• Journal of Gifted/Talented Education
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• v.19 no.3
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• pp.603-646
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• 2009

This paper explores in depth Gaston Bachelard's theory of imagination so as to establish the philosophical bases of creativity. While he had begun his studies on imagination to eliminate unreliable subjectivity hampering objectivity of philosophy of science, he was fascinated to become a philosopher of imagination by its unlimited power. Since his theory of imagination marked a prominent spot in the history of Western idea, this paper will seek its significant implications that will also shed light on the philosophical grounds of creativity. The best way to approach his theory is to differentiate whether imagination is the power of forming images or that of transforming them. If not misguided by surface simplicity of the aforementioned differentiation, it will be revealed that it has accrued strata in the history of Western idea. The power of forming images is related to theory of mimesis or of representation, and to ocularcentric and logo-centric structures. Bachelard strongly opposes to the theory of imagination as power of forming images, since, if it is so, its expansion and development are not possible. He thereby constructs the theory of imagination as power of transforming images. The force of movement lies at the core of his theory. Imagination as an ability to intuit movement is directly related to the problem of change in the history of Western idea. If an entity is incessantly changes itself, it becomes a crucial role of imagination to capture the force perse in the perpetual movement without distortedly and abruptly fixing it at a still point of time and space. Bachelard criticizes such a method that makes movement a controllable entity consisting of partitioned moments of space; instead, he constructs theory of imagination that reveals the true power of indispensable movement. Furthermore, it will be revealed that Bachelard's theory has more affinities with Kantian imagination and reflective judgement of aesthetics than the past researches on Bachelard showed. This paper, by means of the above investigation, will transcend the superficiality of defining what are Bachelard's formal, material, and dynamic imaginations; simultaneously, it will bear philosophical conditions of possibility that makes us experience imagination fully. These conditions also become the philosophical foundations of creativity. It will draw to a provisional close its imaginative journey of everlasting movement by making ontological and ethical dimensions of imagination and creativity.

• Journal of Korean Society of Forest Science
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• v.80 no.1
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• pp.32-41
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• 1991

This study is focusted to the national park of Korean typical mountain Chi-ri, its visual resources of forests, and practiced inspectind course by way of hypothesis and tests, to show visual resource management objectively, and that of quantitative basic data. spatial image structure measured by Semantic Differential(S.D.) Scale was shown through factor analysis algorithm for the analyzing psychological amount and examined the flowing out of decisive factor and the objective importance related mutual factors by appling the measurement of visual quality. Also, it has been investigated the differences between the degrees of expectation which is used before and of satisfaction which is used, by appling instrumental expectation theory. And showed patterns of investigation area through factor analyzing algorithm. As a national Park, visual factors that have natural landscape harmonized forest, sky, surface of the water, curious stones and rocks, temples should be escalate their values affirmatively so as to be the scenery of pointed direction and enjoyable, and it is needs the techniques visual resource management and its controlling technique to make artificial structures more intentional planning and systemical setting. Manmade distinctive quality in the spatials that affect complementally or harmonizingly, should be received considering relations between the distifution and joining which in line with various visual presenting massive factors. More than that, it is needed united basic standard to the beauty of arrangements which contain visual continuity considered balance between nature and human work, simplicity of structural types, assimilation, emphasis, unification of different factors and pro rata.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.3
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• pp.385-392
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• 2011

Although monoculture methods have been remarkably useful due to their simplicity, they have serious limitation because of the different types of cells communication with each other in many physiological situations. We demonstrated levels of markers of endothelial dysfunction such as tumor necrosis factor-$\alpha$ (TNF-$\alpha$) and interleukin-1$\beta$ (IL-1$\beta$) as well as stimulation of receptor of advanced glycation endproducts (AGEs) on monoand co-culture system such as only monocyte (THP-1) cultivation system, only endothelial cell (HUVEC) cultivation system, and co-cultivation system of THP-1 and HUVEC. The mRNA levels of TNF-$\alpha$ and IL-1$\beta$ on HUVEC increased by the co-culture with monocyte after 4 hr at 100 ${\mu}g/mL$ glyceraldehyde-AGE. The secreted protein contents into medium of TNF-$\alpha$ and IL-1$\beta$ increased after 8 hr approximately 2~2.5 times compared to mono-cultivation. In contrast, the mRNA level of receptor of AGE (RAGE) was relatively insensitive on the co-culture system. The mediators by which monocytes activate endothelial cell have not been fully elucidated. In this study we confirmed production of soluble cytokines such as TNF-$\alpha$ and IL-1$\beta$ by monocytes. Use of monocyte conditioned medium, which contains both cytokines, can activate endothelial cell.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Computational Structural Engineering
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• v.7 no.3
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• pp.95-103
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• 1994

The safety related components in the nuclear power plant should be designed to withstand the seismic load. Among these components the integrity of reactor internals under earthquake load is important in stand points of safety and economics, because these are classified to Seismic Class I components. So far the modelling methods of reactor internals have been investigated by many authors. In this paper, the dynamic behaviour of reactor internals of Yong Gwang 1&2 nuclear power plants under SSE(Safe Shutdown Earthquake) load is analyzed by using of the simpled Global Beam Model. For this, as a first step, the characteristic analysis of reactor internal components are performed by using of the finite element code ANSYS. And the Global Beam Model for reactor internals which includes beam elements, nonlinear impact springs which have gaps in upper and lower positions, and hydrodynamical couplings which simulate the fluid-filled cylinders of reactor vessel and core barrel structures is established. And for the exciting external force the response spectrum which is applied to reactor support is converted to the time history input. With this excitation and the model the dynamic behaviour of reactor internals is obtained. As the results, the structural integrity of reactor internal components under seismic excitation is verified and the input for the detailed duel assembly series model could be obtained. And the simplicity and effectiveness of Global Beam Model and the economics of the explicit Runge-Kutta-Gills algorithm in impact problem of high frequency interface components are confirmed.

