Modeling texture evolution during rolling process of AZ31 magnesium alloy with elasto-plastic self consistent model

To gain a better understanding about texture evolution during rolling process of AZ31 alloy, polycrystalline plasticity model was implemented into the explicit FE package, ABAQUS/Explicit by writing a user subroutine VUMAT. For each individual grain in the polycrystalline aggregate, the rate dependent model was adopted to calculate the plastic shear strain increment in combination with the Voce hardening law to describe the hardening response, the lattice reorientation caused by slip and twinning were calculated separately due to their different mechanisms. The elasto-plastic self consistent （EPSC） model was employed to relate the response of individual grain to the response of the polycrystalline aggregate. Rolling processes of AZ31 sheet and as-cast AZ31 alloy were simulated respectively. The predicted texture distributions are in aualitative a^reement with experimental results.

A Brief Analysis of the Self-correction Model——from the View of Linking Theory

Based on the theory of the post method pedagogy and the teaching practice，this paper proposes a new teaching model，the self-correction model，and explains it from the point of view of linking theory.

Analysis of the Self-correction Model——from the view of cognitive psychology

In view of the learners Chinglish,this paper puts forward a new teaching model-the Self-correction Model and makes an analysis of it from the view of cognitive psychology.

Self-Learning and Its Application to Laminar Cooling Model of Hot Rolled Strip

The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.

Experimental Testing and Validation of the Mathematical Model for a Self-Humidifying PEM Fuel Cell

This paper presents an experimental testing and validation results for a zero-dimensional self-humidifying PEM (Proton Exchange Membrane) fuel cell stack. The model incorporates major electric and thermodynamic variables and parameters involved in the operation of the PEM fuel cell under different operational conditions. The mathematical equations are modelled by using Matlab-Simulink tools in order to simulate the operation of the developed model with a commercially available 1 kW Horizon (H-1000) PEM fuel cell stack, which is used for the purposes of model validation and tuning of the developed model. The model is mathematically modelled and presented in the recent published work of authors. The observations from model simulations provide sufficient evidence and support to the results and observations obtained from testing 1 kW Horizon (H-1000) PEM fuel cell stack used in this research. The developed model can be used as a generic model and simulation platform for a self-humidifying PEM fuel cell with an output power varying from 50 W to 1 kW, with extrapolation to higher powers is also possible.

The Effect of an Educational Program Based on the Health Belief Model on Self-Efficacy among Patients with Type 2 Diabetes Referred to the Iranian Diabetes Association in 2014

Background: Patient self-efficacy is one of the most important factors in treating and overcoming disease. Objective: The aim of this study was to evaluate the effect of an educational program based on the health belief model on self-efficacy among patients with type 2 diabetes referred to the Iranian Diabetes Association in 2014. Method: A randomized controlled clinical trial was conducted. Eighty patients with type 2 diabetes were selected randomly by the double block sample method. They were then divided into two groups of intervention and control (40 patients in each group) by random allocation. Data were collected by a questionnaire based on the Health Belief Model and self-efficacy. The data were gathered two months after the educational program was held. The educational program was designed on the basis of data collected in the pre-test phase. Then, the educational program was executed for the intervention group in 8 sessions (each 30 minutes) using lectures and an educational booklet. Data analysis was done with Chi-square Test, Pearson’s correlation, Independent samples T-test and paired T-test. The significance level was considered at 0.05. Results: Before intervention, no significant difference was detected between the two groups. However, after intervention all variables were significantly different except for perceived threat. Moreover, there were significant linear relationships between Self-efficacy and all Health Belief Model components after the educational intervention in both groups (p < 0.05). Conclusion: The educational program based on the health belief model increased self-efficacy in type 2 diabetes mellitus patients.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

Modelling self-sensing of a magnetostrictive actuator based on a terfenol-D rod

A simplified quasi-static computational model for self-sensing applications of magnetostrictive actuators based on terfenol-D rods is presented. Paths and angle changes in the magnetic moments rotation of Tb0.3Dy0.7Fe2 alloy are studied as functions of compressive stress and magnetic field, and then used to determine the magnetization in its actuation. Then sensing of magnetic induction picked from a driving coil in an actuator is derived. The model is quick and efficient to solve moments rotation and its magnetization. Sensing results of compressive stress and magnetostriction calculated by the model are in good agreement with experiments and will be helpful in the design and control of self-sensing applications in actuators.

Self-Consistent Model for Pulsed Direct-Current N_2 Glow Discharge

A self-consistent analysis of a pulsed direct-current (DC) N2 glow discharge is presented. The model is based on a numerical solution of the continuity equations for electron and ions coupled with Poisson's equation. The spatial-temporal variations of ionic and electronic densities and electric field are obtained. The electric field structure exhibits all the characteristic regions of a typical glow discharge (the cathode fall, the negative glow, and the positive column). Current-voltage characteristics of the discharge can be obtained from the model. The calculated current-voltage results using a constant secondary electron emission coefficient for the gas pressure 133.32 Pa are in reasonable agreement with experiment.

SELF-ORGANIZING ASSEMBLY MODELING BASED ON RELATIONAL CONSTRAINTS

On the research of assembly modeling of mechanical products,current CAD systems can only support the design Process of component-to-assembly. It is difficult to realize the design process of assembly-to -component.The theory of self-organizing assembly modeling based on relational constraints is proposed, which implements the product design of assembly-to-component commencing with conceptual design and supporting abstract design and step-nice refinement design.

Self-Constructing Neural Network Modeling and Control of an AGV

Tracking precision of pre-planned trajectories is essential for an auto-guided vehicle (AGV). The purpose of this paper is to design a self-constructing wavelet neural network (SCWNN) method for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs, kinematical models have been preferred in recent research documents. However, in this paper, to enhance the trajectory tracking performance through including the AGV’s inertial effects in the control system, a learned dynamical model is replaced to the kinematical kind. As the base of a control system, the mathematical models are not preferred due to modeling uncertainties and exogenous inputs. Therefore, adaptive dynamic and control models of AGV are proposed using a four-layer SCWNN system comprising of the input, wavelet, product, and output layers. By use of the SCWNN, a robust controller against uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories. Owing to the adaptive structure, the number of nodes in the layers is adjusted in online and thus the computational burden of the neural network methods is decreased. Using software simulations, the tracking performance of the proposed control system is assessed.

On Self-growing Modeling for Curves

This paper presents a novel curve modeling method based on controlling rules of the shaping technique. The method describes the curve based on steplength and turning angle, and the characteristics of the curve near a point. Then it introduces the process to extract ＂growing-rules＂for 2D and 3D curves described by familiar analytical expressions and curvature-torsion expressions. Examples of self-growing modeling for familiar analytical curves are presented. New curves are obtained by designing the grow-rules; corresponding examples are also presented.

Two-dimensional modeling of the self-limiting oxidation in silicon and tungsten nanowires

Self-limiting oxidation of nanowires has been previously described as a reaction- or diffusion-controlled process. In this letter, the concept of finite reactive region is introduced into a diffusion-controlled model, based upon which a two-dimensional cylindrical kinetics model is developed for the oxidation of silicon nanowires and is extended for tungsten. In the model, diffusivity is affected by the expansive oxidation reaction induced stress. The dependency of the oxidation upon curvature and temperature is modeled. Good agreement between the model predictions and available experimental data is obtained. The de- veloped model serves to quantify the oxidation in two-dimensional nanostructures and is expected to facilitate their fabrication via thermal oxidation techniques.

Hidden Markov Models and Self-Organizing Maps Applied to Stroke Incidence

Several studies were devoted to investigate the effects of meteorological factors on the occurrence of stroke. Regression models had been mostly used to assess the correlation between weather and stroke incidence. However, these methods could not describe the process proceeding in the back-ground of stroke incidence. The purpose of this study was to provide a new approach based on Hidden Markov Models (HMMs) and self-organizing maps (SOM), interpreting the background from the viewpoint of weather variability. Based on meteorological data, SOM was performed to classify weather patterns. Using these classes by SOM as randomly changing “states”, our Hidden Markov Models were constructed with “observation data” that were extracted from the daily data of emergency transport at Nagoya City in Japan. We showed that SOM was an effective method to get weather patterns that would serve as “states” of Hidden Markov Models. Our Hidden Markov Models provided effective models to clarify background process for stroke incidence. The effectiveness of these Hidden Markov Models was estimated by stochastic test for root mean square errors (RMSE). “HMMs with states by SOM” would serve as a description of the background process of stroke incidence and were useful to show the influence of weather on stroke onset. This finding will contribute to an improvement of our understanding for links between weather variability and stroke incidence.

Mathematical model for cooling process and its self-learning applied in hot rolling mill

Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

基于selffer net构建的人工智能模型在风机叶根螺栓失效预测场景下的应用

针对风机叶根螺栓失效问题,文章提出了一种基于selffer net构建的叠加树人工智能模型。该模型集成了回溯训练层,叠加树模型,遮蔽特征模型,以及LightGBM模型,实现了对风机叶根螺栓失效概率的预测。经过实验验证,该模型在预测风机叶根螺栓失效概率方面具有较高的准确性和稳定性,为风机叶根螺栓失效预测场景提供了有效的解决方案。

Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

基于Self-CGRU模型的地铁基坑周边地表沉降预测

为提升地铁基坑开挖引发的地表沉降的预测精度,基于自注意力机制和深度学习提出一种能捕捉沉降数据时空特性的深度注意力组合预测模型(self-attention convolutional gated recurrent units,Self-CGRU)。Self-CGRU模型由空间模块和时间模块搭建。空间模块中,选择卷积神经网络捕捉相邻监测点沉降数据的空间相关性;时间模块中,使用门控循环单元神经网络分析沉降数据的时间规律,并引入自注意力机制捕获沉降数据内部的自相关性,进而得到沉降预测值。选取中国深圳市地铁基坑周边地表沉降数据验证Self-CGRU模型,结果表明:相比现有模型,Self-CGRU模型预测性能更好,使预测精度提高了17.48%~29.17%。研究成果可为地铁基坑周边地表沉降预测提供一种准确且稳定的新模型。

新几何模型与SELF-SReM模型的关系

将新几何模型和SELF-SReM模型的优点相结合，提出了通过引入多元系中的某些特征点信息．提高模型对多元系过剩热力学性质预报精度的新方法．并用之于对Mn-Fe-C和Fe-Si-C两个三元合金熔体的过剩Gibbs自由能的计算。