• Steel and Composite Structures
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• v.48 no.2
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• pp.113-130
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• 2023

The present manuscript aims to investigate the deviation between the middle surface (MS) and neutral surface (NS) formulations on the static response of bi-directionally functionally graded (BDFG) porous plate. The higher order shear deformation plate theory with a four variable is exploited to define the displacement field of BDFG plate. The displacement field variables based on both NS and on MS are presented in detail. These relations tend to get and derive a new set of boundary conditions (BCs). The porosity distribution is portrayed by cosine function including three different configurations, center, bottom, and top distributions. The elastic foundation including shear and normal stiffnesses by Winkler-Pasternak model is included. The equilibrium equations based on MS and NS are derived by using Hamilton's principles and expressed by variable coefficient partial differential equations. The numerical differential quadrature method (DQM) is adopted to solve the derived partial differential equations with variable coefficient. Rigidities coefficients and stress resultants for both MS and NS formulations are derived. The mathematical formulation is proved with previous published work. Additional numerical and parametric results are developed to present the influences of modified boundary conditions, NS and MS formulations, gradation parameters, elastic foundations coefficients, porosity type and porosity coefficient on the static response of BDFG porous plate. The following model can be used in design and analysis of BDFG structure used in aerospace, vehicle, dental, bio-structure, civil and nuclear structures.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Advances in nano research
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• v.10 no.2
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• pp.115-128
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• 2021

In this article, free vibration behavior of electro-magneto-thermo sandwich Timoshenko beam made of porous core and Graphene Platelet Reinforced Composite (GPLRC) in a thermal environment is investigated. The governing equations of motion are derived by using the modified strain gradient theory for micro structures and Hamilton's principle. The magneto electro are under linear function along the thickness that contains magnetic and electric constant potentials and a cosine function. The effects of material length scale parameters, temperature change, various distributions of porous, different distributions of graphene platelets and thickness ratio on the natural frequency of Timoshenko beam are analyzed. The results show that an increase in aspect ratio, the temperature change, and the thickness of GPL leads to reduce the natural frequency; while vice versa for porous coefficient, volume fractions and length of GPL. Moreover, the effect of different size-dependent theories such as CT, MCST and MSGT on the natural frequency is investigated. It reveals that MSGT and CT have most and lowest values of natural frequency, respectively, because MSGT leads to increase the stiffness of micro Timoshenko sandwich beam by considering three material length scale parameters. It is seen that by increasing porosity coefficient, the natural frequency increases because both stiffness and mass matrices decreases, but the effect of reduction of mass matrix is more than stiffness matrix. Considering the piezo magneto-electric layers lead to enhance the stiffness of a micro beam, thus the natural frequency increases. It can be seen that with increasing of the value of WGPL, the stiffness of microbeam increases. As a result, the value of natural frequency enhances. It is shown that in hc/h = 0.7, the natural frequency for WGPL = 0.05 is 8% and 14% less than its for WGPL = 0.06 and WGPL = 0.07, respectively. The results show that with an increment in the length and width of GPLs, the natural frequency increases because the stiffness of micro structures enhances and vice versa for thickness of GPLs. It can be seen that the natural frequency for aGPL = 25 ㎛ and hc/h = 0.6 is 0.3% and 1% more than the one for aGPL = 5 ㎛ and aGPL = 1 ㎛, respectively.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Ocean and Polar Research
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• v.39 no.1
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• pp.13-21
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• 2017

Temporal changes in the number of zooplankton species are important information for understanding basic characteristics and species diversity in marine ecosystems. The aim of the present study was to estimate the optimal monitoring frequency (OMF) to guarantee and predict the minimum number of species occurrences for studies concerning marine ecosystems. The OMF is estimated using the temporal number of zooplankton species through bi-weekly monitoring of zooplankton species data according to operational taxonomic units in the Tongyoung coastal sea. The optimal model comprises two terms, a constant (optimal mean) and a cosine function with a one-year period. The confidence interval (CI) range of the model with monitoring frequency was estimated using a bootstrap method. The CI range was used as a reference to estimate the optimal monitoring frequency. In general, the minimum monitoring frequency (numbers per year) directly depends on the target (acceptable) estimation error. When the acceptable error (range of the CI) increases, the monitoring frequency decreases because the large acceptable error signals a rough estimation. If the acceptable error (unit: number value) of the number of the zooplankton species is set to 3, the minimum monitoring frequency (times per year) is 24. The residual distribution of the model followed a normal distribution. This model can be applied for the estimation of the minimal monitoring frequency that satisfies the target error bounds, as this model provides an estimation of the error of the zooplankton species numbers with monitoring frequencies.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• The Journal of the Acoustical Society of Korea
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• v.23 no.8
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• pp.554-561
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• 2004

As the technologies of semiconductor and multimedia communication have been improved. the high-quality video and the multi-channel audio have been highlighted. MPEG Audio Layer 3 decoder has been implemented as a Processor using a standard. Since the synthesis filter of MPEG-1 Audio Layer 3 decoder requires the most outstanding operation in the entire decoder. the synthesis filter that can reduce the amount of operation is needed for the design of the high-speed processor. Therefore, in this paper, the synthesis filter. the most important part of MPEG Audio, is materialized in FPGA using the method of DAULT (distributed arithemetic look-up table). For the design of high-speed synthesis filter, the DAULT method is used instead of a multiplier and a Pipeline structure is used. The Performance improvement by 30% is obtained by additionally making the result of multiplication of data with cosine function into the table. All hardware design of this Paper are described using VHDL (VHIC Hardware Description Language) Active-HDL 6.1 of ALDEC is used for VHDL simulation and Synplify Pro 7.2V is used for Model-sim and synthesis. The corresponding library is materialized by XC4013E and XC4020EX. XC4052XL of XILINX and XACT M1.4 is used for P&R tool. The materialized processor operates from 20MHz to 70MHz.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.213-220
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• 2023

This paper presents a model simulating the folding process and collision dynamics of "ddakji", a traditional Korean game played using paper tiles (which are also referred to as ddakji). The model uses two A4 sheets as the base materials for ddakji. The folding process involves a series of boundary conditions that transform the wing part of the paper structure into a twisted configuration. A rigid plate boundary condition is also adopted for squeezing, establishing the shape and stress state of the game-ready ddakji through dynamic relaxation analysis. The gaming process analysis involves a forced displacement of the striking ddakji to a predetermined collision position. Collision analysis then follows at a given speed, with the objective of overturning the struck ddakji--a winning condition. A genetic algorithm-based optimization analysis identifies the optimal collision conditions that result in the overturning of the struck ddakji. For efficiency, the collision analysis is divided into two stages, with the second stage carried out only if the first stage predicts a possible overturn. The fitness function for the genetic algorithm during the first stage is the direction cosine of the struck ddakji, whereas in the second stage, it is the inverse of the speed, thus targeting the lowest overall collision speed. Consequently, this analysis provides optimal collision conditions for various compression thicknesses.