• (The) Research of the performance art and culture
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• no.32
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• pp.399-426
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• 2016

Sanjo has a quintessential value not only because of its musical depth but also because of its style of music that best expresses Korean sentiment. However, new values are being established from different viewpoints as time passes so Sanjo's values need a modification in order to encompass a contemporary value that is required and accepted by modern society. In this context, while focusing on communication with the public, I contemplated the developmental direction of Sanjo in five perspectives; The Social realization of value, The Experimental and social transformation, The Social diffusion of creation, The Leap of fusion and harmony and finally The Socialization of contents. In the perspective of 'social realization of value', Sanjo refers to creative activity as a new 'duneum' which allows traditional and creative activity to deviate freely while still being under the Sanjo guideline. Either way, it has a periodical value because new forms with new rhythms are the only ways to communicate with the modern public. When these values can be understood by modern society can Sanjo be revived and be acknowledged as an infinite value. Secondly as an experiment and social transformation, there is a transformation of musical instruments in the 21th century. Our musical instruments should be transformed to effectively perform our music rather than to perform Western music. Third, social diffusion of creation should be the 'new Sanjo festival in 21th century' which can facilitate the communication with the public. Fourth, regarding leap of fusion and harmony, I suggest a performing culture consisting of 'Storytelling Sanjo' and 'Media Art' which will ceaselessly evolve Sanjo performance as a medium to communicate with the public. Finally, in regards to the socialization of contents, I emphasize that Sanjo should have contents of mass media as a way of means to help utilize mass media.

• Journal of Korean Philosophical Society
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• v.123
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• pp.409-440
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• 2012

It is well known that Deleuze began his philosophical work from researching the biographies of many predecessors' such as D. Hume, Lucretius, I. Kant, B. Spinoza, F. W. Nietzsche, H. Bergson, and etc. Among them, especially Spinoza, Nietzsche, and Bergson constitute a foundation supporting the whole idea of Deleuze. He declared that the goal of his early biographical work is a theme converging the identity of two philosophers, Spinoza and Nietzsche, and it shows that these two philosophers had such a huge significance to Deleuze, at least by then. However, we may point out two issues here. The first is why Deleuze, who is recognized as a philosopher of 'difference', deals the identity. The other issue is, what kind of identity exists between the two philosophers, Spinoza and Nietzche. In a common sense, their ideas contradict to each other. Spinoza puts God at the center of his philosophical system, whereas Nietzsche declared 'God is dead'. Though Nietzsche expressed a concurrent opinion with Spinoza at first, it is well known that he turned his side against him soon after and criticized him sharply. There is a conflict at the core of this criticism concerning the existence or non-existence of God. Many think that Spinoza, however, cannot be free from the argument that his philosophy allows a possibility of atheism. Deleuze, who also called Spinoza an atheist, suggested a new viewpoint of the philosophy of Spinoza based on his attribution to the concept of 'power'. On the other hand, Deleuze reinterpreted Nietzsche, where he analyzed 'the will to power' in a totally inventive way. Likewise, the reciprocal communication of ideas connected by the concept of 'power' gives a foundation of identifying the two philosophers to Deleuze. In this paper, considering this reciprocal communication, I intent to reveal the foundation of identity of the two philosophers, Spinoza and Nietzsche, and as a result, investigate what the concept of identity means to Deleuze, the philosopher of difference. Furthermore, we will also take a look at how Deleuze presents a new perspective on the conflict on the existence of God of the two philosophers in the process of validating the identity.

• Korean Journal of Heritage: History & Science
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• v.42 no.2
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• pp.154-169
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• 2009

Korea is famous for a number of stone pagodas. In particular, it is noticeable that the stone pagodas came after wooden pagodas in all the Kingdoms of Goguryeo, Baekje, and Silla. Since the advent of wooden pagodas, it was during the latter half period of Three Kingdoms(especially, in the early Seventh century) that the first stone pagoda appeared at Mireuksa Temple site in imitation of the wooden ones. Now that no one can deny that Korean stone pagodas have developed, imitating the wooden pagodas. It is also obvious that the Stone Pagoda at Mireuksa site is the prototype of Korean stone pagodas. However, this study casts doubt on the theory that the stone pagodas in the Silla Kingdom originated not from the wooden pagodas, but from the brick pagodas, whereas the stone pagodas in Baekje Kingdom which has been said to come from the wooden ones. The fact that the temples and pagodas in both Baekje and Silla were erected by the same builders and technicians is one of the evidences supporting the assertion of the study. This study, accordingly, examines on the origin of the Silla pagodas by supposing the two genealogies. The first one can be summarized in chronological order as follows: starting from wooden pagodas, Stone Pagoda at Mireuksa site, Stone Pagoda at Jungrimsa site, Stone Pagoda at Gameunsa site, and Stone Pagoda at Goseonsa site. The second one, on the other hand, runs as follows: starting from bick pagodas, Stone Pagoda at Bunhwangsa, Uiseong Tapri five-storied Stone Pagoda, Seonsan Jukjang-ri five-storied Stone Pagoda, and Seonsan Naksan-ri three-storied Stone Pagoda in order. As the above genealogies show, the origin of the stone pagodas has been an controversy, especially because of the two different points of view: the one is that the roof-supporting strata(Okgaesuk-Bachim) originated from the brick structure and the ancient tomb ceiling of Goguryeo Kingdom, and the other is that the strata is a sort of the simplified design of the wooden roof structure. This study, however, takes note of the difference in length of the strata between the brick pagodas and the stone pagodas; the former stretches out its strata longer than the latter. Consequently, the study points out that the roof-supporting strata of the stone pagodas is originally a sort of modification of the wooden roof structure.

