Simultaneous Flow and Thermal Conductivity Sensing on a Single Chip Using Artificial Neural Networks

Whose Balance Sheet Is This? Neural Networks for Bankss Pattern Recognition

This study describes the implementation of Artificial Neural Network Pattern Recognition using Back-Propagation algorithm which can perform cardiovascular health pre-diagnosis of a patient through a generated blood pressure profile. The proponents gathered data from institutions that conduct Exercise Stress Testing, specifically the Treadmill Stress Test. The data gathered were age, gender, height, weight, blood pressure and heart rate readings and is considered as blood pressure condition risk factor. They compose the 47 input parameters of the network and was then divided into two — the training data and the testing data. This was put into a database created using Microsoft Excel. The back-propagation neural network model gives an accurate pre-diagnosis. The trained system gives an acceptable pre-diagnosis in reference to the given diagnosis by the attending practitioner that facilitated the collection of data.

The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.

Error diagnosis and detection have become important in modern production due to the importance of spinning equipment. Artificial neural network pattern recognition methods are widely utilized in rotating equipment fault detection. These methods often need a large quantity of sample data to train the model; however, sample data (especially fault samples) are uncommon in engineering. Preliminary work focuses on dimensionality reduction for big data sets using semisupervised methods. The rotary machine’s polar coordinate signal is used to build a GAN network structure. ANN and tiny samples are utilized to identify DCGAN model flaws. The time-conditional generative adversarial network is proposed for one-dimensional vibration signal defect identification under data imbalance. Finally, auxiliary samples are gathered under similar conditions, and CCNs learn about target sample characteristics. Convolutional neural networks handle the problem of defect identification with small samples in different ways. In high-dimensional data sets with nonlinearities, low fault type recognition rates and fewer marked fault samples may be addressed using kernel semisupervised local Fisher discriminant analysis. The SELF method is used to build the optimum projection transformation matrix from the data set. The KNN classifier then learns low-dimensional features and detects an error kind. Because DCGAN training is unstable and the results are incorrect, an improved deep convolutional generative adversarial network (IDCGAN) is proposed. The tests indicate that the IDCGAN generates more real samples and solves the problem of defect identification in small samples. Time-conditional generation adversarial network data improvement lowers fault diagnosis effort and deep learning model complexity. The TCGAN and CNN are combined to provide superior fault detection under data imbalance. Modeling and experiments demonstrate TCGAN’s use and superiority.

Online detection technology research on agricultural product quality deterioration

Arcing fault identification using combined Gabor Transform-neural network for transmission lines

ABSTRACTThis review addresses fundamentals and progress in field of thermally conducting polymer/nanocarbon nanocomposite. Upsurge in thermal conductivity of materials may lead to rapid heat diffusion, which in turn may prevent degradation. Thermally conductive nanofillers (carbon nanotube, graphene, nanodiamond, inorganics) have been effectively employed to form desired nanocomposite. In polymer/nanocarbon nanocomposites, thermal conductivity depends on nanofiller type, dispersion, loading level, polymer nature, morphology, and crystallinity. Thermal conductivity parameter has been significantly considered in aerospace, automotive, electronics, and energy-related industries, where thermal dissipation has become a challenging problem. In future, it is desired to design high performance nanocomposites with manageable thermal conduction.

Thermal conductivity is an important parameter for many applications, including having an essential role in the thermal history reconstruction of a petroleum system. This paper will present an accurate methodology of rock’s thermal conductivity measurement by time-varying heat flow. The thermal conductivity measurements were done to various lithologies of rock samples from Java and Sumatra, including mud from Purwodadi Bledug Kuwu mudflow. The collected sample cover many lithologies, such as Limestone, Volcanic, Shale, Coal, and Sand.Particular measurement of time-varying heat flow has been designed and applied to various samples. The equipment consists temperature sensors, heat source, heat measurement and data recorder. The measurement results show that the thermal conductivity of shale rocks ranges from 0.414-2.749 J / s.m.°C, sandstone 0.931-2.748 J / s.m.°C, coal 0.353 J / s.m.°C whereas limestone carbonate is 0.92-6.86 J / s.m.°C. The thermal conductivity of each type of rock is unique, even for the same rock’s lithology may have different values. The thermal conductivity parameter is influenced by the particle size distribution, pore parameter including pore shape or pore structure, fracture parameter of rock, and even the fluid or water content in the rock pores.

