• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.129-136
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• 2005

The damped least-squares inversion has become a most popular method in finding the solution in geophysical problems. Generally, the least-squares inversion is to minimize the object function which consists of data misfits and model constraints. Although both the data misfit and the model constraint take an important part in the least-squares inversion, most of the studies are concentrated on what kind of model constraint is imposed and how to select an optimum regularization parameter. Despite that each datum is recommended to be weighted according to its uncertainty or error in the data acquisition, the uncertainty is usually not available. Thus, the data weighting matrix is inevitably regarded as the identity matrix in the inversion. We present a new inversion scheme, in which the data weighting matrix is automatically obtained from the analysis of the data resolution matrix and its spread function. This approach, named 'extended active constraint balancing (EACB)', assigns a great weighting on the datum having a high resolution and vice versa. We demonstrate that by applying EACB to a two-dimensional resistivity tomography problem, the EACB approach helps to enhance both the resolution and the stability of the inversion process.

• 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.

• Proceedings of the Korea Crystallographic Association Conference
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• 2003.05a
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• pp.8-8
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• 2003

Electron-phonon interaction plays a significant role in forming of colossal magnetoresistance effect (CMR). Polaron formation was observed by neutron diffraction and by extended X-ray absorption fine structure (EXAFS) analysis. Local probe as given by the EXAFS is a useful method to study the polaronic charge and its dependence on temperature and ions size. Here we present the EXAFS study of polaronic charge in La/sub 0.7/Ca/sub 0.3-X/Ba/sub X/MnO₃ compositions. The single phase La/sub 0.7/Ca/sub 0.3-X/Ba/sub X/MnO₃ manganites (x=0; 0.03; 0.06, ..., 0.3) were prepared by ceramic technology [1]. The Curie temperature was determined by extrapolation of the temperature dependence of the magnetization (down to zero magnetization). EXAFS experiments were carried out at the 7C EC beam line of the Pohang Light Source (PLS) in Korea. The atomic pair distribution functions (PDF) were obtained by re-regularization method [2] from filtered spectra. The PDF for the x=0.3 sample showed a single peak function and for x=0.0, 0.03, 0.06, 0.09, 0.12 compositions were asymmetric in agreement with a small Jahn-Teller elongation of two (short and long) bonds of the MnO/sub 6/ octahedron. Dispersion, σ/sub Min-O//sup 2/, and asymmetry, σ/sub Min-O//sup 3/, of the Mn-O bond distances varied significantly with x and showed a maximums at x=0.09. The maximum of σ/sub Min-O//sup 2/ is caused by increase of dynamic rms displacements of the Mn-O distances near the T/sub C/. The observed x dependence of σ/sub Min-O//sup 3/ reflects the reduction of charge carriers mobility at approaching to T/sub C/ from low as well as high temperatures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.19-28
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• 2017

In this paper, detailed safety inspection reports on the 915 expressway bridges which had been published from 1996 to 2010 are collected and condition of these bridges are analyzed. Damages are categorized into 'damage by defect', 'damage by physical force', and 'damage by deterioration' and the concept of damage possession rate is introduced to investigate the occurrence time and the characteristics of damages. Based on the top 10 damage patterns of expressway bridges and the deterioration characteristics of heavy snow and freezing cold area, reasonable improvement direction of inspection activity is suggested. From this study, it is known that improvement of inspection regularization during construction or at completion stage of bridges is needed. Since the deterioration progress of the heavy snow and freezing cold area is faster than that of general area, environmental characteristics should be considered in inspection activity. The results of present study can be widely used for improvement of inspection activity of expressway bridges.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.258-268
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• 2019

The purpose of this study was to investigate the effects of stress factors on fatigue and depression of aesthetician. The subjects of this study were 133 Aesthetician in Busan Metropolitan City. Analysis was done by frequency analysis, t-test, one-way ANOVA and regression analysis. The results of this study are as follows. First, the aesthetician showed an average 2.57 of job stress. Second, the aesthetician showed an average 2.45 of socio-psychological stress. Third, the aesthetician showed an average fatigue(4.16) and depression(1.54). Therefore, in order to reduce the duties and socio-psychological stress of the aesthetician, fatigue and depression are reduced through regularization of the employees, adjustment of legal working hours, and establishment of similar paying system. Finally, it is necessary to lower the turnover rate of the aesthetician and increase the job satisfaction.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.817-824
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• 2007

Tractography using Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvector in the white matter of the brain. However, the fiber tracking methods suffer from the noise included in the diffusion tensor images that affects the determination of the principal eigenvector. As the fiber tracking progresses, the accumulated error creates a large deviation between the calculated fiber and the real fiber. This problem of the DT-MRI tractography is known mathematically as the ill-posed problem which means that tractography is very sensitive to perturbations by noise. To reduce the noise in DT-MRI measurements, a tensor-valued median filter which is reported to be denoising and structure-preserving in fiber tracking, is applied in the tractography. In this paper, we proposed the modified gradient descent method which converges fast and accurately to the optimal tensor-valued median filter by changing the step size. In addition, the performance of the modified gradient descent method is compared with others. We used the synthetic image which consists of 45 degree principal eigenvectors and the corticospinal tract. For the synthetic image, the proposed method achieved 4.66%, 16.66% and 15.08% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively. For the corticospinal tract, at iteration number ten the proposed method achieved 3.78%, 25.71 % and 11.54% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively.

• Journal of The Korean Association of Information Education
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• v.17 no.2
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• pp.191-198
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• 2013

There are many studies about cognitive and non-cognitive characteristics of gifted children in the areas of math and science until now. Also, there is a lot of research for about cognitive and non-cognitive characteristics of gifted children. But, it lacks a lot of research on the characteristics of gifted children for information science area. So, characteristics of gifted children in the areas of information science are defined as structured information recall ability, regularization ability, reasoning ability, efficiency ability, structured ability, generalization ability, and abstract ability. And real problems for each ability are proposed. To make the evaluation questions proposed in this study on the cognitive gifted characteristics when compared with student achievement and prove that there is a correlation. The results of this study can be utilized in the evaluation of information giftedness children and can be utilized in the development of gifted education programs.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.1-10
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• 2001

Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.

• Structural Monitoring and Maintenance
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• v.6 no.4
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• pp.317-346
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• 2019

Performance assessment of pavements proves useful, in terms of handling the ride quality, controlling the travel time of vehicles and adequate maintenance of pavements. Roughness profiles provide a good measure of the deteriorating condition of the pavement. For the accurate estimates of pavement roughness from dynamic vehicle responses, vehicle parameters should be known accurately. Information on vehicle parameters is uncertain, due to the wear and tear over time. Hence, condition monitoring of pavement requires the identification of pavement roughness along with vehicle parameters. The present study proposes a scheme which estimates the roughness profile of the pavement with the use of accurate estimates of vehicle parameters computed in parallel. Pavement model used in this study is a two-layer Euler-Bernoulli beam resting on a nonlinear Pasternak foundation. The asphalt topping of the pavement in the top layer is modeled as viscoelastic, and the base course bottom layer is modeled as elastic. The viscoelastic response of the top layer is modeled with the help of the Burgers model. The vehicle model considered in this study is a half car model, fitted with accelerometers at specified points. The identification of the coupled system of vehicle-pavement interaction employs a coupled scheme of an unbiased minimum variance estimator and an optimization scheme. The partitioning of observed noisy quantities to be used in the two schemes is investigated in detail before the analysis. The unbiased minimum variance estimator (MVE) make use of a linear state-space formulation including roughness, to overcome the linearization difficulties as in conventional nonlinear filters. MVE gives estimates for the unknown input and fed into the optimization scheme to yield estimates of vehicle parameters. The issue of ill-posedness of the problem is dealt with by introducing a regularization equivalent term in the objective function, specifically where a large number of parameters are to be estimated. Effect of different objective functions is also studied. The outcome of this research is an overall measure of pavement condition.