• Journal of Digital Convergence
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• v.15 no.6
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• pp.71-82
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• 2017

Most banks in Myanmar have begun to provide their services via mobile phones. However, few studies investigated the factors that may help to set mobile services from a customer perspective. So, this study aims to propose and test a conceptual research model to predict the user's intention to use and actual use level of mobile banking service by combining UTAUT and DeLone-Mclean IS model. Data were collected from 206 citizens who had experienced mobile banking in various regions of Myanmar. The study found that performance expectancy, effort expectancy, information quality and service quality influence the user 's intention to adopt mobile banking services which directly affects the user's actual use of them. However, social influence, facilitating condition and system quality don't influence the user's intention. The study results contribute to meeting customer's needs and reducing customer risk in Myanmar's mobile banking industry, suggesting to seamlessly provide the necessary resources like technology improvements, organizational infrastructure and service centers. Another future study are required to include service's security and trust factors so that the service providers could gain their customers' reliability and trust.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.121-131
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• 2017

With emergence of Internet, social media, and mobile service, the consumers have actively presented their opinions and sentiment, and then it is spreading out real time as well. The user-generated text data on the Internet and social media is not only the communication text among the users but also the valuable resource to be analyzed for knowing the users' intent and sentiment. In special, economic participants have strongly asked that the social big data and its' analytics supports to recognize and forecast the economic trend in future. In this regard, the governments and the businesses are trying to apply the social big data into making the social and economic solutions. Therefore, this study aims to reveal the capability of social big data analysis for the economic use. The research proposed a social big data analysis model and an online consumer sentiment index. To test the model and index, the researchers developed an economic survey ontology, defined a sentiment dictionary for sentiment analysis, conducted classification and sentiment analysis, and calculated the online consumer sentiment index. In addition, the online consumer sentiment index was compared and validated with the composite consumer survey index of the Bank of Korea.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.640-653
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• 2011

We simulate the propagation of earthquake waves in the continental margin of Antarctica using the elastic wave modeling algorithm, which is modified to be suitable for acoustic-elastic coupled media and earthquake source. To simulate the various types of earthquake source, the staggered-grid finite-difference method, which is composed of velocity-stress formulae, can be more appropriate to use than the conventional, displacement-based, finite-difference method. We simulate the elastic wave propagation generated by earthquakes combining 3D staggered-grid finite-difference algorithm composed of displacement-velocity-stress formulae with double couple mechanisms for earthquake source. Through numerical tests for left-lateral strike-slip fault, normal fault and reverse fault, we could confirm that the first arrival of P waves at the surface is in a good agreement with the theoretically-predicted results based on the focal mechanism of an earthquake. Numerical results for a model made after the subduction zone in the continental margin of Antarctica showed that earthquake waves, generated by the reverse fault and propagating through the continental crust, the oceanic crust and the ocean, are accurately described.

• Magazine of the Korea Concrete Institute
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• v.8 no.3
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• pp.219-229
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• 1996

Recent construction activity of infrastructures has been booming and accelerating to incur shortage of river sand for concrete works. Thus, sea sand has been excessively used instead of river sa.nd, that directly causes to decrease the quality and the durability of concrete, and then might lead to the collapse of concrete structures. The purpose of this experimental research is not only to develop high-strength concrete using sea sand, but also to investigate mechanical properties of high-strength concrete, such as elastic moduli, compressive strength and etc, which could be used for important design data of concrete structures. Rational analytic formula for elastic moduli have been proposed together with those for the splitting tensile strength and the flexural strength, which are to be predicted from compressive strength of concrete cylinder. Optimum water-cement and water-binder ratio have been experimentally obtained so as to develop high compressive strength with and without using silica fume as a admixture for concrete. It is noted that experimental elastic moduli for high strength concrete above aCk=330kgf /cm2 are less than those by the Code. Appropriate amount of concrete mixture has been experimentally investigated so as to develop maximum compressive, flexural and splitting tensile strength.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.113-127
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• 2018

This study develops a fusion priority signal control algorithm to pass without delay in emergency events. Fusion priority signal control is method combined center control with local control. The center control method applies signal times for each signalized intersection on the emergency vehicle's route when an emergency call is received. As signals are controlled before the emergency vehicle leaves for its destination, it is possible to clear the queues at each intersection more effectively. However, since the traffic information (speed, position) of the real-time emergency vehicle is not used, the intersection arrival time predicted by center control and actual arrival time of the emergency vehicle may be different from each other. In the case, it is possible to experience a delay caused by the signal. Local control method operate priority signal use the real-time information of EV, but there is a limitation that queue elimination time can not be reflected. In this study, fusion(center+local) control algorithm is proposed to compensate the disadvantages of center and local control also maximizing its advantages. Proposed algorithm is expected to decrease delay time of EV in emergency situation.

