miscanthus is superior to other energy crops in resistance to poor environments including cold, saline and damp soil, nitrogen utilization efficiency, budget of input energy and carbon which are required for producing biomass and output which are stored in biomass. The major species for production of energy and industrial products including construction material in Europe, USA and Canada is M. x giganteus which was introduced from Japan in 1930s. In present, many breeding programs are conducted to supplement demerits of present varieties and to develop "Miscanes" which is hybrid of miscanthus and sugar cane. In Korea, the researches on breeding and cultivation of miscanthus were initiated in 2007 by collecting germplasms, and developed "Goedae-Uksae 1" which is high biomass yield and "mass propagation method of miscanthus" which can improve propagation efficiency in 2009. In order to develop "Korean miscanthus industry" in future, the superior varieties available not only domestic but also foreign market should be developed by new breeding method including molecular markers. Researches on production process of cellulosic bio-ethanol including pre-treatment and saccharification of miscanthus biomass also should be strengthen.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.181-203
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• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.223-226
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• 2006

It can formulate a basic concept to the study on styles of drawing on animation which is influenced by styles of drawing on comic. Especially the transferability of comic and eel animation have high possibility because they have common part as drawing, continuity of imagery and narrative, in spit of the differentiation with comic for spatial and animation for temporal. So the study on comic drawing styles are inevitable check point for investigating it's animation. To analyse of the style of animation drawing, can be employed the styles of comic drawing. For a case study, comic authors and their works is utilized for establishing basic concept of transferring comic to animation with the influence of drawing styles to animation and than it will apply to classify and analyse animation by them. The consequence of this study, it can be sorted in the three areas that are the cheerful comic, the drama, the boy-meets-girl comic. and can be proved that the close relationship of comic and animation. Among three aspects, a boy-meets-girl comic can not be detected in any selected animations. The presumption of this result that can be apply to the general idea which is a rare case to use in animation. Also to use partly cheerful comic to drama style as different drawing styles is for elucidating the effect of intending isolation with the play theory of Bertolt Brecht and empathizing dramatical situation through the disparity of emotion. This study is to investigate on the influence of the drawing style of comics in animation, and can provide the basic ideas of inter-relation of media through drawing styles study with the concept of hybrid.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.