• Journal of the Korean Society for Precision Engineering
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• v.19 no.5
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• pp.139-148
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• 2002

Motion errors of linear motion guideway are analyzed theoretically in this paper. For the analysis, an new algorithm predicting motion errors of bearing and guideway is proposed using the Hertz's elastic deformation theory. Accuracy averaging effect can be calculated quantitatively by analyzing relationship between motion errors of guideway and spatial frequency of rail form error. Influences of design parameters on the motion errors including the number of balls, preload, ball diameter, bearing length and the number of bearings are analyzed. As it is difficult to measure the rail form error, experimental results are compared with results analyzed by the equivalent analysis method which evaluate the motion errors of guideway using the measured errors of bearing. From the experimental results, it is confirmed that the proposed analysis method it effective lo analyze the motion errors of linear motion bearing and guideway.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.273-278
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• 2009

The determination of the associated weights in the theory of ordered weighted averaging (OWA) operators is one of the important issue. Recently, Wang and Parkan [Information Sciences 175 (2005) 20-29] proposed a minimax disparity approach for obtaining OWA operator weights and the approach is based on the solution of a linear program (LP) model for a given degree of orness. Recently, Liu [International Journal of Approximate Reasoning, accepted] showed that the minimum variance OWA problem of Fuller and Majlender [Fuzzy Sets and Systems 136 (2003) 203-215] and the minimax disparity OWA problem of Wang and Parkan always produce the same weight vector using the dual theory of linear programming. In this paper, we give an improved proof of the minimax disparity problem of Wang and Parkan while Liu's method is rather complicated. Our method gives the exact optimum solution of OWA operator weights for all levels of orness, $0\leq\alpha\leq1$, whose values are piecewise linear and continuous functions of $\alpha$.

• Advances in materials Research
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• v.8 no.4
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• pp.337-359
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• 2019

Shape Memory Polymer Composites (SMPC) have gained popularity over the last few decades due to its flexible shape memory behaviour over wide range of strains and temperatures. In this paper, non-linear bending analysis has been carried out for SMPC beam under the application of uniformly distributed transverse load (UDL). Simplified C⁰ continuity Finite Element Method (FEM) based on Higher Order Shear Deformation Theory (HSDT) has been adopted for flexural analysis of SMPC. The numerical solutions are obtained by iterative Newton Raphson method. Material properties of SMPC with Shape Memory Polymer (SMP) as matrix and carbon fibre as reinforcements, have been calculated by theory of volume averaging. Effect of temperature on SMPC has been evaluated for numerous parameters for instance number of layers, aspect ratio, boundary conditions, volume fraction of carbon fiber and laminate stacking orientation. Moreover, deflection profile over unit length and behavior of stresses across thickness are also presented to elaborate the effect of glass transition temperature (T_g). Present study provides detailed explanation on effect of different parameters on the bending of SMPC beam for large strain over a broad span of temperature from 273-373K, which encompasses glass transition region of SMPC.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1029-1062
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• 2016

In this research, the nonlinear free vibration analysis of boron-nitride micro ribbon (BNMR) on the Pasternak elastic foundation under electrical, mechanical and thermal loadings using modified strain gradient theory (MSGT) is studied. Employing the von $K{\acute{a}}rm{\acute{a}}n$ nonlinear geometry theory, the nonlinear equations of motion for the graphene micro ribbon (GMR) using Euler-Bernoulli beam model with considering attached mass and size effects based on Hamilton's principle is obtained. These equations are converted into the nonlinear ordinary differential equations by elimination of the time variable using Kantorovich time-averaging method. To determine nonlinear frequency of GMR under various boundary conditions, and considering mass effect, differential quadrature element method (DQEM) is used. Based on modified strain MSGT, the results of the current model are compared with the obtained results by classical and modified couple stress theories (CT and MCST). Furthermore, the effect of various parameters such as material length scale parameter, attached mass, temperature change, piezoelectric coefficient, two parameters of elastic foundations on the natural frequencies of BNMR is investigated. The results show that for all boundary conditions, by increasing the mass intensity in a fixed position, the linear and nonlinear natural frequency of the GMR reduces. In addition, with increasing of material length scale parameter, the frequency ratio decreases. This results can be used to design and control nano/micro devices and nano electronics to avoid resonance phenomenon.

• Journal of IKEEE
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• v.12 no.1
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• pp.65-74
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• 2008

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it is notorious for memory usage and can't learn additional information from new data. In order to overcome this problem, we propose an incremental learning algorithm (iMPA). iMPA divides the entire pattern space into fixed number partitions, and generates representatives from each partition. Also, due to the fact that it can not learn additional information from new data, we present iMPA which can learn additional information from new data and not require access to the original data, used to train. Proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

• Transactions of the Korean Society of Mechanical Engineers
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• v.8 no.2
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• pp.133-140
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• 1984

A "two-fluid" equation model has been applied for predicting gas-solid suspension flows through a Venturi tube. In the "two-fluid"equation model, the bulk motion of the particles is considered as a continuum whose governing equation is obtained by averaging the conservation equations over a volume and expressing the equations in differential forms. Closure of the time-mean equations is achieved by modeling the turbulent correlations with an extended mixing-length theory. Proposed closure model is found to aptly simulate the dependency of the static pressure drop on the particle size, flow rate and the loading ratio.d the loading ratio.

• Journal of Power Electronics
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• v.5 no.3
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• pp.180-189
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• 2005

In this paper a DSP based control scheme for the interleaved boost converter is presented. The mathematical model for the interleaved boost converter operating in a continuous inductor current mode is developed. A state-space averaging technique is used for modeling the converter system. A fixed frequency sliding mode controller is designed to ensure current distribution between the two converter modules and to achieve the load voltage regulation simultaneously. Necessary and sufficient conditions, using variable structure theory, are derived for the sliding mode to exist. The range of sliding mode controller coefficients is also determined. The designed controller capability, load distribution among the individual boost cells and load voltage regulation against source and load disturbances, are demonstrated through PSIM simulation results. A real-time controller based on ADMC401 DSP is developed. Experimental results are provided to validate the proposed control scheme.

• Asia Marketing Journal
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• v.24 no.3
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• pp.96-108
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• 2022

This study investigates how affects elicited by visual images in print ads are integrated to form a liking for the ads. Assuming a sequential rather than simultaneous processing of still-cut images, we adopt the 'think-aloud' method to capture consumers' spontaneous responses to visual images. We hypothesize that not only would consumers show mixed affects toward a still-cut visual image but that they would also integrate their serial affects heuristically rather than simply averaging the affects as suggested by the compensatory hypothesis. By comparing the effects of two contradictory affect integration hypotheses (i.e., peak-affect and mood-maintenance) with compensatory integration, using a single regression model, we found that peak-negative along with mood maintenance integration of serial affects for a print ad works best in the formation of ad liking. The results also support our initial premise that people can have mixed valence even toward a still-cut ad.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.