Flexural Characterization of Foam Cored E-Glass Reinforced Smart Materials Using Artificial Neural Network

The work aims at the comparison of experimental and artificial neural network (ANN) results of smart structures (SWC’s) comprising of three different densities of polyurethane foam (PUF) cored Vinyl ester (VE) SWC’s. As the expected life span of the SWC’s is not properly known yet, the long term durability assessment explores the decay of structural strength of SWC’s. The paper reports the degradation results of PUF cored E-glass mats, i.e., Chopped Strand Stitched Mat (CSM-S) and Stitch Bond Mat (SBM) reinforcement in VE matrix with change in density of PUF for moisture absorption at different temperatures with 95% RH for 180 days. The reduced core shear strength and face bending strength has shown in experimental samples. The verification of the experimental results done using ANN technique with the help of MAT Lab. It is observed that the neural network models are powerful methods for solving the material system issues by determining the complex and nonlinear relationship representing input/output information acquired in the analysis. The observed results infer that the experimental and ANN results are in good convergence.

A comparison analysis has been undertaken to study the effect of different wood fillers on density and mechanical properties of two different types of commercial polyurethane (PUR) foams: low-density flexible foams and high-density rigid foams. The micro-fillers used were based on wood flour particles of size 70-250 microns (wood waste of furniture industry). The relationships between the filler size and content, density and various mechanical properties - compression test, compression force deflection test and modulus of elasticity have been studied. It was found that micro-fillers provide various effects depending on flexibility and density of PUR foams. For low-density flexible PUR foams, the density and mechanical properties increased for both types of fillers. However, for high-density rigid PUR foams these effects were just the opposite: the density and compression strengths decreased for both types of fillers. Explanation of the observed effects should take into consideration the chemical nature and physical structure of flexible and rigid PUR binders based on various types of polyol-isocyanate systems, as well as chemical and physical affiliation of cellulose based fillers with the matrices.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short- and long-term forecasts of river flow events in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting. The studies in modelling the intermittent series are quite limited because of the complexity of fitting models in to these series. The available conventional models necessitate the adjustment of numerous parameters for calibration. Three different ANN techniques, namely, feed-forward back propagation (FFBP), generalized regression neural networks, and radial basis function-based neural networks (RBF) are applied to continuous and intermittent river flow data of two Turkish rivers for short-range and long-range forecasting studies. The k-fold partitioning method is employed for preparing the ANN training data successfully. In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria. It was observed that the FFBP method had some drawbacks such as a local minima problem and negative flow generation.

Artificial Neural Network (ANN) techniques have recently been applied to many different fields and have demonstrated their capabilities in solving complex problems. In a business environment, the techniques have been applied to predict bond ratings and stock price performance. In these applications, ANN techniques outperformed widely-used multivariate statistical techniques. The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance. The paper provides an overview of the basic concepts of ANN techniques in order to enhance the understanding of this emerging technique. The similarities and differences between ANN and DA techniques in representing their models are described. This study also proposes a method to overcome the limitations of the ANN approach, Finally, a case study using a data set in a business environment demonstrates the superiority of ANN over DA as a method of classification of observations.

This study predicts the moisture ratio history data of dehydrating yam slices from partial data using Artificial Neural Network (ANN) techniques. The moisture ratio history data at 65 oC, 75 oC, 85 oC, and 95 oC were recorded for the dehydration of 1.5 mm, 3.0 mm, and 4.5 mm thick yam slices in a Refractance Window Dryer. The Artificial Neural Network within MATLAB software (v. 8.5), using the Levenberg-Marquardt back-propagation algorithm, was trained with some of the data. After training, the Neural Network software predicted the moisture ratio of the primary variables not used in training. The predicted and experimental values were compared. The results showed that, the Artificial Neural Network (ANN) model using the Levenberg-Marquardt back-propagation training algorithm could accurately predict the experimental results not used in training., the predicted and observed data values fitted each other with correlation coefficient (R2) values of 0.97, 0.99 and 0.99, respectively, for the three-process condition considered. The high R2 establishes a strong correlation between the experimental and predicted values. This work is essential as it establishes that Artificial Neural Network (ANN) techniques, using the Levenberg-Marquardt back-propagation training algorithm, can predict food samples moisture ratios of in a drying process when data is incomplete.