• KSBB Journal
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• v.14 no.6
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• pp.651-654
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• 1999

Using recombinant human growth hormone as a model protein, we carried out unfolding by adding a denaturant such as urea, guanidine HCl, or SDS followed by refolding by dilution and dialysis. The objectives were to monitor the structural changes during in vitro refolding process and, based on the results, to develop a quantitative method of refolding progress assessment. The changes in surface hydrophobicity were measured by fluorescence tagging of 1-anilinonaphthalene-8-sulfonate(1,8-ANS) to the hydrophobic portions, and those in the secondary structure were monitored by using far UV-CD(circular dichroism) spectroscopy. Also, we used RP-HPLC to separate and quantify the folded and unfolded proteins to correlate the result with the structure analysis. Our results indicate the surface hydrophobicity are well correlated with the formations of the secondary structure, primarily ${\alpha}$-helices, as well as the disulfide bridges. We expect this monitoring technique can be applied in industrial fields as a means to quantitatively assess the progress of in-vitro refolding of recombinant proteins.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.1
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• pp.16-24
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• 1998

In this study, Ocean General Circulation Model (OGCM) has been developed as a counterpart of Atmospheric General Circulation (AGCM) for the study of coupled ocean-atmosphere climate system. The oceanic responses to given atmospheric boundary conditions have been investigated using the OGCM. In an integration carried out over 100 simulated years with climatological monthly mean data (EXP 1), most parts of the model reached a quasi-equilibrium climate reproducing many of the observed large-scale oceanic features remarkably well. Some observed narrow currents, however, such as North Equatorial Counter Current, were inevitably distorted due to the model's relatively coarse resolution. The seasonal changes in sea ice cover over the southern oceans around Antarctica were also simulated. In an experiment (EXP 2) under boundary condition of 10-year monthly data (1982-1991) from NCEP/NCAR Reanalysis Project model properly reproduced major oceanic changes during the period, including El Ni$\tilde{n}$os of 1982-1983 and 1986-87. During the ENSO periods, the experiment showed eastward expansion of warm surface waters and a negative vertical velocity anomalies along' the equator in response to expansion of westerly current velocity anomalies as westerly wind anomalies propagated eastward. Simulated anomalous distribution and the time behavior in response to El Ni$\tilde{n}$o events is consistent with that of the observations. These experiments showed that the model has an ability to reproduce major mean and anomalous oceanic features and can be effectively used for the study of ocean-atmosphere coupling system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.5
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• pp.547-553
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• 2016

Black carbon (BC) contained in particulate matter (PM) originating from the exhaust gases of ships' diesel engines has been receiving great attention as a cause of glacial melting and warming in the polar regions. In this study, we took samples from various locations of PM emitted from the training ship (T/S) HANBADA's main engine, in cooperation with the Korea Maritime and Ocean University. We analyzed the structure and characteristics of these samples using high-resolution transmission electron microscopy (HR-TEM) and applied our findings as fundamental research for developing PM reduction technology. We also employed our results to determine appropriate preemptive action to meet upcoming PM/BC regulations. In addition, we confirmed the emission trend of pollutants from exhaust gases under various engine operating conditions using an exhaust gas analyzer. Results obtained from the analysis of HR-TEM images showed that the structure of the PM is chain-like wispy agglomerates consisting of a number of individual spherical particles. As the sampling location was moved away from the turbo charger (T/C) towards the funnel, more condensates were observed at a low temperature and the molecular structure of the PM lost its characteristic BC structure as an amorphous structure gradually appeared. Furthermore, through the analysis of exhaust gases, we predicted a decrease in PM concentration in the exhaust stream as engine rpm increase.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.