Using MLPs for fault analysis in analog circuits

Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors

An exhaustive literature survey shows, that a very little effort has been done towards Artificial Neural Network (ANN) approach in the area of concrete technology [1, 2, 3]. In the present investigation, development of ANN approach for prognostication of concrete mix proportion in lieu of conventional laboratory approach. The traditional lab approach attracts some drawbacks such as lot of manual involvement, time consuming, chances of creeping of human errors, uncertain prediction and always invasive in nature. Hence to reduce above said drawbacks, this study is undertaken to develop a ANN between concrete mix ingredient properties namely maximum size of aggregate, degree of quality control, degree of workability, type of exposure, characteristic compressive strength required in the field at 28 days and concrete mix proportion. Prognostication of concrete mix proportion is essential for all structural works. The present work deals with collection of huge input data base from literatures, ANN’s training and its testing are adopted to fix the appropriate weighted matrix (Illustrated in Fig [1]) which in turn Prognosticates the appropriate concrete mix proportion. Indian standard code (IS 10262:1982) procedure is also adopted to compare the concrete mix proportions of same samples. The Prognosticated concrete mix proportion from ANN approach yielded very high accuracy results (As shown in fig [2]) compared with IS code method. To account for larger sample data the results of this work will contribute for the prognostication of concrete mix proportions up to a certain degree of level, which will assist a structural engineer in estimation of concrete mix proportion, with minimum effort and non- invasive technique.

Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes

Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

Silk fibroin/xanthan composite was investigated as a suitable biomedical material for controlled drug delivery, and blending ratios of silk fibroin and xanthan were optimized by response surface methodology (RSM) and artificial neural network (ANN) approach. A non-linear ANN model was developed to predict the effect of blending ratios, percentage swelling and porosity of composite material on cumulative percentage release. The efficiency of RSM was assessed against ANN and it was found that ANN is better in optimizing and modeling studies for the fabrication of the composite material. In-vitro release studies of the loaded drug chloramphenicol showed that the optimum composite scaffold was able to minimize burst release of drug and was followed by controlled release for 5days. Mechanistic study of release revealed that the drug release process is diffusion controlled. Moreover, during tissue engineering application, investigation of release pattern of incorporated bioactive agent is beneficial to predict, control and monitor cellular response of growing tissues. This work also presented a novel insight into usage of various drug release model to predict material properties. Based on the goodness of fit of the model, Korsmeyer-Peppas was found to agree well with experimental drug release profile, which indicated that the fabricated material has swellable nature. The chloramphenicol (CHL) loaded scaffold showed better efficacy against gram positive and gram negative bacteria. CHL loaded SFX55 (50:50) scaffold shows promising biocomposite for drug delivery and tissue engineering applications.

The design goal of a control system is to influence the behavior of dynamic systems to achieve some pre-determinate objectives. A control system is usually designed on the premise that an accurate knowledge of a given object and environment cannot be obtained in advance. It usually requires suitable methods to address the problems related to uncertain and highly complicated dynamic system identification. As a matter of fact, system identification is an important branch of research in the automatic control domain. However, the majority of methods for system identification and parameters' adjustment are based on linear analysis: therefore it is difficult to extend them to complex non-linear systems. Normally, a large amount of approximations and simplifications have to be performed and, unavoidably, they have a negative impact on the desired accuracy. Fortunately the characteristics of the Artificial Neural Network (ANN) approach, namely non-linear transformation and support to highly parallel operation, provide effective techniques for system identification and control, especially for non-linear systems [1-9]. The ANN approach has a high potential for identification and control applications mainly because: (1) it can approximate the nonlinear input-output mapping of a dynamic system [10]; (2) it enables to model the complex systems’ behavior and to achieve an accurate control through training, without a priori information about the structures or parameters of systems. Due to these characteristics, there has been a growing interest, in recent years, in the application of neural networks to dynamic system identification and control.

For the purposes of long-term planning and budgeting, infrastructure user cost allocation, and financial need forecasts, infrastructure agencies seek knowledge of the annual expenditure levels for maintaining their assets. Often, this information is expressed in dollars per unit dimension of the infrastructure and is estimated using observed data from historical records. This paper presents an artificial neural network (ANN) approach for purposes of estimating annual expenditures on infrastructure maintenance and demonstrates the application of the approach using a case study involving rural interstate highway pavements. The results of this exploratory study demonstrate that not only is it feasible to use ANN to derive reliable predictions of annual maintenance expenditures (AMEX) at aggregate level, but also it is possible to identify the influential factors of such expenditures and to quantify the sensitivity of AMEX to such factors.