• (The)Study of the Eastern Classic
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• no.43
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• pp.7-31
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• 2011

Danjong had suffered the worst misfortune in the history of Lee Dynasty. His throne was usurped by Sejo, his uncle and he was banished to Youngwol where he ended his life for good. Youngwol remembers the last moment of this tragic person clearly through the various traditional stories passed down from the past. In this study, the works assumed to be formed in the contemporary times among Danjong-related stories were targeted. Time of story creation is not known but it could be assumed that the time when heroine of the story was lived may be coincided with the time of traditional story creation, in this story created in a form of experience story. If any objects or elements that may lead to time of traditional story are contained in the story, we may be able to identify the time of traditional story. All of , and were created in the contemporary times. However, these traditional stories are not new at all. If any personal experience story is added on the existing transmitted traditional story, deviation of traditional story near to recreation is taken place. Traditional Danjong story created like this in the contemporary times have some characteristics. First, transmission power of maintaining Danjong story up to the present times was expanded from a nature of misfortune and holiness to general eccentricity. Ability of deity derived from Danjong's grief and grudge is weakened as time goes by and this story is substituted by an amazing things that can be experienced by anybody. Second, at present, Danjong story is transformed to a form of folktale. Danjong story has developed into a myth through deification from a personal legend. This myth is transformed into a folktale everybody can experience in the contemporary times. Third, this story has been recreated in a form of experience story, not a modification. It is said that as the personal experience story is combined with existing traditional stories, a new work of traditional story is created. It could be observed that through contemporary Danjong story, traditional story of historical person could be created in the contemporary times as well and additionally, a process that personal experience story is positioned as a traditional story could be observed. Now is the time to pay attention to various traditional stories being created in the contemporary times like experience stories.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.201-210
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• 2020

The aim of this thesis is to investigate exhibition spaces which are being expanded and transformed concurrently with social phenomena that are also the result of rapid changes, all of which are reflective of a modern society in transition. Such investigation would also include an analysis of changes in the viewing public and artworks themselves, and also an assessment of the public nature of art and its effective aspects. Expansion of exhibition spaces and the increasing connection between art and the public have very important ramifications, in many respects. They present opportunities for the viewing public to immerse themselves in artistic spaces, with some reaching further into other activities - activities that they often share with other individuals. This also leads them to expand their range of activities, turning them into more mobile, proactive audiences. In connection, many corporations have turned their attention to this public aspect of art, which has resulted in a display of art in different types of spaces. The government also began to adopt 'public art' as a matter of policy, using it as a medium of communication between the state and its populace. The public aspect of art, being highlighted as a result of expansion and diversification of exhibition spaces, will have a significant impact not only on the viewing public, but also on the art market. This represents a momentous change for creators of art, which naturally warrants close scrutiny and research.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of the Korean Wood Science and Technology
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• v.13 no.1
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• pp.19-62
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• 1985