• Tuberculosis and Respiratory Diseases
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• v.45 no.5
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• pp.1000-1011
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• 1998

Background: For the diagnosis or evaluation of airway obstruction in bronchial asthma and chronic obstructive lung disorders, various parameters derived from the forced expiratory volume curve and maximal expiratory flow volume curve have been used. Recently the peak expiratory flow(PEF) measured by the peak flow meter is widely used because of its simplicity and convenience. But there were still no data of the predicted normal values measured by the peak flow meter in Korea. This study was to obtain the predicted normal value of PEF and to know the accuracy of this value to predict $FEV_1$. Method: The measurements of PEF by the MiniWright peak flow meter and several parameters derived from the forced expiratory volume and maximal expiratory flow volume curves by the Microspiro HI 501(Chest Co.) were done in 129 men and 125 women without previous history of the respiratory diseases. The predicted normal values of parameters according to the age and the height were obtained, and the regression equation of $FEV_1$ by PEF was calculated. Results: The predicted normal values of PEF(L/min) were -2.45$\times$Age(year) +1.36 $\times$ Height(cm)+427 in men, and -0.96 $\times$ Age (year) + 2.01 $\times$ Height (cm) + 129 in women. FEFmax derived from the maximal expiratory flow volume curve was less than by 125 L/min in men and 118 L/min in women respectively compared to PEF. $FEV_1$(ml) predicted by PEF was 5.98 $\times$ PEF(L/min) + 303 in men, and 4.61 $\times$ PEF(L/min) + 291 in women respectively. Conclusion : The predicted normal value of PEF measured by the peak flow meter was calculated and it could be used as a standard value of PEF while taking care of patients with airway obstruction. $FEV_1$, the gold standard of ventilatory function, could be predicted by PEF to a certain extent.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.3
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• pp.143-150
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• 2012

A simulation methodology and corresponding program based on it is to be discussed for analyzing the effects of the networking operation of existing DHC system in connection with CHP system on-site. The practical simulation for arbitrary areas with various building compositions is carried out for the analysis of operational features in both systems, and the various aspects of thermal energy grids with connecting operation are highlighted through the detailed assessment of predicted results. The intrinsic operational features of CHP prime movers, gas engine, gas turbine etc., are effectively implemented by realizing the performance data, i.e. actual operation efficiency in the full and part loads range. For the sake of simplicity, a simple mathematical correlation model is proposed for simulating various aspects of change effectively on the existing DHC system side due to the connecting operation, instead of performing cycle simulations separately. The empirical correlations are developed using the hourly based annual operation data for a branch of the Korean District Heating Corporation (KDHC) and are implicit in relation between main operation parameters such as fuel consumption by use, heat and power production. In the simulation, a variety of system configurations are able to be considered according to any combination of the probable CHP prime-movers, absorption or turbo type cooling chillers of every kind and capacity. From the analysis of the thermal network operation simulations, it is found that the newly proposed methodology of mathematical correlation for modelling of the existing DHC system functions effectively in reflecting the operational variations due to thermal energy grids with connecting operation. The effects of intrinsic features of CHP prime-movers, e.g. the different ratio of heat and power production, various combinations of different types of chillers (i.e. absorption and turbo types) on the overall system operation are discussed in detail with the consideration of operation schemes and corresponding simulation algorithms.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.288-294
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• 2011

BACKGROUND: Methylmercury is analyzed by HPLC-ICP/MS because of the simplicity for sample preparation and interference. However, most of the pre-treatment methods for methylmercury need a further pH adjustment of the extracted solution and removal of organic matter for HPLC. The purpose of this study was to establish a rapid and accurate analytical method for determination of methylmercury in fish by using HPLC-ICP/MS. METHOD AND RESULTS: We conducted an experiment for pre-treatment and instrument conditions and analytical method verification. Pre-treatment condition was established with aqueous 1% L-cysteine HCl and heated at $60^{\circ}C$ in microwave for 20 min. Methylmercury in $50{\mu}L$ of filtered extract was separated by a C18 column and aqueous 0.1% L-cysteine HCl + 0.1% L-cysteine mobile phase at $25^{\circ}C$. The presence of cysteine in mobile phase and sample solution was essential to eliminate adsorption, peak tailing and memory effect problems. Correlation coefficient($r^2$) for the linearity was 0.9998. The limits of detection and quantitation for this method were 0.15 and $0.45{\mu}g/kg$ respectively. CONCLUSION: Result for analytical method verification, accuracy and repeatability of the analytes were in good agreement with the certified reference materials values of methylmercury at a 95% confidence level. The advantage of the established method is that the extracted solution can be directly injected into the HPLC column without additional processes and the memory effect of mercury in the ICP-MS can be eliminated.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.