• Structural Engineering and Mechanics
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• v.15 no.3
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• pp.299-314
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• 2003

This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.524-526
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• 1999

In the construction of successful fuzzy models and/or controllers for nonlinear systems, the identification of a good fuzzy inference system is an important yet difficult problem, which is traditionally accomplished by a time-consuming trial-and-error process. In this paper, we propose a systematic identification procedure for complex multi-input single-output nonlinear systems with DNA coding method. A DNA coding method is optimization algorithm based on biological DNA as conventional genetic algorithms(GAs) are. The strings in the DNA coding method are variable-length strings, while standard GAs work with a fixed-length coding scheme. the DNA coding method is well suited to learning because it allows a flexible representation of a fuzzy inference system. We also propose a new coding method fur applying the DNA coding method to the identification of fuzzy models. This coding scheme can effectively represent the zero-order Takagi-Sugeno(TS) fuzzy model. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a Duffing-forced oscillation system.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.16-22
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• 2010

In this paper, the diffraction problems for fixed offshore structures are solved using a hybrid scheme. In this hybrid scheme, potential-based solutions and the Navier-Stokes-based finite volume method (FVM) with a volume-of-fluid (VOF) method are combined. We introduce a buffer zone for efficient wave-making and damping. In this buffer zone, the near field solution from FVM-VOF is gradually changed to Stokes' 2nd order wave solutions. Three different models, including the truncated cylinder, sphere, and wigleyIII model, are numerically investigated in regular waves with a wave steepness of 1/30. The efficiency and accuracy of the hybrid scheme are numerically validated from results using different domain sizes and buffer zones. The wave exciting forces from the FVM-VOF simulations are compared with experiments and potential-based solutions from the higher-order boundary element method (HOBEM). This comparison shows good agreement between the hybrid scheme and potential-based solutions.

• The Journal of Korean Association of Computer Education
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• v.16 no.3
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• pp.89-98
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• 2013

In order to effectively manage after-school works from 2011, NEIS after-school system has been developed and operated. As NEIS after-school system that has been currently operating will need to analyze the effectiveness, it makes the use of this system activate. After-school elementary school teacher 273 people survey of 11 school district of the Seoul Metropolitan Office of Education was commissioned. 116 teachers participated in the survey. NEIS after-school system showed a reduction in task processing time (M = 3.92) were higher than that of most complaints about paperwork (M = 3.82), work efficiency (M = 3.81), the accuracy of your work (M = 3.76) in the order of many complaints were.

• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.3
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• pp.205-212
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• 2007

Parabolic approximation wave models based on $Pad{\acute{e}}$ approximants are analyzed in order to calculate wave transformation. In this study a $Pad{\acute{e}}(2,2)$ parabolic approximation model is developed to increase the accuracy of computation in comparison with the existing models. Numerical studies on a constant sloping bed show that the new model proves to allow for much more successful treatment of large angles of incidence than is possible using the previously available models.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.372-372
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• 2000

In this paper, we propose Neuro Fuzzy Polynomial Networks(NFPN) based on Polynomial Neural Network(PNN) and Neuro-Fuzzy(NF) for model identification of complex and nonlinear systems. The proposed NFPN is generated from the mutually combined structure of both NF and PNN. The one and the other are considered as the premise part and consequence part of NFPN structure respectively. As the premise part of NFPN, NF uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. As the consequence part of NFPN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. NFPN is available effectively for multi-input variables and high-order polynomial according to the combination of NF with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. In order to evaluate the performance of proposed models, we use the nonlinear function. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.127-134
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• 2012

The purpose of this study is to present evidence for a particular processing approach based on the language-specific characteristics of Korean. To compare individuals' sentence-comprehension abilities, this study measured the accuracy and reaction times (RT) of 12 aphasic patients (AP) and 12 normal controls (NC) during a sentence-picture matching task. Four versions of a sentence were constructed with the two types of voice (active/passive) and two types of word order (agent-first/patient-first). To examine the effects of increased processing demand, picture stimuli were manipulated in such a way that they appeared immediately after the sentence was presented. As expected, the AP group showed higher error rates and longer RT for all conditions than the NC group. Furthermore, Korean speakers with aphasia performed above a chance level in sentence comprehension, even with passive sentences. Aphasics understood sentences more quickly and accurately when they were given in the active voice and with agent-first order. The patterns of the NC group were similar. These results confirm that Korean adults with aphasia do not completely lose their knowledge of sentence comprehension. When the processing demand was increased by delaying the picture stimulus onset, the effect of increased processing demands on RT was more pronounced in the AP than in the NC group. These findings fit well with the idea that the computational system for interpreting sentences is intact in aphasics, but its ability is compromised when processing demands increase.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.127-140
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• 2016

In this paper, a new first shear deformation plate theory based on neutral surface position is developed for the static and the free vibration analysis of functionally graded plates (FGPs). Moreover, the number of unknowns of this theory is the least one comparing with the traditional first-order and the other higher order shear deformation theories. The neutral surface position for a functionally graded plate which its material properties vary in the thickness direction is determined. The mechanical properties of the plate are assumed to vary continuously in the thickness direction by a simple power-law distribution in terms of the volume fractions of the constituents. Based on the present shear deformation plate theory and the neutral surface concept, the governing equations are derived from the principle of Hamilton. There is no stretching-bending coupling effect in the neutral surface based formulation. Numerical illustrations concern flexural and dynamic behavior of FG plates with Metal-Ceramic composition. Parametric studies are performed for varying ceramic volume fraction, length to thickness ratios. The accuracy of the present solutions is verified by comparing the obtained results with the existing solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.