This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

본 논문에서는 TBM 터널의 세그먼트 라이닝 설계 자동화 기술 개발의 일환으로 인공신경망기법을 이용한 세그먼트 라이닝 부재력 산정기법 개발에 관한 내용을 다루었다. 부재력 평가가 가능한 인공신경망을 개발하기 위해 먼저 다양한 설계조건을 도출하고 이에 대해 2-Ring Beam 모델을 이용한 유한요소해석을 수행하여 인공신경망 학습에 필요한 설계조건별 부재력에 관한 DB를 구축하였다. 구축된 DB를 활용하여 인공신경망의 최적화 과정을 통해 최대 부재력 및 분포도를 예측할 수 있는 인공신경망을 구축하였다. 검토 결과 구축된 인공신경망은 유한요소해석과 동일한 정밀도의 부재력 산정 기능을 확보하는 것으로 검토되었으며 따라서 TBM 세그먼트 라이닝 설계시 필요한 부재력 평가를 위한 효율적인 수단으로 활용될 수 있는 것으로 판단된다. This paper presents development of artificial neural network(ANN) based prediction method for section forces of TBM tunnel segment lining in an effort to develop an automatized design technique. A series of design cases were first developed and subsequently analyzed using the two-ring beam finite element model. The results were then used to form a database for use as training and validation data sets for ANN development. Using the database, optimized ANNs were developed that can readily be used to predict maximum sectional forces and their distributions. It is shown that the compute maximum section forces and their distributions by the developed ANNs are almost identical to the computed by the two-ring beam finite element model, implying that the developed ANNs can be used as design tools which expedite routine design calculation process. The results of this study indicate that the neural network model can be effectively used as a reliable and simple predictive tool for the prediction of segment sectional forces for design.

The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

In the last few years, due to excessive exploitation of groundwater resources, the water level has gone down in many parts of India. According to a data from the Central Ground Water Board (CGWB), the average groundwater level in India has decreased by 61 per cent between 2007 and 2017, due to which the availability and quality of water are taking serious problems day-by-day. According to the CGWB (2017), 313 blocks out of a total of 6881 blocks are Critical, 1186 blocks are over exploited, and 100 blocks fall into the category of saline. And there are 94 such blocks where the availability of groundwater is significantly less. The overall development and management of groundwater resources are critical because of the water crisis arising from the declining level of groundwater resources and its immense importance in the social and economic growth of the country. Accurate forecasting of groundwater levels is essential for efficient management of groundwater resources and sustainable development of ecosystems. In this study, Multi-Layer Perceptron (MLP) artificial neural network (ANN) was used to predict groundwater levels in a selected aquifer system. The development of the ANN model included data on rainfall, temperature and river water level and groundwater level. The ANN model was trained using the gradient decant algorithm (GDM). The said model was applied and validated at seven different observation locations, and the estimated capacity of the ANN model was developed for each location. This was assessed using four statistical indicators [Bias, RMSE, NSE and MSE] as well as visual testing. Based on the results of this study, the Neural Network Model (ANN) was found to be efficient for forecasting monthly groundwater levels at almost all selected observation locations. The study concluded that artificial neural network techniques (ANNs) could be used efficiently to predict groundwater level fluctuations, especially in data-scarce situations.

An improved noise model for pseudomorphic high electron mobility transistors (PHEMT) based on the combination of the artificial neural network (ANN) and conventional equivalent circuit modeling technique is presented. The frequency dispersion of the gate noise model parameter P, drain noise model parameter R, and the correlation coefficient C have been taken into account by using an ANN model. The influence of the gate leakage current can be accommodated by using the proposed noise model. The noise model parameters are determined directly from on wafer noise parameters measurement based on the noise correlation matrix technique. Good prediction for noise parameters and significant improvements of the accuracy of noise parameters are obtained up to 26 GHz for 2 × 40 μm gate width (number of gate fingers × unit gate width) 0.25 μm Double Heterojunction δ-doped PHEMTs over a wide range of bias points. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

view Abstract Citations (4) References (8) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Multidimensional Interpolation Using Artificial Neural Networks: Applications to an HI Cloud in Perseus Serra-Ricart, Miquel ; Trapero, Joaquin ; Beckman, John E. ; Garrido, Lluis ; Gaitan, Vicens Abstract In this paper we propose a method for interpolating multidimensional unbinned data, which could also be sparse, using artificial neural network techniques. An artificial example is first presented in order to show the reliability and potential of the neural network interpolator. A robust behavior is found. We apply the technique to the mapping of a cloud of interstellar atomic hydrogen. The cloud was mapped in H I at 21 cm and we find the neural network method ideal for interpolating the unevenly sampled data, yielding a map from which the global physical parameters of the cloud can be readily obtained. Publication: The Astronomical Journal Pub Date: January 1995 DOI: 10.1086/117274 Bibcode: 1995AJ....109..312S Keywords: Data Processing; Interpolation; Neural Nets; H I Regions; Interstellar Gas; Mapping; Spline Functions; Astronomy; ISM: CLOUDS full text sources ADS |

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order (ν) and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication.

In this paper, Artificial Neural Network (ANN) technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS) method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm) and mechanical torque (±7 N.m) in steady state and dynamic conditions. The simulation results illustrate the effectiveness of the proposed controller without speed or torque sensors.