• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.679-695
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• 2014

A design method of second generation wavelet (SGW)-based multivariable finite elements is proposed for static and vibration beam analysis. An important property of SGWs is that they can be custom designed by selecting appropriate lifting coefficients depending on the application. The SGW-based multivariable finite element equations of static and vibration analysis of beam problems with two and three kinds of variables are derived based on the generalized variational principles. Compared to classical finite element method (FEM), the second generation wavelet-based multivariable finite element method (SGW-MFEM) combines the advantages of high approximation performance of the SGW method and independent solution of field functions of the MFEM. A multiscale algorithm for SGW-MFEM is presented to solve structural engineering problems. Numerical examples demonstrate the proposed method is a flexible and accurate method in static and vibration beam analysis.

• Structural Engineering and Mechanics
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• v.38 no.6
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• pp.733-751
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• 2011

In the present study, a new kind of multivariable Reissner-Mindlin plate elements with two kinds of variables based on B-spline wavelet on the interval (BSWI) is constructed to solve the static and vibration problems of a square Reissner-Mindlin plate, a skew Reissner-Mindlin plate, and a Reissner-Mindlin plate on an elastic foundation. Based on generalized variational principle, finite element formulations are derived from generalized potential energy functional. The two-dimensional tensor product BSWI is employed to form the shape functions and construct multivariable BSWI elements. The multivariable wavelet finite element method proposed here can improve the solving accuracy apparently because generalized stress and strain are interpolated separately. In addition, compared with commonly used Daubechies wavelet finite element method, BSWI has explicit expression and a very good approximation property which guarantee the satisfying results. The efficiency of the proposed multivariable Reissner-Mindlin plate elements are verified through some numerical examples in the end.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.1
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• pp.33-44
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• 1989

A controller disign method is proposed for multivariable nonlinear stochastic systems with hard nonlinearities such as Coulomb friction, backlash and saturation. In order to take the nonlinearities into account statistical linearization techniques are used. And multi- variable pole placement techniques are applied to design controller for the statistically linearized multivariable systems. The basic concept of the controller design method is to solve two coupled equations, characteristic equation and Lyapunov equation, simultaneously and iteratively for statistically linearized multivariable stochastic systems. An aircraft with saturation serves as a design example. The design example illustrates the influence of nonlinear effects. The results of the analysis are compared to Monte Carlo simulation to test their accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.760-765
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• 1993

A multivariable learning control is designed in frequency domain. A general to of feedback assisted learning scheme is considered and an inverse model based learning algorithm is derived through convergence analysis in frequency domain. Performance of the proposed control method is evaluated through numerical simulation.

• Computers and Concrete
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• v.20 no.1
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Radiation Oncology Journal
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• v.37 no.1
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• pp.13-21
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• 2019

Purpose: Glioblastoma (GBM) carries a high propensity for in-field failure despite trimodality management. Past studies have failed to show outcome improvements with dose-escalation. Herein, we examined trends and outcomes associated with dose-escalation for GBM. Materials and Methods: The National Cancer Database was queried for GBM patients who underwent surgical resection and external-beam radiation with chemotherapy. Patients were excluded if doses were less than 59.4 Gy; dose-escalation referred to doses ≥66 Gy. Odds ratios identified predictors of dose-escalation. Univariable and multivariable Cox regressions determined potential predictors of overall survival (OS). Propensity-adjusted multivariable analysis better accounted for indication biases. Results: Of 33,991 patients, 1,223 patients received dose-escalation. Median dose in the escalation group was 70 Gy (range, 66 to 89.4 Gy). The use of dose-escalation decreased from 8% in 2004 to 2% in 2014. Predictors of escalated dose were African American race, lower comorbidity score, treatment at community centers, decreased income, and more remote treatment year. Median OS was 16.2 months and 15.8 months for the standard and dose-escalated cohorts, respectively (p = 0.35). On multivariable analysis, age >60 years, higher comorbidity score, treatment at community centers, decreased education, lower income, government insurance, Caucasian race, male gender, and more remote year of treatment predicted for worse OS. On propensity-adjusted multivariable analysis, age >60 years, distance from center >12 miles, decreased education, government insurance, and male gender predicted for worse outcome. Conclusion: Dose-escalated radiotherapy for GBM has decreased over time across the United States, in concordance with guidelines and the available evidence. Similarly, this large study did not discern survival improvements with dose-escalation.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.564-573
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• 2023

Objective: To investigate the feasibility of assessing the viscoelastic properties of the brain using magnetic resonance elastography (MRE) and a novel MRE transducer to determine the relationship between the viscoelastic properties and glymphatic function in neurologically normal individuals. Materials and Methods: This prospective study included 47 neurologically normal individuals aged 23-74 years (male-to-female ratio, 21:26). The MRE was acquired using a gravitational transducer based on a rotational eccentric mass as the driving system. The magnitude of the complex shear modulus |G^*| and the phase angle 𝛗 were measured in the centrum semiovale area. To evaluate glymphatic function, the Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) method was utilized and the ALPS index was calculated. Univariable and multivariable (variables with P < 0.2 from the univariable analysis) linear regression analyses were performed for |G^*| and 𝛗 and included sex, age, normalized white matter hyperintensity (WMH) volume, brain parenchymal volume, and ALPS index as covariates. Results: In the univariable analysis for |G^*|, age (P = 0.005), brain parenchymal volume (P = 0.152), normalized WMH volume (P = 0.011), and ALPS index (P = 0.005) were identified as candidates with P < 0.2. In the multivariable analysis, only the ALPS index was independently associated with |G^*|, showing a positive relationship (β = 0.300, P = 0.029). For 𝛗, normalized WMH volume (P = 0.128) and ALPS index (P = 0.015) were identified as candidates for multivariable analysis, and only the ALPS index was independently associated with 𝛗 (β = 0.057, P = 0.039). Conclusion: Brain MRE using a gravitational transducer is feasible in neurologically normal individuals over a wide age range. The significant correlation between the viscoelastic properties of the brain and glymphatic function suggests that a more organized or preserved microenvironment of the brain parenchyma is associated with a more unimpeded glymphatic fluid flow.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Communications of Mathematical Education
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• v.24 no.1
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• pp.259-282
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• 2010

In this paper, we introduce a modified Moore Method designed for the multivariable calculus course, and discuss about the effective teaching and learning method by observing the changes in the understanding of students' learning and the effects on students' learning in the class implemented by this modified Moore Method. This teaching experiment research was conducted with the 15 students who took the multivariable calculus course offered as a 3 week summer session in 2008 at H University. To guide the students' active preparation, stepwise course materials structured in the form of questions on the important mathematical notions were provided to the students in advance. We observed the process of the students' small-group collaborative learning activities and their presentations in the class, and analysed the students' class journals collected at the end of every lecture and the survey carried out at the end of the course. The analysis of these results show that the students have come to recognize that a deeper understanding of the subjects are possible through their active process of search and discovery, and the discussion among the peers and teaching each other allowed a variety of learning experiences and reflective thinking.