• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.387-393
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• 2010

This paper studies static crack growth of asphalt pavement using the extended finite element method (XFEM). To consider nonlinear characteristics of asphalt concrete, a viscoelastic constitutive equation using the Maxwell chain is used. And a linear cohesive crack model is used to regularize the crack. Instead of constructing the viscoelastic constitutive law from the Prony approximation of compliance and retardation time measured experimentally, we use a smooth log-power function which optimally fits experimental data and is infinitely differentiable. The partial moduli of the Maxwell chain from the log-power function make analysis easy because they change more smoothly in a more stable way than the ordinary method such as the least square method. Using the developed method, we can simulates the static crack growth test results satisfactorily.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3A
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• pp.447-455
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• 2006

A general purpose of 4-node co-rotational resultant shell element is developed for the solution of nonlinear problems of reinforced concrete, steel and fiber-reinforced composite structures. The formulation of the geometrical stiffness presented here is defined on the mid-surface by using the second order kinematic relations and is efficient for analyzing thick plates and shells by incorporating bending moment and transverse shear resultant forces. The present element is free of shear locking behavior by using the ANS (Assumed Natural Strain) method such that the element performs very well as thin shells. Inelastic behaviour of concrete material is based on the plasticity with strain hardening and elasto-plastic fracture model. The plasticity of steel is based on Von-Mises Yield and Ivanov Yield criteria with strain hardening. The transverse shear stiffness of laminate composite is defined by an equilibrium approach instead of using the shear correction factor. The proposed formulation is computationally efficient and versitile for most civil engineering application and the test results showed good agreement.

• Animal Bioscience
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• v.37 no.3
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• pp.481-491
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• 2024

Objective: This study determined fermentation characteristics of commonly used feedstuffs, especially tropical roughages, for dairy cattle in Southeast Asia. This information is considered relevant in the context of the observed low milk fat content and milk production in Southeast Asia countries. Methods: A total of 29 feedstuffs commonly used for dairy cattle in Vietnam and Thailand were chemically analysed and subjected to an in vitro gas production (GP) test. For 72 h, GP was continuously recorded with fully automated equipment and methane (CH₄) was measured at 0, 3, 6, 9, 12, 24, 30, 36, 48, 60, and 72 h of incubation. A triphasic, nonlinear, regression procedure was applied to analyse GP profiles while a monophasic model was used to obtain kinetics related to CH₄ production. Results: King grass and VA06 showed a high asymptotic GP related to the soluble- and non-soluble fractions (i.e. A1 and A2, respectively) and had the highest acetate to propionate ratio in the incubation fluid. The proportion of CH₄ produced (% of GP at 72 h) was found to be not different (p>0.05) between the various grasses. Among the selected preserved roughages (n = 6) and whole crops (n = 4), sorghum was found to produce the greatest amount of gas in combination with a relatively low CH₄ production. Conclusion: Grasses belonging to the genus Pennisetum, and whole crop sorghum can be considered as suitable ingredients to formulate dairy rations to enhance milk fat content in Vietnam/Thailand.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.649-656
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• 2011

A model for the numerical simulation of lithium-ion batteries (LIBs) is developed for use in battery cell design, with a view to improving the performances of such batteries. The model uses Newman-type electrochemical and transfer $theories^{(1,2)}$ to describe the behavior of the lithium-ion cell, together with the Levenberg-Marquardt optimization scheme to estimate the performance or design parameters in nonlinear problems. The mathematical model can provide an insight into the mechanism of LIB behavior during the charging/discharging process, and can therefore help to predict cell performance. Furthermore, by means of least-squares fitting to experimental discharge curves measured at room temperature, we were able to obtain the values of transport and kinetic parameters that are usually difficult to measure. By comparing the calculated data with the life-test discharge curves (SB LiMotive cell), we found that the capacity fade is strongly dependent on the decrease in the reaction area of active materials in the anode and cathode, as well as on the electrolyte diffusivity.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• Resources Recycling
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• v.29 no.3
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• pp.11-23
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• 2020

This study aims to investigate breakage and liberation characteristics of iron ore from Shinyemi mine, Jeongseon by ball mill. Parameters of breakage functions for three grade samples of iron ore were obtained using single-sized-feed breakage test and back-calculation based on nonlinear programming. The results showed that with the increase in the grade of iron ore, the breakage rate factor decrease whereas the particle size sensitivity decreases. This results from retardation of microcrack-propagation by magnetite grain in the ore. Breakage distribution analysis showed that the breakage mechanism appear to be impact fracture dominant with the increase of grade owing to the stress distribution effect by magnetite grain. Degree of liberation (DOL) increased with the increase in grade and decrease in particle size, respectively. Using the breakage function and size-DOL relationship, a model that can predict time-dependent-DOL is established. When scale-up factors from operating condition are available, the model is expected to be capable of predicting size and DOL with time in actual mining process.

• Journal of the Korean GEO-environmental Society
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• v.24 no.9
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• pp.15-24
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• 2023

The behavior of buried pipes is directly influenced by the nonlinearity and complex characteristics of the surrounding soil. However, the simplified beam-spring model, which ignores the nonlinearity and complex behavior of soil, is commonly used in practice. In response, several studies have employed continuum analysis methods to account for the nonlinear and complex behavior of the soil. This paper presents various numerical continuum analysis techniques and verifies their comparison with full-scale tests. The study found that reaction force results close to the full-scale test could be obtained by applying contact surface characteristics that take into account the interaction between the ground and the buried pipe. In the case of sharing pipe and soil node method and ignoring the interaction between pipe and soil, excessive reaction force was derived, and the failure shapes were different. In addition, this study applied the dynamic explicit analysis method, ALE method, and CEL method. It was confirmed that the displacement-reaction relationship and failure shape are similar to those of the static analysis.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.