• Cross-Cultural Studies
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• v.21
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• pp.165-188
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• 2010

One of the things that should be done first in establishing the cosmic life culture is to reflect on the old human-oriented culture. If the cosmic life culture absolutizes everything except for humans according to the logic of reason, its foundation will weaken or eventually get lost. Separating humans from the universe is just like separating life from it. Given that all life, whether it is humans or the earth, originated from the universe, such an effort for separation merely values an individual life by excluding all life or whole life. When the human body and the cosmic chi' blood are not in an active flow, it means there is a problem with life. What is in the greatest need in such a case is the sincere human mind that follows the principle of cosmic life. It is like the sincerity found in the pasonri singer, who mellows all the hardships and difficulties in the world out and create songs out of them like the shadow. It is the pansori singer's shadow that changes the universe. It is only when the extreme force of human mind communicates with that of the universe that the cosmic life or cosmic life culture can be created. In that sense, it is urgent to create life out of the universe inside me and create a universe out of all life in and outside me. It is such a grave plan in human history in that it involves finding the "Sanal" which is the core of life living hidden inside the body whose life force gradually goes away or inside the universe, and creating the culture of Bokseung in which it bursts out. The most important thing in life is the flow, and the mankind is currently standing in the life flow of the massive universe's chaosmos. The greatest task the mankind is currently faced with is to think over how to deal with the period of Big Chaos in the massive universe's chaosmos reversely and establish the cosmic life culture anew.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.313-319
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• 2023

In this paper, a time series machine learning model, Long Short Term Memory (LSTM), is applied into the bubble flow noise data and the underwater projectile launch noise data to predict missing values of time-series underwater noise data. The former is mixed with bubble noise, flow noise, and fluid-induced interaction noise measured in a pipe and can be classified into three types. The latter is the noise generated when an underwater projectile is ejected from a launch tube and has a characteristic of instantaenous noise. For such types of noise, a data-driven model can be more useful than an analytical model. We constructed an LSTM model with given data and evaluated the model's performance based on the number of hidden units, the number of input sequences, and the decimation factor of signal. It is shown that the optimal LSTM model works well for new data of the same type.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• Membrane and Water Treatment
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• v.9 no.2
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.120-126
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• 2018

The film is an audiovisual art and conveys stories using various audiovisual techniques. Among the auditory elements, music has a wide variety of expressive techniques, a wide range of applications, and is sometimes used as a proprietary tool to convey stories. This study examines the musical expression technique for expressing the psychology of the character in the film by using the movie "Punch Drunk Love" as a subject of study. We study the use of music used to reveal meaning that does not appear on the screen in the film story flow, which is a tool to create the hidden meaning of the person's inner psychology and situation. This provides an example and perspective of the contrast and symbolic expression of character psychology in film music.

• Journal of Ecology and Environment
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• v.29 no.2
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• pp.175-183
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• 2006

The discovery of deep-sea hydrothermal vents and their ecosystems is a monumental landmark in the history of Ocean Sciences. Deep-sea hydrothermal vents are scattered along the global mid-ocean ridges and back-arc basins. Under sea volcanic phenomena related to underlying magma activities along mid-ocean ridges generate extreme habitats for highly specialized communities of animals. Multidisciplinary research efforts during past three decades since the first discovery of hydrothermal vents along the Galapagos Rift in 1977 revealed fundamental components of physiology, ecology, and evolution of specialized vent communities of micro and macro fauna. Heterogeneous regional geological settings and tectonic plate history have been considered as important geophysical and evolutionary factors for current patterns of taxonomic composition and distribution of vent faunas among venting sites in the World Ocean basins. It was found that these communities are based on primary production of chemosynthetic bacteria which directly utilize reduced compounds, mostly $H_2S$ and $CH_4$, mixed in vent fluids. Symbioses between these bacteria and their hosts, vent invertebrates, are foundation of the vent ecosystem. Gene flow and population genetic studies in parallel with larval biology began to unveil hidden dispersal barrier under deep sea as well as various dispersal characteristics cross taxa. Comparative molecular phylogenetics of vent animals revealed that vent faunas are closely related to those of cold-water seeps in general. In perspective additional interesting discoveries are anticipated particularly with further refined and expanded studies aided by new instrumental technologies.

• Journal of the Korean Home Economics Association
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• v.50 no.3
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• pp.83-93
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• 2012

In Tongyeong Ogwangdae, the characters use many ornaments and these ornaments represent different meanings. The following results were observed from the analysis that was carried out, to find the symbolic meanings of ornaments and props, and they- were used in Tongyeong Ogwangdae. The ornaments and props used in the traditional mask play are used to effectively represent the roles, characters, situations, and certain parts of body. They put each character in a psychological mood that enables him or her to perform his or her role more realistically. This in turn moves the audience. The ornaments and the props that were used in Tongyeong Ogwangdae help the audience to understand the characters and the hidden meaning of the play. These ornaments and props can be classified into three categories namely, one representing the character's social status, one representing the role of the character, and one indicating the flow of the play.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.781-786
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• 2019

We should give a realistic value on the large amounts of relevant data obtained from these studies to achieve effective objectives of the disease study which is dealing with various vital phenomenon today. In this paper, the proposed MKSV algorithm is estimated by optimal probability distribution, and the input pattern is determined. After classifying it into data mining, it is possible to obtain efficient computational quantity and recognition rate. MKSV algorithm is useful for studying the relationship between disease and gene in the present society by simulating the probabilistic flow of gene data and showing fast and effective performance improvement to classify data through the data mining process of big data.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.