• Korean Journal of Psychosomatic Medicine
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• v.30 no.2
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• pp.55-65
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• 2022

Although humans exist as Homo Empathicus, human society is actually constantly divided and conflicted between groups. The human empathy response is very sensitive to the justice of others, and depending on the level of others' justice, they may feel empathy or schadenfreude to the suffering of them. However, our empathy to others' suffering are not always fair, and have inherent limitations of ingroup-biased empathy. Depending on whether the suffering other persons belongs to an ingroup or an outgroup, we may feel biased empathy or biased schadenfreude to them without even realizing it. Recent advances in information and communication technology facilitate biased access to ingroup-related SNS or ingroup media, thereby deepening the establishment of a more biased semantic information network related groups. These processes, through interacting with the inherent limitation of empathy, can form a vicious cycle of more biased ingroup empathy and ingroup-related activities, and accelerate divisions and conflicts. This research investigated the properties and limitations of empathy by reviewing studies on the neural mechanism of empathy. By examining the relationship between empathy and justice from a neuroscientific point of view, this research tried to illuminate the modern society of division and conflict in a different dimension from the classical perspective of social science.

• Wind and Structures
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• v.34 no.1
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• pp.115-125
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• 2022

The high-Reynolds number flow around a rectangular cylinder, having streamwise to crossflow length ratio equal to 5 is analyzed in the present paper. The flow is characterized by shear-layer separation from the upstream edges. Vortical structures of different size form from the roll-up of these shear layers, move downstream and interact with the classical vortex shedding further downstream in the wake. The corresponding mean flow is characterized by a recirculation region along the lateral surface of the cylinder, ending by mean flow reattachment close to the trailing edge. The mean flow features on the cylinder side have been shown to be highly sensitive to set-up parameters both in numerical simulations and in experiments. The results of 21 Large Eddy Simulations (LES) are analyzed herein to highlight the impact of the lateral mean recirculation characteristics on the near-wake flow features and on some bulk quantities. The considered simulations have been carried out at Reynolds number Re=DU_∞/ν=40 000, being D the crossflow dimension, U_∞ the freestream velocity and ν the kinematic viscosity of air; the flow is set to have zero angle of attack. Some simulations are carried out with sharp edges (Mariotti et al. 2017), others with different values of the rounding of the upstream edges (Rocchio et al. 2020) and an additional LES is carried out to match the value of the roundness of the upstream edges in the experiments in Pasqualetto et al. (2022). The dimensions of the mean recirculation zone vary considerably in these simulations, allowing us to single out meaningful trends. The streamwise length of the lateral mean recirculation and the streamwise distance from the upstream edge of its center are the parameters controlling the considered quantities. The wake width increases linearly with these parameters, while the vortex-shedding non-dimensional frequency shows a linear decrease. The drag coefficient also linearly decreases with increasing the recirculation length and this is due to a reduction of the suctions on the base. However, the overall variation of C_D is small. Finally, a significant, and once again linear, increase of the fluctuations of the lift coefficient is found for increasing the mean recirculation streamwise length.

• Journal of the Daesoon Academy of Sciences
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• v.25_2
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• pp.177-213
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• 2015

We live in two giant pendulum in called 'science' and 'religion'. But science and religion are contained in disparate information with each other, Those two is not easy to achieve convergence. But if you accept the ontological scheme of Great Chain of Being(存在의 大連鎖) in the Perennial philosophy(永遠의 哲學), Debate between religion and science is meaningless 'Great Nest of Being(存在의 대둥지, Great Chain of Being)' is similar to the multiple concentric circles, there are different dimension that the each top level to subsume surrounding the lower level. For example, upper zone 'Mysticism(神秘主義)' includes but transcends(or transcends but includes.) the sub-region theology, psychology, biology and physics. The Perennial philosophy is the great spiritual teachers of the world, philosophers and thinkers have adopted a common worldview, a religious views. Philosophers of the perennial philosophy seem to match the cross-cultural almost unanimous about the general level of the 'Great Nest of Being' for the past 3,000 years. The perennial philosophy made the conclusion that God exists in the world. Several types of view of God existing religions in the world have 'Monotheism(一神論)', 'Pantheism(汎神論)' and 'Panentheism (汎在神論)'. Although traditionally the God of the philosophers is the classical Theism, theological trends of today it is moving in the direction of Panentheism. Panentheism see that god is immanent and transcendent. also Daesoon Thoughts is the position of the Panentheism. so this paper points out the fact that the view of God of the perennial philosophy is precisely consistent with the view of God of Daesoon Thoughts. Wilber says 'envelopment [transcend and include]'. The word translates as 'powol(包越)' in Korean. 'Powol(包越)' means that all the developmental evolution is to surround the sub-region developed into the higher realms. View of the God in the perennial philosophy is 'powol theism(包越的 有神論)'. but 'powol immanent God(包越的 內在神論)' rather than building regulations as 'powol theism(包越的 有神論)'. It would have to be a more accurate representation of it. Because in the existing 'theism(有神論)' the god and humans are thought to exist apart. However, Daesoon Thoughts are deemed to also recognize another universal laws. also Sangje(上帝, the Supreme God) is recognized as a cosmic existence that transcends the laws. This point, as the characteristics of the Daesoon Thoughts, In other religions can not be found. Therefore, More specifically represent(More accurately represent), Sangje of the Daesoon Thoughts can be described as 'powol theism' or 'transcendental and included deism(包越的 理神論)'. Importantly, The idea of God can be captured directly by the discipline. In this sense, In terms of the other religions have no discipline law, the practice [discipline] of the Daesoon Thoughts required in the present age. It has the absoluteness.

• Journal of Korean Classical Literature and Education
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• no.39
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• pp.137-180
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• 2018

This article examines the constitutional characteristics of the works in the "Shinbi Apartment" (Mysterious Apartment) series produced by Tooniverse, a domestic animation channel, and considers the meaning of the contents of the ghoststory (鬼神談). The "Shinbi Apartment" series is a horror animation for children. It was produced for the first time in Korea and recorded high ratings. Additionally, it is different from Japanese horror animations that were dubbed and broadcast in Korea in terms of composition and narrative direction, and it succeeds in the form and direction of the traditional Korean ghost story. "Shinbi Apartment - The Secret of Ghost Ball" enriches narrative stories by embracing the structure of the "female ghost story" in traditional ghost stories while following the form of ghosts that suddenly pop up in the daily routines of contemporary ghost stories. The ghost's shape, which has a bizarre and unpredictable aspect, embodies the ghost as the object of fear that modern horror stories intend. However, it does not stop there, but puts the attention on the hero who focuses on the emerging ghost and listens and communicates with it, placing the emphasis of the story on communication, understanding, forgiveness, and reconciliation. The structure and contents of the unique story of "The Secret of Ghost Ball" contribute to the transformation of the ghost into a subject of friendliness and entertainment, not merely as one of shock, fear, and anxiety. Additionally, as the concept of "child" is being created, the custom of modernity, which deals with the story of ghosts in the dimension of teaching and edification, is also manifested in "The Secret of Ghost Ball." In other words, through the figure of the devil, it is to continue the lesson of the story by revealing the adventure, the courage necessary for the "child," and the boundaries for substance and appearance. The "Shinbi Apartment" series has also contributed to the success of ghosts as commercial contents. The structure of the story and its characters have been actively used as educational tools and toys for children. It can be said that ghost culture contributed to this popularization by establishing a base for enjoying ghosts for amusement and entertainment.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.90-103
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• 2009

