• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• Interaction and multiscale mechanics
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• v.1 no.2
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• pp.211-230
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• 2008

Owing to the growing size of the eigenvalue problem and the growing number of eigenvalues desired, solution methods of iterative nature are becoming more popular than ever, which however suffer from low efficiency and lack of proper convergence criteria. In this paper, three efficient iterative eigenvalue algorithms are considered, i.e., subspace iteration method, iterative Ritz vector method and iterative Lanczos method based on the cell sparse fast solver and loop-unrolling. They are examined under the mode error criterion, i.e., the ratio of the out-of-balance nodal forces and the maximum elastic nodal point forces. Averagely speaking, the iterative Ritz vector method is the most efficient one among the three. Based on the mode error convergence criteria, the eigenvalue solvers are shown to be more stable than those based on eigenvalues only. Compared with ANSYS's subspace iteration and block Lanczos approaches, the subspace iteration presented here appears to be more efficient, while the Lanczos approach has roughly equal efficiency. The methods proposed are robust and efficient. Large size tests show that the improvement in terms of CPU time and storage is tremendous. Also reported is an aggressive shifting technique for the subspace iteration method, based on the mode error convergence criteria. A backward technique is introduced when the shift is not located in the right region. The efficiency of such a technique was demonstrated in the numerical tests.

• Bulletin of the Korean Mathematical Society
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• v.29 no.1
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• pp.125-135
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• 1992

In various viscus flow problems it has been the custom to replace the convective derivative by the ordinary partial derivative in problems for which the data are small. In this paper we consider the Benard Convection problem with small data and compare the solution of this problem (assumed to exist) with that of the linearized system resulting from dropping the nonlinear terms in the expression for the convective derivative. The objective of the present work is to derive an estimate for the error introduced in neglecting the convective inertia terms. In fact, we derive an explicit bound for the L$_{2}$ error. Indeed, if the initial data are O(.epsilon.) where .epsilon. << 1, and the Rayleigh number is sufficiently small, we show that this error is bounded by the product of a term of O(.epsilon.$^{2}$) times a decaying exponential in time. The results of the present paper then give a justification for linearizing the Benard Convection problem. We remark that although our results are derived for classical solutions, extensions to appropriately defined weak solutions are obvious. Throughout this paper we will make use of a comma to denote partial differentiation and adopt the summation convention of summing over repeated indices (in a term of an expression) from one to three. As reference to work of continuous dependence on modelling and initial data, we mention the papers of Payne and Sather [8], Ames [2] Adelson [1], Bennett [3], Payne et al. [9], and Song [11,12,13,14]. Also, a similar analysis of a micropolar fluid problem backward in time (an ill-posed problem) was given by Payne and Straughan [10] and Payne [7].

• Imaging Science in Dentistry
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• v.42 no.4
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• pp.207-212
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• 2012

Purpose: This study was performed to determine the relative frequency of positioning errors, to identify those errors directly responsible for diagnostically inadequate images, and to assess the quality of panoramic radiographs in a sample of records collected from a dental college. Materials and Methods: This study consisted of 1,782 panoramic radiographs obtained from the Department of Oral and Maxillofacial Radiology. The positioning errors of the radiographs were assessed and categorized into nine groups: the chin tipped high, chin tipped low, a slumped position, the patient positioned forward, the patient positioned backward, failure to position the tongue against the palate, patient movement during exposure, the head tilted, and the head turned to one side. The quality of the radiographs was further judged as being 'excellent', 'diagnostically acceptable', or 'unacceptable'. Results: Out of 1,782 radiographs, 196 (11%) were error free and 1,586 (89%) were present with positioning errors. The most common error observed was the failure to position the tongue against the palate (55.7%) and the least commonly experienced error was patient movement during exposure (1.6%). Only 11% of the radiographs were excellent, 64.1% were diagnostically acceptable, and 24.9% were unacceptable. Conclusion: The positioning errors found on panoramic radiographs were relatively common in our study. The quality of panoramic radiographs could be improved by careful attention to patient positioning.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.209-218
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• 2015

Computer simulation plays an important role for a theoretical foundation in convergence technology and the interpolation is to know the unknown values from known values on grid points. Therefore it is an important problem to select an interpolation method for digital simulation. The aim of this paper is to compare analysis of interpolation methods for digital simulation. we test six different interpolation methods namely: Quartic-Lagrangian, Cubic Spline, Fourier, Hermit, PWENO and SL-WENO. Through digital simulation of a linear advection equation, we analyse pros and cons for each method. In order to compare performance, we introduce accuracy computing and Error functions. The accuracy computing is used well-known $L^1-norm$ and the Error functions are dispersion function, dissipation function and total error function. High-order methods well apply to computer simulation, unfortunately, side-effects (Oscillation) happen.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.8-16
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• 2023

In Japan, high-speed ground transportation service using linear motors at speeds of 500 km/h is scheduled to begin in 2027. To accommodate 5G services in trains, a subcarrier spacing frequency of 30 kHz will be used instead of the typical 15 kHz subcarrier spacing to mitigate Doppler effects in such high-speed transport. Furthermore, to increase the cell size of the 5G mobile system, multiple base station antennas will transmit identical downlink (DL) signals to form an expanded cell size along the train rails. In this situation, the forward and backward antenna signals are Doppler-shifted in opposite directions, respectively, so the receiver in the train may suffer from estimating the exact Channel Transfer Function (CTF) for demodulation. In a previously published paper, we proposed a channel estimator based on Delay and Doppler Profiler (DDP) in a 5G SISO (Single Input Single Output) environment and successfully implemented it in a signal processing simulation system. In this paper, we extend it to 2×2 MIMO (Multiple Input Multiple Output) with spatial multiplexing environment and confirm that the delay and DDP based channel estimator is also effective in 2×2 MIMO environment. Its simulation performance is compared with that of a conventional time-domain linear interpolation estimator. The simulation results show that in a 2×2 MIMO environment, the conventional channel estimator can barely achieve QPSK modulation at speeds below 100 km/h and has poor CNR performance versus SISO. The performance degradation of CNR against DDP SISO is only 6dB to 7dB. And even under severe channel conditions such as 500km/h and 8-path inverse Doppler shift environment, the error rate can be reduced by combining the error with LDPC to reduce the error rate and improve the performance in 2×2 MIMO. QPSK modulation scheme in 2×2 MIMO can be used under severe channel conditions such as 500 km/h and 8-path inverse Doppler shift environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.123-128
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• 2014

This paper presents the study about MGV(Magnetic guidance vehicle) with up-and-down rotating type differential drive unit. Previous MGV needs the landmarks to get the driving information and additional sensor to recognize the landmarks except for localization sensor. Previous MGV requires at least 2 drive units when common fixed differential drive unit is used because it occurs the problems with driving control and localization error from imbalance of the MGV's weight. To solve such problems, we propose the MGV using up-and-down rotating type differential drive unit. Proposed MGV recognizes the driving information from the pattern which is consisted of both pole of magnet without landmarks and additional sensors, and it control the backward movement using up-and-down rotating type differential drive unit instead of common drive units. Proposed MGV considers KF(Kalman filter) to improve the localization accuracy. To verify the performance of proposed method, we designed MGV for the experiment. As the results, we can confirm the performance of propoesed method to recognize the pattern and to control the backward movement. With respect to localization, proposed method has the less RMSE about 5.6904 mm than previous method.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.122-132
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• 2010

AT-DMB system has been developed to increase data rate up to double of conventional T-DMB in the same bandwidth while maintaining backward compatibility. The AT-DMB system adopted hierarchical modulation which adds BPSK or QPSK signal as enhancement layer to existing DQPSK signal. The enhancement layer signal should be small enough to maintain backward compatibility and to minimize the coverage loss of conventional T-DMB service coverage. But this causes the enhancement layer signal of AT-DMB susceptible to fading effect in transmission channel. A turbo code which has improved error correction capability than convolutional code, is applied to the enhancement layer signal of the AT-DMB system for compensating channel distortion. However there is a need for other solutions for better reception of AT-DMB signal in receiver side without increasing transmitting power. In this paper, we propose adaptive array antenna system with Eigen-space beamforming algorithm which benefits beamforming gain along with diversity gain. We analyzed the reception performances of AT-DMB system in indoor and mobile environments when this new smart antenna system and algorithm is introduced. The computer simulation results are presented along with analysis comments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.12
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• pp.1923-1930
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• 2004

This paper presents digital microflow controllers(DMFC), where a fluidic digital-to-analog converter(DAC) is used to achieve high-linearity, fine-level flow control for applications to precision biomedical dosing systems. The fluidic DAC, composed of binary-weighted flow resistance, controls the flow-rate based on the ratio of the flow resistance to achieve high-precision flow-rate control. The binary-weighted flow resistance has been specified by a serial or a parallel connection of an identical flow resistor to improve the linearity of the flow-rate control, thereby making the flow-resistance ratio insensitive to the size uncertainty in flow resistors due to micromachining errors. We have designed and fabricated three different types of 4-digit DMFC: Prototype S and P are composed of the serial and the parallel combinations of an identical flow resistor, while Prototype V is based on the width-varied flow resistors. In the experimental study, we perform a static test for DMFC at the forward and backward flow conditions as well as a dynamic tests at pulsating flow conditions. The fabricated DMFC shows the nonlinearity of 5.0% and the flow-rate levels of 16(2$^{N}$) for the digital control of 4(N) valves. Among the 4-digit DMFC fabricated with micromachining errors, Prototypes S and P show 27.2% and 27.6% of the flow-rate deviation measured from Prototype V, respectively; thus verifying that Prototypes S and P are less sensitive to the micromachining error than Prototype V.V.