Numerical investigation for a hyperbolic annular fin with temperature dependent thermal conductivity

In this paper, Wavelet Collocation Method has been used to solve nonlinear n problem with temperature dependent thermal conductivity and heat transfer coecient. Thermal conductivity of n materials varies any type so that we consider thermal conductivity as the general function of temperature. Here we consider three particular cases, where we assume that thermal conductivity is constant, linear and exponential function of temperature. In each case eciency of n is evaluated. The whole analysis is presented in dimensionless form and the eect of variability of n parameter, exponent and thermal conductivity parameter on temperature distribution and n eciency is shown graphically and discussed in detail.

The effects of Kuvshinshiki fluid on Magnetohydrodynamic (MHD) heat and mass transfer flow over a vertical porous plate with chemical reaction of nth order and thermal conductivity was carried out. The governing partial differential equations were solved numerically using implicit Crank-Nicolson method. A parametric study was performed to illustrate the impact of visco-elastic parameter, radiation parameter, thermal conductivity parameter, magnetic parameter, Prandtl number on the velocity,temperature and concentration profiles.The results were presented graphically with tabular presentations of the skin-friction,rate of heat and mass transfer which were all computed and discussed for different values of parameters of the problem. The numerical results revealed that the visco- elastic of Kuvshinshiki fluid type is growing as concentration profile increases, while the velocity and temperature profile falls ,then the radiation and thermal conductivity were growing as velocity and temperature increases. Also Sherwood number decreases as radiation increases but Sherwood number remains unchanged as thermal conductivity growing.

In this paper, heat transfer in a straight fin with a step change in thickness and variable thermal conductivity which is losing heat by radiation to its surroundings is analyzed. The calculations are carried out by using the differential transformation method (DTM) and variational iteration method (VIM) that can be applied to various types of differential equations. The results obtained employing the DTM and VIM are compared with a finite difference technique with Richardson extrapolation which is an accurate numerical solution to verify the accuracy of the proposed methods. As an important result, it is depicted that the DTM results are more accurate in comparison with those obtained by VIM. After these verifications the effects of parameters such as thickness parameter α, dimensionless fin semi‐thickness δ, length ratio λ, thermal conductivity parameter β, and radiation–conduction parameter Nr, on the temperature distribution and fin efficiency are illustrated and explained. © 2012 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library (wileyonlinelibrary.com/journal/htj). DOI 10.1002/htj.21000

A pattern recognition artificial neural network method for random fatigue loading life prediction

This article addresses an investigation of the entropy analysis of Williamson nanofluid flow in the presence of gyrotactic microorganisms by considering variable viscosity and thermal conductivity over a convectively heated bidirectionally stretchable surface. Heat and mass transfer phenomena have been incorporated by taking into account the thermal radiation, heat source or sink, viscous dissipation, Brownian motion, and thermophoretic effects. The representing equations are nonlinear coupled partial differential equations and these equations are shaped into a set of ordinary differential equations via a suitable similarity transformation. The arising set of ordinary differential equations was then worked out by adopting a well‐known scheme, namely the shooting method along with the Runge‐Kutta‐Felberge integration technique. The effects of flow and heat transfer controlling parameters on the solution variables are depicted and analyzed through the graphical presentation. The survey finds that magnifying viscosity parameter, Weissenberg number representing the non‐Newtonian Williamson parameter cause to retard the velocity field in both the directions and thermal conductivity parameter causes to reduce fluid temperature. The study also recognizes that enhancing magnetic parameters and thermal conductivity parameters slow down the heat transfer rate. The entropy production of the system is estimated through the Bejan number. It is noticeable that the Bejan number is eminently dependent on the heat generation parameter, thermal radiation parameter, viscosity parameter, thermal conductivity parameter, and Biot number. The skillful accomplishment of the present heat and mass transfer system is achieved through the exteriorized choice of the pertinent parameters.

Numerical sensitivity study of thermal response tests