• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.101-108
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• 2011

Climate change could have a pronounced impact on natural and agricultural ecosystems. To assess the impact of climate change, projected climate data have been used as inputs to models. Because such studies are conducted occasionally, it would be useful to employ Cloud computing, which provides multiple instances of operating systems in a virtual environment to do processing on demand without building or maintaining physical computing resources. Furthermore, it would be advantageous to use open source geospatial applications in order to avoid the limitations of proprietary software when Cloud computing is used. As a pilot study, Amazon Web Service ? Elastic Compute Cloud (EC2) was used to calculate the number of days with rain in a given month. Daily sets of climate projection data, which were about 70 gigabytes in total, were processed using virtual machines with a customized database transaction application. The application was linked against open source libraries for the climate data and database access. In this approach, it took about 32 hours to process 17 billion rows of record in order to calculate the rain day on a global scale over the next 100 years using ten clients and one server instances. Here I demonstrate that Cloud computing could provide the high level of performance for impact assessment studies of climate change that require considerable amount of data.

• Earthquakes and Structures
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• v.13 no.1
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• pp.59-66
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• 2017

Modern seismic codes rely on performance-based seismic design methodology which requires that the structures withstand inelastic deformation. Many studies have focused on the inelastic deformation ratio evaluation (ratio between the inelastic and elastic maximum lateral displacement demands) for various inelastic spectra. This paper investigates the inelastic response spectra through the ductility demand ${\mu}$, the yield strength reduction factor $R_y$, and the inelastic deformation ratio. They depend on the vibration period T, the post-to-preyield stiffness ratio ${\alpha}$, the peak ground acceleration (PGA), and the normalized yield strength coefficient ${\eta}$ (ratio of yield strength coefficient divided by the PGA). A new inelastic deformation ratio $C_{\eta}$ is defined; it is related to the capacity curve (pushover curve) through the coefficient (${\eta}$) and the ratio (${\alpha}$) that are used as control parameters. A set of 140 real ground motions is selected. The structures are bilinear inelastic single degree of freedom systems (SDOF). The sensitivity of the resulting inelastic deformation ratio mean values is discussed for different levels of normalized yield strength coefficient. The influence of vibration period T, post-to-preyield stiffness ratio ${\alpha}$, normalized yield strength coefficient ${\eta}$, earthquake magnitude, ruptures distance (i.e., to fault rupture) and site conditions is also investigated. A regression analysis leads to simplified expressions of this inelastic deformation ratio. These simplified equations estimate the inelastic deformation ratio for structures, which is a key parameter for design or evaluation. The results show that, for a given level of normalized yield strength coefficient, these inelastic displacement ratios become non sensitive to none of the rupture distance, the earthquake magnitude or the site class. Furthermore, they show that the post-to-preyield stiffness has a negligible effect on the inelastic deformation ratio if the normalized yield strength coefficient is greater than unity.

• The Korean Journal of Emergency Medical Services
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• v.20 no.2
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• pp.7-19
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• 2016

Purpose: To investigate the effect of early hypothermia on post-resuscitation myocardial recovery and survival time after cardiac arrest and resuscitation in a rat model of myocardial infarction(MI). Methods: Thoracotomies were performed in 10 male Sprague Dawley rats weighing 450-455g. Myocardial infarction was induced by ligation of the left anterior descending coronary artery. Ninety minutes after arterial ligation, ventricular fibrillation was induced, cardiopulmonary resuscitation was subsequently performed before defibrillation was attempted. Animals were randomized to control group and experimental group(acute MI-normothermia)($32^{\circ}C$ for 4 hours). Duration of survival was recorded. Myocardial functions, including cardiac output, left ventricular ejection fraction, and myocardial performance index were measured using echocardiography. Results: Myocardial function was significantly better in hypothermia group than the control group during the first 4 hours post-resuscitation. The survival time of the experimental group was greater than that of the control group(p<.050). Conclusion: This study suggests that early hypothermia can attenuate post-resuscitation myocardial dysfunction after acute myocardial function, and may be a useful strategy in post-resuscitation care.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.986-998
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• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.803-812
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• 2006

This paper analytically evaluates the FEC(Forward Error Correction) symbol size's effect on the performance and energy consumption of 802.11 protocol with the block FEC algorithm over WSN(Wireless Sensor Network). Since the basic recovery unit of block FEC algorithms is symbols not bits, the FEC symbol size affects the packet correction rate even with the same amount of FEC check bits over a given WSN channel. Precisely, when the same amount of FEC check bits are allocated, the small-size symbols are effective over channels with frequent short bursts of propagation errors while the large ones are good at remedying the long rare bursts. To estimate the effect of the FEC symbol site, the paper at first models the WSN channel with Gilbert model based on real packet traces collected over TIP50CM sensor nodes and measures the energy consumed for encoding and decoding the RS (Reed-Solomon) code with various symbol sizes. Based on the WSN channel model and each RS code's energy expenditure, it analytically calculates the transmission efficiency and power consumption of 802.11 equipped with RS code. The computational analysis combined with real experimental data shows that the RS symbol size makes a difference of up to 4.2% in the transmission efficiency and 35% in energy consumption even with the same amount of FEC check bits.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.149-163
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• 2015

In this study, we conducted study on the confirmation of thermal-hydraulic safety for Mast assembly with Computational Fluid Dynamics(CFD) analysis. Before performing the natural convection analysis for the Mast assembly by using CFD code, we validated the CFD code against two benchmark natural convection data for the evaluation of turbulence models and confirmation of its applicability to the natural convection flow. From the first benchmark test which was performed by Betts et al. in the simple rectangular channel, we selected standard k-omega turbulence model for natural convection. And then, calculation performance of CFD code was also investigated in the sub-channel of rod bundle by comparing with PNL(Pacific Northwest Laboratory) experimental data and prediction results by MATRA and Fluent 12.0 which were performed by Kwon et al.. Finally, we performed main natural convection analysis for fuel assembly inside the Mast assembly by using validated turbulence model. From the calculation, we observed stable natural circulation flow between the mast assembly and pool side and evaluated the thermal-hydraulic safety by calculating the departure from nucleate boiling ratio.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.306-311
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• 2016

Software reliability in the software development process is an important issue. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, reliability software cost model considering logarithmic and exponential fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software reliability model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. The logarithmic and exponential fault detection model is also efficient in terms of reliability because it (the coefficient of determination is 80% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, the software developers have to consider life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.457-463
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• 2015

A gas turbine engine plays an important role as a prime mover that is used in the marine transportation field as well as the space/aviation and power plant fields. However, it has a complicated structure and there is a time delay element in the combustion process. Therefore, an elaborate mathematical model needs to be developed to control a gas turbine engine. In this study, a modeling technique for a gas generator, a PLA actuator, and a metering valve, which are major components of a gas turbine engine, is explained. In addition, sub-models are obtained at several operating points in a steady state based on the trial running data of a gas turbine engine, and a method for controlling the engine speed is proposed by designing an NPID controller for each sub-model. The proposed NPID controller uses three kinds of gains that are implemented with a nonlinear function. The parameters of the NPID controller are tuned using real-coded genetic algorithms in terms of minimizing the objective function. The validity of the proposed method is examined by applying to a gas turbine engine and by conducting a simulation.