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Development of a Methodology for Determination and Analysis of Thermal Displacements of Machine Tools Using Finite Elements Method and artificial neural network

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Journal of Mechanics Engineering and Automation 2014年第6期4卷 488-498页

作者： Romualdo Figueiredo de Sousa Fracisco Augusto Vieira da Silva Joao Bosco Aquino Silva Jose Carlos de Lima Junior Department of Mechanical Engineering Federal University of Paraiba Joao Pessoa 58051-900 Brazil

In the processes of manufacturing, MT （machine tools） plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are those which are thermally induced which are from internal and external heat sources acting on the machine. In this paper, a methodology for determining and analyzing the thermal deformation of machine tools using FEM （finite element method） and ANN （artificial neural network.） is presented. After modeling the machine using FEM is defined the location of the heat sources, it is possible to obtain the temperature gradient and the corresponding thermal deformation at predetermined periods. Results obtained with simulations using the software NX.7.5 showed that this methodology is an effective tool in determining the thermal deformation of the machine, correlating the temperature reading at strategic points with volumetric deformation at the tool tip. Therefore, the thermal analysis of the errors in the pair tool part can be established. After training and validation process, the network.will be able to make the prediction of thermal errors just stating the temperature values of specific points of each heat source, providing a way for compensation of thermally induced errors.

关键词： Thermal displacement machine tool finite element method artificial neural network.

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A Selective Attention Guided Initiative Semantic Cognition Algorithm for Service Robot

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International Journal of Automation and computing 2018年第5期15卷 559-569页

作者： Huan-Zhao Chen Guo-Hui Tian Guo-Liang Liu School of Control Science and Engineering University of Shandong

With the development of artificial intelligence and robotics, the study on service robot has made a significant progress in recent years. Service robot is required to perceive users and environment in unstructured domestic environment. Based on the perception,service robot should be capable of understanding the situation and discover service task. So robot can assist humans for home service or health care more accurately and with initiative. Human can focus on the salient things from the mass observation information. Humans are capable of utilizing semantic knowledge to make some plans based on their understanding of the environment. Through intelligent space platform, we are trying to apply this process to service robot. A selective attention guided initiatively semantic cognition algorithm in intelligent space is proposed in this paper. It is specifically designed to provide robots with the cognition needed for performing service tasks. At first, an attention selection model is built based on saliency computing and key area. The area which is highly relevant to service task could be located and referred as focus of attention（FOA）. Second, a recognition algorithm for FOA is proposed based on a neural network. Some common objects and user behavior are recognized in this step. At last, a unified semantic knowledge base and corresponding reasoning engine is proposed using recognition result. Related experiments in a real life scenario demonstrated that our approach is able to mimic the recognition process in humans, make robots understand the environment and discover service task based on its own cognition. In this way, service robots can act smarter and achieve better service efficiency in their daily work.

关键词： Service robot cognition computing selective attention semantic knowledge base artificial neural network.

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A Web-based Intelligent Fashion Design System

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Journal of Donghua University(English Edition) 2006年第1期23卷 36-41页

作者：李继云方远 College of Computer Science and Technology Donghua UniversityShanghai 200051

A web-based fashion design system-FashDesn was developed for non-professionals to make their own style-based design by choosing and combining fashion parts. We use genetic algorithm to find the optimal satisfied style, while the fitness function of GA is approached by artificial neural network. The users’ style intention can be reached through the learning process of the artificial neural network. The system architecture and some realization details of the system are given in the paper. An output contrast for elegant style women’s before and after user’s interaction is used to illustrate its usefulness in the end.

关键词： fashion design genetic algorithm artificial neural network.

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Hybrid artificial neural network. and analytical model for prediction of optical constants and bandgap energy of 3D nanonetwork.silicon structures

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Opto-Electronic Advances 2021年第10期4卷 11-23页

作者： Shreeniket Joshi Amirkianoosh Kiani Micro/Nano Manufacturing Facility Faculty of Engineering and Applied ScienceOntario Tech University2000 Simcoe St NOshawaOntario L1G 0C5Canada

The aim of this study is to develop a reliable method to determine optical constants for 3D-nanonetwork.Si thin films manufactured using a pulsed-laser ablation technique that can be applied to other materials synthesized by this *** analytical method was introduced to calculate optical constants from reflectance and transmittance *** band gaps for this novel material and other important insights on the physical properties were derived from the optical *** existing optimization methods described in the literature were found to be complex and prone to errors while determining optical constants of opaque materials where only reflectance data is available.A supervised Deep Learning Algorithm was developed to accurately predict optical constants from the reflectance spectrum *** hybrid method introduced in this study was proved to be effective with an accuracy of 95%.

关键词： 3D nanonetwork nanostructures optical properties artificial neural network.

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Hydrolysis of Lactose： Estimation of Kinetic Parameters Using artificial neural network.

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Journal of Agricultural Science and Technology(A) 2013年第10期3卷 811-818页

作者： Anahi V. Cuellas Sebastian Oddone Enrique J. Mammarella Amelia C. Rubiolo lngenierla en Alimentos Universidad Nacional de Quilmes Roque Saenz Peha 380 Bernal-Buenos Aires Argentina Universidad Argentina de la Empresa-UADE Lima 775 Buenos Aires Argentina Instituto de Desarrollo Tecnol6gico para la Industria Quimica-INTEC (Conicet-UNL) Giiemes 3450 Santa Fe Argentina

The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network.（ANN） to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial 13-galactosidase either free or immobilized in a chitosan support was used. The neural network.developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysis.

关键词： Cheese whey fl-galactosidase lactose hydrolysis artificial neural network.

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Research on BP-ANN Model of Semi-rigid Connection

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Journal of Civil Engineering and Architecture 2012年第3期6卷 385-389页

作者： Jian Liu Xiangyun Huang Guangen Zhou Jiping Hao Da Ren Yue Gao School of Civil Engineering Guangzhou University Guangzhou China Postdoctoral Research Station Zhejiang Southeast Lattice Frame Group Zhejiang China College of Civil Engineering Xi 'an University of Architecture and Technology Xi 'an China

The beam-to-column semirigid connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element. The beam-to-column semirigid connection behavior is represented by its moment-rotation relationship. Several traditional mathematical models have been proposed to fit the moment-rotation curves from the experimental database,but they may be more reliable within certain ranges. In this paper, the intellectualized analytical model is proposed in the semirigid connections for top and seat angles with double web angles using the feed-forward back-propagation artificial neural network.（BP-ANN） technique. the intellectualized analytical model from experimental results based on BP-ANN is more reliable and it is a better choice to the moment-rotation curves for beam-to-column semirigid connection. The results are found to provide effectiveness to the experimental response that is satisfactory for use in steel structural engineering design.

关键词： beam-to-column joint semirigid connection intellectualized analytical model artificial neural network.

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