• Steel and Composite Structures
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• v.17 no.5
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• pp.641-665
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• 2014

This paper presents the analytical solutions for the size-dependent static analysis of the functionally graded (FG) beams with various boundary conditions based on the nonlocal continuum model. The nonlocal behavior is described by the differential constitutive model of Eringen, which enables to this model to become effective in the analysis and design of nanostructures. The elastic modulus of beam is assumed to vary through the thickness or longitudinal directions according to the power law. The governing equations are derived by using the nonlocal continuum theory incorporated with Euler-Bernoulli beam theory. The explicit solutions are derived for the static behavior of the transversely or axially FG beams with various boundary conditions. The verification of the model is obtained by comparing the current results with previously published works and a good agreement is observed. Numerical results are presented to show the significance of the nonlocal effect, the material distribution profile, the boundary conditions, and the length of beams on the bending behavior of nonlocal FG beams.

• Geomechanics and Engineering
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• v.17 no.4
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• pp.385-392
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• 2019

The present fractional-order plasticity models for granular soil are mainly established under the triaxial compression condition, due to its difficult in analytically solving the fractional differentiation of the third stress invariant, e.g., Lode's angle. To solve this problem, a three dimensional fractional-order elastoplastic model based on the transformed stress method, which does not rely on the analytical solution of the Lode's angle, is proposed. A nonassociated plastic flow rule is derived by conducting the fractional derivative of the yielding function with respect to the stress tensor in the transformed stress space. All the model parameters can be easily determined by using laboratory test. The performance of this 3D model is then verified by simulating multi series of true triaxial test results of rockfill.

• Steel and Composite Structures
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• v.32 no.2
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• pp.173-187
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• 2019

This study is concerned with the stability of laminated composite plates modelled using Eringen's nonlocal differential model (ENDM) and a novel refined-hyperbolic-shear-deformable plate theory. The plate is assumed to be lying on the Pasternak elastic foundation and is under the influence of an in-plane magnetic field. The governing equations and boundary conditions are obtained through Hamilton's principle. An analytical approach considering Navier series is used to fine the critical bucking load. After verifying with existing results for the reduced cases, the present model is then used to study buckling of the laminated composite plate. Numerical results demonstrate clearly for the first time the roles of size effects, magnetic field, foundation parameters, moduli ratio, geometry, lay-up numbers and sequences, fiber orientations, and boundary conditions. These results could be useful for designing better composites and can further serve as benchmarks for future studies on the laminated composite plates.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.140-147
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• 2022

In this study, in order to efficiently detect data patterns and outliers in time series data, outlier detection processing is performed for each section based on a smart contract in the data preprocessing process, and parameters for the ARIMA model are determined by generating and reflecting the significance and outlier-related parameters of the data. It was created and applied to the modified arithmetic expression to lower the data abnormality. To evaluate the performance of this study, the normality of the data was compared and evaluated when the parameters of the general ARIMA model and the ARIMA model through this study were applied, and a performance improvement of more than 6% was confirmed.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1614-1623
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• 2021

In this paper, the purpose is evaluation of the effect of using fractal feature in machine learning based pancreatic tumor classification. We used the data that Pancreas CT series 469 case including 1995 slice of benign and 1772 slice of malignant. Feature selection is implemented from 109 feature to 7 feature by Lasso regularization. In Fractal feature, fractal dimension is obtained by box-counting method, and hurst coefficient is calculated range data of pixel value in ROI. As a result, there were significant differences in both benign and malignancies tumor. Additionally, we compared the classification performance between model without fractal feature and model with fractal feature by using support vector machine. The train model with fractal feature showed statistically significant performance in comparison with train model without fractal feature.

• Annual Conference of KIPS
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• 2023.05a
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• pp.325-327
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• 2023

Diabetes is a common chronic disease that threatens human life and health, and its prevalence remains high because its mechanisms are complex, further its etiology remains unclear. According to the International Diabetes Federation (IDF), there are 463 million cases of diabetes in adults worldwide, and the number is growing. This study aims to explore the potential influencing factors of diabetes by learning data from the UCI diabetes dataset, which is a multivariate time series dataset. In this paper we propose the TCN-prophet model for diabetes. The experimental results show that the prediction of insulin concentration by the TCN-prophet model provides a high degree of consistency, compared to the existing LSTM model.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.179-188
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• 2017

It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

• Korean Security Journal
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• no.19
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• pp.1-21
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• 2009

Manpower demand forecasting in private security industry can be used for both policy and information function. At a time when police agencies have fewer resources to accomplish their goals, forming partnership with private security firms should be a viable means to choose. But without precise understanding of each other, their partnership could be superficial. At the same time, an important debate is coming out whether security industry will continue to expand in numbers of employees, or level-off in the near future. Such debates are especially important for young people considering careers in private security industry. Recently, ARIMA model has been widely used as a reliable instrument in the many field of industry for demand forecasting. An ARIMA model predicts a value in a response time series as a linear combination of its own past values, past errors, and current and past values of other time series. This study conducts a short-term forecast of manpower demand in private security industry using ARIMA model. After obtaining yearly data of private security officers from 1976 to 2008, this paper are forecasting future trends and proposing some policy orientations. The result shows that ARIMA(0, 2, 1) model is the most appropriate one and forecasts a minimum of 137,387 to maximum 190,124 private security officers will be needed in 2013. The conclusions discuss some implications and predictable changes in policing and coping strategies public police and private security can take.