The poem 'Kyungjusipiyung(慶州十二詠)' written by Seo, Geo-jeong(徐居正) describes sentiments felt for the ruined historical and cultural landscape of Silla's capital city, Kyungju. It differs from the existing 'Eight Sceneries(八景)' as it conveys the strong metaphorical aesthetics of ruins as the episodes and figures are sung, as well as the myths and stories related to the representative holy places of the Silla culture: Gyelim(鷄林), Banwolseong(半月城), Najeong(蘿井), Oneung(五陵), Geumosan(金鰲山), the scenic beauty of deep placeness, Poseokjeong(鮑石亭), Mooncheon(蚊川), Cheomseongdae(瞻星臺), Boonhwangsa(芬皇寺), Youngmyosa(靈妙寺) and Grave of the General Kim Yu-Sin(金庾信墓). Compared with the former "Eight Sceneries" Poems, including Seo Geojeong's 'Kyungjusipiyung', there is a difference in the content of theme recitation, as well as in structure and form, especially with the deep impression of the classical features of the meanings and acts. The sequence of theme recitation seems to be composed of more than two visual corridors visited during trips that last longer than two days. The dominant emotions expresses in this poem, through written in the spring, are regret and sadness such as 'worn', 'broken and ruined', 'old and sad', without touching on the beauty of nature and the taste for life that is found in most of the Eight Sceneries Poems. Thus, the feelings of the reciter himself, Seo, Geo-jeong, about the described sceneries and their symbolism are more greatly emphasized than the beauty of form. The characteristic aspect of his experiences of ruins expressed from 'Kyungjusipiyung' is that the experiences were, first of all, qualitative of the aura conveyed; that is, the quality omnipresent throughout the culture of Silla as reflected in the twelve historical and cultural landscapes. In this poem, the cultural ruins of the invisible dimension such as the myths and legends are described by repetition, parallelism, juxtaposition, reflection and admiration from the antiphrases, as well as the civilized ruins of the visible dimension such as the various sceneries and features of Kyungju. This seems to be characteristic of the methods by which Seo, Geo-jeong appreciates 'Silla' in the poem 'Kyungjusipiyung'. Ruins as an Aesthetic Object imply the noble pride of Seo, Geo-jeong in identifying himself with the great nature of ruins. In 'Kyungjusipiyung', the images of the ruins of Silla and Kyungju are interspersed in spite of his positive recognition of 'the village of Kyungju' based on his records. However, though the concept of ruins has a pessimistic tone connoting the road of extinction and downfall, the aspect here seems to ambivalently contain the desire to recover and revive Kyungju through the Chosun Dynasty as adominant influence on the earlier Chosun's literary tide. The aesthetics of the scenery found in Seo, Geo-jeong's 'Kyungjusipiyung' contain the strongest of metaphor and symbolism by converting the experiences of the paradoxical ruins into the value of reflective experiences.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Cartoon and Animation Studies
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• s.36
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• pp.147-165
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• 2014

Around the turn of 21st century, there has been a major technological shift in the animation industry. With development of reality-based computer graphics, major American animation studios replaced hand-drawn method with the new 3D computer graphics. Traditional animation was known for its simplified shapes such as circles and triangle that makes characters' movements distinctive from non-animated feature films. Computer-generated animation has largely replaced it, but is under continuous criticism that automated movements and reality-like graphics devaluate the aesthetics of animation. Although hand-drawn animation is still produced, 3D computer graphics have taken commercial lead and there has been many changes to acting of animated characters, which calls for detailed investigation. Firstly, the changes in acting of 3D characters can be traced from looking at human-like rigging method that mimics humanistic moving mechanism. Also, if hair and clothing was part of hand-drawn characters' acting, it has now been hidden inside mathematical simulation of 3D graphics, leaving only the body to be used in acting. Secondly, looking at "Stretch and Squash" method, which represents the distinctive movements of animation, through the lens of media, a paradox arises. Hand-drawn animation are produced frame-by-frame, and a subtle change would make animated frames shiver. This slight shivering acts as an aesthetic distinction of animated feature films, but can also require exaggerated movements to hide the shivering. On the contrary, acting of 3D animation make use of calculated movements that may seem exaggerated compared to human acting, but seem much more moderate and static compared to hand-drawn acting. Moreover, 3D computer graphics add the third dimension that allows more intuitive movements - maybe animators no longer need fine drawing skills; what they now need is directing skills to animate characters in 3D space intuitively. On the assumption that technological advancement and change of artistic expressionism are inseparable, this paper compares acting of 3D animation studio Pixar and classical drawing studio Disney to investigate character acting style and movements.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.