• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.358-371
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• 2003

Automatic word spacing is a process of deciding correct boundaries between words in a sentence including spacing errors. It is very important to increase the readability and to communicate the accurate meaning of text to the reader. The previous statistical approaches for automatic word spacing do not consider the previous spacing state, and thus can not help estimating inaccurate probabilities. In this paper, we propose two statistical word spacing models which can solve the problem of the previous statistical approaches. The proposed models are based on the observation that the automatic word spacing is regarded as a classification problem such as the POS tagging. The models can consider broader context and estimate more accurate probabilities by generalizing hidden Markov models. We have experimented the proposed models under a wide range of experimental conditions in order to compare them with the current state of the art, and also provided detailed error analysis of our models. The experimental results show that the proposed models have a syllable-unit accuracy of 98.33% and Eojeol-unit precision of 93.06% by the evaluation method considering compound nouns.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.140-148
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• 2017

An important factor in determining the design and treatment efficiency of wastewater treatment plants (WWTPs) is the quantity and quality of influent. These detailed and accurate information is essential for process control, diagnosis and operation, as well as the basis in designing the plant, selecting the process and determining the optimal capacity of each bioreactor. Probabilistic models are used to predict the wastewater quantity and quality of WWTPs, which are widely used to improve the design and operation of WWTPs. In this study, the optimal probability distribution of time series influent data was derived for predicting water quantity and quality, and wastewater influent data were generated using the Monte Carlo simulation analysis. In addition, we estimated various alternatives for the improvement of bioreactor operations based on present operation condition using the generated influent data and activated sludge model, and suggested the alternative that can operate the most effectively. Thus, the influent quantity and quality are highly correlated with the actual operation data, so that the actual WWTPs influent characteristics were well reproduced. Using this will improve the operating conditions of WWTPs, and a proposed improvement plan for the current TMS (Tele Monitoring System) effluent quality standards can be made.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.99-109
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• 2007

Variable message signs (VMS), which are used for providing real-time information on traffic conditions and accident occurrences, are one of the important components of intelligent transportation systems VMS messages need to meet human factor requirements: messages should be readable and understandable while driving. Lab-controlled experiments on VMS messages were conducted to obtain useful data for analyzing drivers' responsive characteristics for VMS messages. Binary logistic regression (BLR) modeling techniques were applied to explore the relationships among drivers' message perceptions, message reading time, and amount of VMS messages. Probabilistic outcomes of the proposed BLR-based perception model could be greatly utilized to design VMS messages considering drivers' legibility performance. The major contribution of this study is to develop invaluable statistical models that can be used in designing VMS messages more effectively from the human factor point of view. The results could be further applied to establish the scheme of VMS message phase and duration.

• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.900-909
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• 2002

Fibers play a role to increase the tensile strength and cracking resistance of concrete structures. The post cracking behavior must be clarified to predict cracking resistance of fiber reinforced concrete. The purpose of this study is to develop a realistic analysis method for the post cracking behavior of synthetic fiber reinforced concrete members. For this purpose, the cracked section is assumed to behave as a rigid body and the pullout behavior of single fiber is employed. A probabilistic approach is used to calculate effective number of fibers across crack faces. The existing theory is compared with test data and shows good agreement. The proposed theory can be efficiently used to describe the load-deflection behavior, moment-curvature relation, load-crack width relation of synthetic fiber reinforced concrete beams.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.98-105
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• 2003

We propose a new technique of deinterleaving multiple radar pulse sequences by means of genetic algorithm for threat identification in electronic warfare(EW) system. The conventional approaches based on histogram or continuous wavelet transform are so deterministic that they are subject to failing in detection of individual signal characteristics under real EW signal environment that suffers frequent signal missing, noise, and counter-EW signal. The proposed algorithm utilizes the probabilistic optimization procedure of genetic algorithm. This method, a time-of-arrival(TOA) only strategy, constructs an initial chromosome set using the difference of TOA. To evaluate the fitness of each gene, the defined pulse phase is considered. Since it is rare to meet with a single radar at a moment in EW field of combat, multiple solutions are to be derived in the final stage. Therefore it is designed to terminate genetic process at the prematured generation followed by a chromosome grouping. Experimental results for simulated and real radar signals show the improved performance in estimating both the number of radar and the pulse repetition interval.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.21-26
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• 2008

It was already realized at a current technological level of home network systems that the systems recognizes a user's simple order and carry out the order in the ubiquitous computing environment. However home is not a simple environment consisting into a large number of family members, so various order and situation would be needed accordingly. From now on we need to reach the technological level to infer that how is the user's behavior patterns and what kinds of service is the fittest to user who belong to the ubiquitous computing environment by using the result of the context interpreter. In this regards, active inferred-model needs to be suggested upgrading user's command into one step more higher level than the simple one adapting diversified feature. This study would like to suggest this active model recognizing context, which is user's environmental information applying basic network and inferring Context-based Service that user wants through the recognized result This study proposes a new method that can infer the user's desire in ubiquitous computing environment. First of all, we define a context as user's information of ubiquitous computing environment situation that user belongs to and we classify the context into 4W1H(Where, Who, When, What) formats. We construct Bayesian network and put the factor of context use as Bayesian network nodes. As a result, we can infer the user's behavior pattern and most proper service for user in the intelligent space from the probabilistic result of Bayesian network.

• Earthquakes and Structures
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• v.14 no.3
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• pp.249-259
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• 2018

This paper aims to assess the seismic risk of a plane moment-resisting frames (MRFs) consisting of concrete-filled double skin steel tube (CFDST) columns and I-section steel beams. Firstly, three typical limit performance levels of CFDST structures are determined in accordance with the cyclic tests of seven CFDST joint specimens with 1/2-scaled and the limits stipulated in FEMA 356. Then, finite element (FE) models of the test specimens are built by considering with material degradation, nonlinear behavior of beam-column connections and panel zones. The mechanical behavior of the concrete material are modeled in compression stressed condition in trip-direction based on unified strength theory, and such numerical model were verified by tests. Besides, numerical models on 3, 6 and 9-story CFDST frames are established. Furthermore, the seismic responses of these models to earthquake excitations are investigated using nonlinear time-history analyses (NTHA), and the limits capacities are determined from incremental dynamic analyses (IDA). In addition, fragility curves are developed for these models associated with 10%/50yr and 2%/50yr events as defined in SAC project for the region on Los Angeles in the Unite State. Lastly, the annual probabilities of each limits and the collapse probabilities in 50 years for these models are calculated and compared. Such results provide risk information for the CFDST-MRFs based on the probabilistic risk assessment method.