Enterobacter sp. SWLC2 for biodegradation of chlorpyrifos in the aqueous medium: Modeling of the process using artificial neural network approaches

In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence (Cn2) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed Cn2 data near the Mauna Loa Observatory, Hawaii are utilized for validation. The proposed approach has demonstrated its prowess by capturing the temporal evolution of Cn2 remarkably well. More interestingly, this ANN approach is found to outperform a widely used similarity theory-based conventional formulation for all the prevalent atmospheric conditions (including strongly stratified conditions).

This study deals with predicting performance parameters and exhaust emissions of a four-stroke, four-cylinder, direct injection diesel engine fuelled with soybean oil methyl ester and its blends with jet fuel, marine fuel and No. 2 diesel fuel using Artificial Neural Network (ANN) approach. For this aim, an ANN model for the engine was developed using experimental data, and the performance of the ANN predictions was measured by comparing them with the experimental results. It was revealed that the ANN approach can accurately predict the performance parameters and exhaust emissions of the diesel engine using various diesel and biodiesel fuels.

This paper attempts to compare the weather indices based regression approach and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) approach for rice yield prediction at district level of West Bengal. The weather indices for weather variables, viz., minimum temperature, maximum temperature, rainfall, and relative humidity are used as input variables along with time variable t and yield of rice as output variable. The study reveals that the ANN approach works better than the standard regression approach in crop yield prediction. The prediction error percentages are found to be consistently less than 5% in MLP ANN approach except for one district.

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

Impact damage effects on the residual mechanical properties of the structure can be quite detrimental, and failure patterns depend strongly on the impact energy levels. This paper presents impact energy level assessment method using 2D multiple signal classification (MUSIC) and artificial neural network (ANN) approach for composite structures. Because uniform linear array has the shortcoming of the half-plane mirror effect, 2D MUSIC algorithm using a plum blossom sensor array is firstly applied to locate the position of impact and extract impact energy feature. Secondly, the relation impact energy feature, impact position, and impact energy level at different measurement points are established using ANN approach. Through the trained ANN, the impact energy level can be predicted when the position and impact energy feature results of unknown impact are given as input. Finally, the proposed method is applied to a quasi-isotropic epoxy laminate plate and a large stiffened carbon fiber composite structure showing its successful performance on composite structure. Copyright © 2015 John Wiley & Sons, Ltd.

Piles are typically designed to ensure the bearing capacity and settlement elastic behavior. However, some projects seem over-designed, leading to unnecessary waste, whereas others experience excessive settlement. This could be caused by various factors, such as site investigation, sampling and testing methods, selection of soil behavior model, and calculation programs. To achieve a successful pile design, engineers must consider, among others, the loads applied to the pile, the resistance capacity of the piles, the pile material's bearing capacity, the pile's displacement, and the soil's settlement. On the other hand, the input parameters for geotechnical problems, in general, and pile design problems, in particular, often do not reflect the actual behavior of the soil due to its heterogeneous and anisotropic nature. To address these challenges, an Artificial Neural Network (ANN) approach is proposed for pile design, using a relatively wide range of soil input data. This study establishes a numerical program for pile design combined with the ANN approach, validated by verifying the pile design of a project constructed in Vietnam. The results indicate that the proposed program can reasonably simulate pile group behavior and assist engineers in deploying appropriate safety factors.

In recent years, a lot of research on Biomedicine is mainly related to heart defects. The recognition and detection of heart defects using an electrocardiogram (ECG) is numerous. This article aims to explain ECG research trends using the artificial neural networks (ANN) approach in the last twelve years. We reviewed journals with the keyword title "ANN ECG" and published from 2011 to 2022. Articles classified by the most frequently discussed topics include: data sets, case studies, pre-processing, feature extraction, and classification/identification methods. Data collection from articles obtained some of them use data banks such as PhysioNet, European Society of Cardiology (ESC), UCI Repository, as well as data collection directly from patients. The pre-processing stage of the total number of journals used 23% uses this stage, while in characteristic extraction almost 25% of papers are used. This research is very interesting because only a few researchers focus on researching about it. This article will provide further explanation of the most widely used algorithms for ECG research with the ANN approach. At the end of this article, critical aspects of ECG research can be carried out in the future, and the use of deep learning becomes a huge opportunity for researchers.