The Topological Derivative Method and Artificial Neural Networks for Numerical Solution of Shape Inverse Problems

The new method is proposed for the numerical solution of a class of shape inverse problems. The size and the location of a small opening in the domain of integration of an elliptic equation is identified on the basis of a observation. The observation includes the finite number of shape functionals. The approximation of the shape functionals by using the so-called topological derivatives is used to perform the learning process of an artificial neural network. The results of computations for 2D examples show that the method allows to determine an approximation of the global solution to the inverse problem, sufficiently closed to the exact solution. The proposed method can be extended to the problems with an opening of general shape and to the identification problems of small inclusions

Towards Real-Time Control of a Semibatch Crystallization Process by Electrical and Ultrasound Tomographic Techniques

This research work presents a feasibility study to demonstrate the application of Electrical Resistance Tomography and transmission-based Ultrasound Computed Tomography for monitoring and control of micron-sized calcium carbonate crystallization process. Herein, precipitated calcium carbonate production is bind to a carbon dioxide absorption process based on hollow-fiber membrane contactor.ERT acquisition system is equipped with 16 electrodes with operating frequency of 156 KHz and image capturing frame rate of 2 Hz. The ultrasound tomography equipment consists of 32 piezoelectric transducers at a frequency of 200 KHz. These sensors are sensitive to changes in suspension density and conductivity. Furthermore, a process control framework is developed by utilizing the fundamental relations of settling velocity of particles. Through simulations in the LabVIEW software, the PI-based feedback controller demonstrates a possibility of setpoint tracking by manipulating the control variable (mixing speed). Upon further investigations, this approach can be used as a multi-dimensional process analytical technology tool for quality assurance and malfunction diagnosis when out-of-specification events occur throughout the entire process

Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application "ERT-Vis" is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution-solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately. <br /

A review of literature on the use of clickers in the business and management discipline

YesClassroom response systems (clickers), in their various forms, are widely used across disciplines, demonstrating effectiveness across a range of different educational settings. However, only a few literature reviews on this technology have been undertaken in general, and no review has yet been performed on this topic in the business and management context. Realising the existing research gap, this article reviews 33 clicker-related studies from the business and management discipline that are largely focused on student perceptions and outcomes. The purpose of this paper is to provide a critical and balanced review of articles from the business and management discipline on various themes such as learner's engagement, performance, learning, participation, satisfaction, feedback, attendance, enjoyability, motivation, and interactivity, to name a few. The review also provides a brief account of lessons learned from the literature published in other disciplines and recommendations provided by studies from the business and management discipline

The topological derivative method and artificial neural networks for numerical solution of shape inverse problems

Theme 4 - Simulation et optimisation de systemes complexes - Projet NumathSIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1999 n.3739 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc