• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• Land and Housing Review
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• v.3 no.1
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• pp.79-82
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• 2012

While there exists a relatively large body of technical information for the engineered design of wood-frame buildings to resist seismic ground motions, the quantitative assessment of seismic resistance of conventional houses built by prescriptive requirements is less well understood. Forintek Canada Corp., in collaboration with other research and industry partners, has embarked on a research project to address this topic. This paper will report on the seismic shake table tests of a full-scale wood-frame building. The two-story specimen, $6m{\times}6m$ in plan, was built on the seismic shake table at Tongji University in Shanghai, China, according to Part 9 of the 1995 National Building Code of Canada and shaken uni-directionally in each of the two principal directions. Three different seismic table motions were applied at increasing peak ground motion amplitudes up to 0.40 and 0.50 g. The specimen was repaired after the above sets of seismic table motions, and successive runs were conducted for increased door openings. Measurements included specimen accelerations, displacements and anchorage forces. Static stiffness of the specimen was measured at low force levels, and natural frequencies were measured after each seismic loading stage by applying low-level random excitation. The results presented consist of the capacity spectra of the shake table tests, changes in specimen stiffness and natural frequencies with increasing seismic loading. These results and those from other recent shake table tests elsewhere will be compared with simplified engineering calculations based on codified values of strength, and on that basis preliminary conclusions will be drawn on the adequacy of the current code provisions and design guides in Canada and the USA for conventional wood-frame construction.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.111-120
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• 2016

In usual, the discontinuous variance function was estimated nonparametrically using a kernel type estimator with data sets split by an estimated location of the change point. Kang et al. (2000) proposed the Gasser-$M{\ddot{u}}ller$ type kernel estimator of the discontinuous regression function using the adjusted observations of response variable by the estimated jump size of the change point in $M{\ddot{u}}ller$ (1992). The adjusted observations might be a random sample coming from a continuous regression function. In this paper, we estimate the variance function using the Nadaraya-Watson kernel type estimator using the adjusted squared residuals by the estimated location of the change point in the discontinuous variance function like Kang et al. (2000) did. The rate of convergence of integrated squared error of the proposed variance estimator is derived and numerical work demonstrates the improved performance of the method over the exist one with simulated examples.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Structural Engineering and Mechanics
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• v.56 no.6
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.365-372
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• 2000

he paper introduces evolutionary computing to Fractal Image Compression(FIC). In Fractal Image Compression(FIC) a partitioning of the image into ranges is required. As a solution to this problem there is a propose that evolution computation should be applied in image partitionings. Here ranges are connected sets of small square image blocks. Populations consist of $N_p$ configurations, each of which is a partitioning with a fractal code. In the evolution each configuration produces $\sigma$ children who inherit their parent partitionings except for two random neighboring ranges which are merged. From the offspring the best ones are selected for the next generation population based on a fitness criterion Collage Theorem. As the optimum image includes duplication in image data, it gets smaller in saving space more efficient in speed and more capable in image quality than any other technique in which other coding is used. Fractal Image Compression(FIC) using evolution computation in multimedia image processing applies to such fields as recovery of image and animation which needs a high-quality image and a high image-compression ratio.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1759-1769
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• 1997

The Error BackPropagation (EBP) algorithm for multi-layered neural networks is widely used in various areas such as associative memory, speech recognition, pattern recognition and robotics, etc. Nevertheless, many researchers have continuously published papers about improvements over the original EBP algorithm. The main reason for this research activity is that EBP is exceeding slow when the number of neurons and the size of training set is large. In this study, we developed new learning speed acceleration methods using variable learning rate, variable momentum rate and variable slope for the sigmoid function. During the learning process, these parameters should be adjusted continuously according to the total error of network, and it has been shown that these methods significantly reduced learning time over the original EBP. In order to show the efficiency of the proposed methods, first we have used binary data which are made by random number generator and showed the vast improvements in terms of epoch. Also, we have applied our methods to the binary-valued Monk's data, 4, 5, 6, 7-bit parity checker and real-valued Iris data which are famous benchmark training sets for machine learning.

• Journal of Microbiology and Biotechnology
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• v.17 no.5
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• pp.806-811
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• 2007

Bacillus anthracis is a soil pathogen capable of causing anthrax that is closely related to several environmental species, including B. cereus, B. mycoides, and B. thuringiensis. DNA homology studies showed that B. anthracis, B. cereus, B. mycoides, and B. thuringiensis are closely related, with a high sequence homology. To establish a method to specifically detect B. anthracis in situations such as environmental contamination, we initially performed RAPD-PCR with a 10-mer random primer and confirmed the presence of specific PCR bands only in B. anthracis species. One region specific for B. anthracis was cloned and sequenced, and an internal primer set was designed to amplify a 241-bp DNA fragment within the sequenced region. The PCR system involving these specific primer sets has practical applications. Using lyses methods to prepare the samples for PCR, it was possible to quickly amplify the 241-bp DNA segment from samples containing only a few bacteria. Thus, the PCR detection method developed in this study is expected to facilitate the monitoring of environmental B. anthracis contamination.

• Korean Journal of Applied Biomechanics
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• v.23 no.2
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• pp.117-123
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• 2013

The purpose of this study was to provide data to increase the success rate of penalty kicks through quantifying the shape of skilled kicks by performing a kinematic analysis on the change of movement during the kicking phase which the goalkeeper uses as a vital clue. Three high definition video cameras(GR-HD1KR, JVC, Japan) were used for the study and 18 reflective markers were attached to the body joints. Corners of the goal, difficult for goalkeepers to block, were set as aims and 1 m by 1.2 m targets were installed. Each subject had five sets of kicks at random, and the analysis was done on the movements that hit the target. Time, speed of the right lower limb's center of mass, joint angle, and angular velocity were chosen as factors and the results of the analysis showed statistical significance. The player taking a penalty kick should train to avoid leaning one's body towards the kicking direction and change the angle of the right foot right before the impact to decide the direction of the ball. The goalkeeper can increase the save success rate by studying the angle of the kicker's body and the right foot as well as the timing of the kick.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.7-13
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• 2011

Accurate response analysis of long span bridges subjected to seismic excitation is important for earthquake hazard mitigation. In this paper, the performance of a typical four span continuous reinforced concrete bridge model subjected to asynchronous multiple seismic excitations at the supports is investigated in both the time and frequency domains and the results are compared with that from a relevant uniform support excitations. In the time domain analysis, a linear modal superposition approach is used to compute the peak response values. In the frequency domain analysis, linear random vibration theory is used to determine the root mean square response values where the cross correlation effects between the modal and the support excitations on the seismic response of the bridge model are included. From the two sets of results, a practical range of peak factors which are defined to be the ratio of peak and the root mean square responses are suggested for displacements and forces in members. With reliable practical values of peak factors, the frequency domain analysis is preferred for the performance based design of bridges because of the computational advantage and the generality of the results as the time domain analysis only yields results for the specific excitation input.