This study was performed to investigate: (i) the bending processing properties of silk worm oak (Quercus acutissima Carr.) and Korean red pine (Pinus densiflora S. et Z.) by boiling and steaming treatments; (ii) the effects of interrelated factors - sapwood and heartwood, annual ring placement, softening temperature and time, moisture content. and wood defects on bending processing properties; (iii) the changing rates of bending radii after release from a tension strap, and (iv) the improving methods of bending process by treatment with chemicals. The size of specimens tested was $15{\times}15{\times}350mm$ for boiling and steaming treatments and $5{\times}10{\times}200mm$ for treatments with chemicals. The specimens were green for boiling treatments and dried to 15 percent for steaming treatments. The specimens for treatments with chemicals were soaked in saturated urea solution, 35 percent formaldehyde solution, 25 percent polyethylene glycol -400 solution, and 25 percent ammonium hydroxide solution for 5 days and immediately followed the bending process, respectively. The results obtained were as follows: 1. The internal temperature of silk worm oak and Korean red pine by boiling and steaming time was raised slowly to $30^{\circ}C$ but rapidly from $30^{\circ}C$ to $80-90^{\circ}C$ and then slowly from $80-90^{\circ}C$ to $100^{\circ}C$. 2. The softening time required to the final temperature was directly proportional to the thickness of specimen. The time required from $25^{\circ}C$ to $100^{\circ}C$ for 15mm-squared specimen was 9.6-11.2 minutes in silk worm oak and 7.6-8.1 minutes in Korean red pine. 3. The moisture content (M.C.) of specimen by steaming time was increased rapidly first 4 minutes in the both species, and moderately from 4 to 20 minutes and then slowly and constantly in silk worm oak, and moderately from 4 to 15 minutes and then slowly and constantly in Korean red pine. The M.C. of 15mm-squared specimen in 50 minutes of steaming was increased to 18.0 percent in the oak and 22.4 percent in the pine from the initial conditioned M.C. of 15 percent The rate of moisture adsorption measured was therefore faster in the pine than in the oak. 4. The mechanical properties of the both species were decreased significantly with the increase of boiling rime. The decrement by the boiling treatment for 60 minutes was measured to 36.6-45.0 percent in compressive strength, 12.5-17.5 percent in tensile strength, 31.6-40.9 percent in modulus of rupture, and 23.3-34.6 percent in modulus of elasticity. 5. The minimum bending radius (M.B.R.) of sapwood and heartwood was 60-80 mm and 90 mm in silk worm oak, and 260 - 300 mm and 280 - 300 mm in Korean red pine, respectively. Therefore, the both species showed better bending processing properties in sapwood than in heartwood. 6. The M.B.R. of edge-grained and flat-grained specimen in suk worm oak was 60-80 mm, but the M.B.R. in Korean red pine was 240-280 mm and 260-360 mm, respectively. Comparing the M.B.R. of edge-grained with flat-grained specimen, in the pine the edge-grained showed better bending processing property than the flat-grained. 7. The bending processing properties of the both species were improved by the rising of softening temperature from $40^{\circ}C$ to $100^{\circ}C$. The minimum softening temperature for bending was $90^{\circ}C$ in silk worm oak and $80^{\circ}C$ in Korean red pine, and the dependency of softening temperature for bending was therefore higher in the oak than in the pine. 8. The bending processing properties of the both species were improved by the increase of softening time as well as temperature, but even after the internal temperature of specimen reaching to the final temperature, somewhat prolonged softening was required to obtain the best plastic conditions. The minimum softening time for bending of 15 mm-squared silk worm oak and Korean red pine specimen was 15 and 10 minutes in the boiling treatment, and 30 and 20 minutes in the steaming treatment, respectively. 9. The optimum M.C. for bending of silk worm oak was 20 percent, and the M.C. above fiber saturation point rather degraded the bending processing property, whereas the optimum M.C. of Korean red pine needed to be above 30 percent. 10. The bending works in the optimum conditions obtained as seen in Table 24 showed that the M.B.R. of silk worm oak and Korean red pine was 80 mm and 240 mm in the boiling treatment, and 50 mm and 280 mm in the steaming treatment, respectively. Therefore, the bending processing property of the oak was better in the steaming than in the boiling treatment, but that of the pine better in the boiling than in the steaming treatment. 11. In the bending without a tension strap, the radio r/t of the minimum bending radius t to the thickness t of silk worm oak and Korean red pine specimen amounted to 16.0 and 21.3 in the boiling treatment, and 17.3 and 24.0 in the steaming treatment, respectively. But in the bending with a tension strap, the r/t of the oak and the pine specimen decreased to 5.3 and 16.0 in t he boiling treatment, and 3.3 and 18.7 in the steaming treatment, respectively. Therefore, the bending processing properties of the both species were significantly improved by the strap. 12. The effect of pin knot on the degradation of bending processing property was very severe in silk worm oak by side, e.g. 90 percent of the oak specimens with pin knot on the concave side were ruptured when bent to a 100 mm radius but only 10 percent of the other specimens with pin knot on the convex side were ruptured. 13. The changing rate in the bending radius of specimen bent to a 300 mm radius after 30 days of exposure to room temperature conditions was measured to 4.0-10.3 percent in the boiling treatment and 13,0-15.0 percent in the steaming treatment. Therefore, the degree of spring back after release was higher in the steaming than in the boiling treatment. And the changing rate of moisture-proofing treated specimen by expoxy resin coating was only -1.0.0 percent. 14. Formaldehyde, 35 percent solution, and 25 percent polyethylene glycol-400 solution found no effect on the plasticization of the both species, but saturated urea solution and 25 percent ammonium hydroxide solution found significant effect in comparison to non-treated specimen. But the effect of the treatment with chemicals alone was inferior to that of the steaming treatment, and the steaming treatment after the treatment with chemicals improved 10-24 percent over the bending processing property of steam-bent specimen. 15. Three plasticity coefficients - load-strain coefficient, strain coefficient, and energy coefficient - were evaluated to be appropriate for the index of bending processing property because the coefficients had highly significant correlation with the bending radius. The fitness of the coefficients as the index was good at load-strain coefficient, energy coefficient, and strain coefficient, in order.