• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.79-94
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• 2013

Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.24 no.3
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• pp.203-213
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• 1991

Zooplankton samples were collected vertically from different layers with a closing net at 14 stations in the middle East Sea of Korea in February, August and September to study distribution of biological indicators for analysis of water masses. Horizontal and vertical distributions of important species of copepods and chaetognathas known as indicator species were closely related to distributions of different water masses and oceanic fronts. Pleuromamma gracilis, Calanus tenuicornis, Sagitta enflata and Sagitta minima were found to be reliable indicator species to determine warm water mass with warm core, and Calanus cristatus, Calanus tonsus and Sagitta elegans could be used as cold water species for evaluating the movement of cold current from North Korea, and Gaetanus armiger was deep sea water species. Therefore, it was found that North Korean Cold Current down to the south along the coast appeared to be significant in the surface around Chumunjin area, and from here towards the south the cold water containing S. elegans submerged under warm water with S. enflata which were about $2{\~}4^{\circ}C$ higher than that of the vicinity and reappeared near Chukpeon area in surface layer. In the layer between loom and 300m depths, distribution of Pleuromamma gracilis and Sagitta bedoti indicated that warm water mass and front zone influenced by the different water systems were formed in northwestern area off Ulreung-do. In $300{\~}500m$ layer, the proper cold water could be estimated to be present in the northwestern area off Ulreung-do throughout the survey period by the high abundance of Gaetanus armiger. In August, distributions of S. bedoti, S. enflata and S. minima were valuable index to find oceanic fronts and warm core.

• Journal of Life Science
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• v.21 no.1
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• pp.102-109
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• 2011

The genioglossus muscle plays an important role in maintaining upper airway patency during inspiration; if this muscle does not contract normally, breathing disorders occur due to closing of the upper airway. These occur because of disorders of synaptic input to the genioglossus motoneurons, however, little is known about it. In this study, the distribution of GABA-, glycine-, and glutamate-like immunoreactivity in axon terminals on dendrites of the rat genioglossus motoneurons, stained intracellularly with horseradish peroxidase (HRP), was examined by using postembedding immunogold histochemistry in serial ultrathin sections. The motoneurons were divided into four compartments: the soma, and primary (Pd), intermediate (Id), and distal dendrites (Dd). Quantitative analysis of 157, 188, 181, and 96 boutons synapsing on 3 soma, 14 Pd, 35 Id, and 28 Dd, respectively, was performed. 71.9% of the total number of studied boutons had immunoreactivity for at least one of the three amino acids. 32.8% of the total number of studied boutons were immunopositive for GABA and/or glycine and 39.1% for glutamate. Among the former, 14.2% showed glycine immunoreactivity only and 13.3% were immunoreactive to both glycine and GABA. The remainder (5.3%) showed immunoreactivity for GABA only. Most boutons immunoreactive to inhibitory amino acids contained a mixture of flattened, oval, and round synaptic vesicles. Most boutons immunoreactive to excitatory amino acids contained clear and spherical synaptic vesicles with a few dense-cored vesicles. When comparisons of the inhibitory and excitatory boutons were made between the soma and three dendritic segments, the proportion of the inhibitory to the excitatory boutons was high in the Dd (23.9% vs. 43.8%) but somewhat low in the soma (35.7% vs. 38.2%), Pd (34.6% vs. 37.8%) and Id (33.1% vs. 38.7%). The percentage of synaptic covering of the inhibitory synaptic boutons decreased in the order of soma, Pd, Id, and Dd, but this trend was not applicable to the excitatory boutons. The present study provides possible evidence that the spatial distribution patterns of inhibitory and excitatory synapses are different in the soma and dendritic tree of the rat genioglussus motoneurons.

• Journal of Korea Game Society
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• v.4 no.3
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• pp.11-20
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• 2004

Virtual reality that we have seen from the movies in 80's and 90's is hawing near based on the rapid progress of science together with a computer technology. Various virtual reality system developments (such as VRML, HMD FishTank, Wall Type, CAVE Type, and so on) and the advancement of those systems make for the embodiment of virtual reality that gives more sense of the real. Virtual reality is so immersive that makes people feel like they are in that environment and enable them to manipulate without experiencing the environment at first hand that is hard to experience in reality. Virtual reality can be applied to the spheres, such as education, high-level programming, remote control, surface exploration of the remote satellite, analysis of exploration data, scientific visualization, and so on. For some connote examples, there are training of a tank and an aeroplane operation, fumiture layout design, surgical operation practice, game, and so on. In these virtual reality systems, the actual operation of the human participant and virtual workspace are connected each other to the hardware that stimulates the five senses adequately to lend the sense of the immersion. There are still long way to go, however, before long it will be possible to have the same feeling in the virtual reality as human being can have by further study and effort. In this thesis, the basic definition, the general idea, and the kind of virtual reality were discussed. Especially, CAVE typed in reality that is highly immersive was analyzed in definition, and then the method of VR programming and modeling in the virtual reality system were suggested by showing the restoration process of Kyongbok Palace (as the content of the original form of the culture) that was made by KISTI(Korea Institute of Science and Technology Information) in 2003 through design process in virtual reality system. Through these processes, utilization of the immersive virtual reality system was discussed and how to take advantage of this CAVE typed virtual reality system at the moment was studied. In closing the problems that had been exposed in the process of the restoration of the cultural property were described and the utilization plan of the virtual reality system was suggested.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.495-505
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• 2006

In order to classify aerosol type, Aerosol Optical Thickness (AOT) and Fine mode Fraction (FF), which is the optical thickness ratio of small particles$(<1{\mu}m)$ to total particles, data from MODIS (MODerate Imaging Spectraradiometer) aerosol products were analyzed over North-East Asia during one year period of 2005. A study area was in the ocean region of $20^{\circ}N\sim50^{\circ}N$ and $110^{\circ}E\simt50^{\circ}E$. Three main atmospheric aerosols such as dust, sea-salt, and pollution can be classified by using the relationship between AOT and FF. Dust aerosol has frequently observed over the study area with relatively high aerosol loading (AOT>0.3) of large particles (FF<0.65) and its contribution to total AOT in spring was up to 24.0%. Pollution aerosol, which is originated from anthropogenic sources as well as a natural process like biomass burning, has observed in the regime of high FF (>0.65) with wide AOT variation. Average pollution AOT was $0.31{\pm}0.05$ and its contribution to total AOT was 79.8% in summer. Characteristic of sea-salt aerosol was identified with low AOT (<0.3), almost below 0.1, and slightly higher FF than dust and lower FF than pollution. Seasonal analysis results show that maximum AOT $(0.33{\pm}0.11)$ with FF $(0.66{\pm}0.21)$ in spring and minimum AOT $(0.19{\pm}0.05)$, FF $(0.60{\pm}0.14)$ in fall were observed in the study area. Spatial characteristic was that AOT increasing trend is observed as closing to the eastern part of China due to transport of aerosols from China by the prevailing westerlies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.