Study of Artificial Neural Network
TL;DR: Although the working principles and simple set of rules of artificial neuron looks like nothing special the full potential and calculation power of these models come to life when the authors start to interconnect them into artificial neural networks.
Abstract: Sonu, Ravi Parkash Bhokal* Dept. of Mathematics, M.D.U. Rohtak Introduction:An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simple mathematical model (function). Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted what means that every input value is multiplied with individual weight. In the middle section of artificial neuron is sum function that sums all weighted inputs and bias. At the exit of artificial neuron the sum of previously weighted inputs and bias is passing trough activation function that is also called transfer function. Although the working principles and simple set of rules of artificial neuron looks like nothing special the full potential and calculation power of these models come to life when we start to interconnect them into artificial neural networks (Figure 1) These artificial neural networks use simple fact that complexity can grown out of merely few basic and simple rules.
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TL;DR: In this article, an experimental-based study of abrasive jet machining (AJM) considering the effect of changing process parameters was presented. But, the experimental results revealed that MRR was highly dependent on the kinetic energy of the abrasive particles, with applied pressure the dominant parameter.
Abstract: The paper presents an experimental-based study of abrasive jet machining (AJM) considering the effect of changing process parameters. A series of drilling tests were carried out on glass workpieces using sand as the abrasive powder. The influence of each process parameter; applied air pressure, standoff distance, nozzle diameter, particle grain size and impact angle on the machining performance was determined in terms of the resultant material removal rate (MRR). The experimental results revealed that MRR was highly dependent on the kinetic energy of the abrasive particles, with the applied pressure the dominant parameter. The experimental results were compared with an erosion rate model previously published by Jafar et al. Though correct trends were predicted, there was a large discrepancy between model and measured values. An artificial neural network (ANN) was utilised to model the MRR more precisely, particularly to establish relationships between applied machining parameters and experimentall...
14 citations
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TL;DR: This review was conducted to summarize the most used chemometric methods in authentication and classification of plants based on spectral data, also presenting recent applications of these techniques.
Abstract: Automatic classification and authentication are increasing in importance in plant science, since there is a need in the agrofood industry to protect the product credibility, as well as to improve the online monitoring procedures. In this context, the use of spectral analysis to classify and authenticate agricultural products in a rapid and nondestructive way has been benefited by the evolution of electronics and computational methods, in particular the chemometrics. The spectral signatures of plants tend to vary with species, varieties, age, internal cellular structure, environmental conditions, chemical composition, and nutritional level, among other properties. When plant spectra present noise or do not visibly differ among themselves it is possible to apply preprocessing techniques, removing irrelevant information and improving the computing efficiency of the mathematical models. There are many chemometric methods which can be successfully used to classify or authenticate plants, each one with its own algorithm for determining how to discriminate at best the different groups. Thus, this review was conducted to summarize the most used chemometric methods in authentication and classification of plants based on spectral data, also presenting recent applications of these techniques.
3 citations
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TL;DR: Current applications of artificial neural networks and its great potential to help users in a personal learning environment to identify relevant and interesting items from a large number of items by suggesting actions to learners are discussed.
Abstract: With the rapid increase in the development of online learning technology and the huge amount of learning materials generated on the web. Besides, the learning resources are growing infinitely making it difficult for users to choose appropriate resources for their learning. This paper discusses current applications of artificial neural networks and its great potential to help users in a personal learning environment to identify relevant and interesting items from a large number of items by suggesting actions to learners.
1 citations
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TL;DR: In this article , the optimal placement of one type of WECS, namely the doubly-fed induction generator (DFIG) with a series and a shunt-FACTS control device called unified power flow controller (UPFC), is discussed.
Abstract: Received Nov 17, 2021 Revised May 25, 2022 Accepted Jun 16, 2022 The stability and security improvements of the grid-connected to the wind energy conversion system (WECS) can be made by optimizing the placement of a flexible alternating current transmission system (FACTS). This study discusses the optimal placement of one type of WECS, namely the doubly-fed induction generator (DFIG) with a series and a shunt-FACTS control device called unified power flow controller (UPFC). The DFIG and UPFC connected grid dynamic performance improvement with a maximum load bus system scenario. The optimal placement of DFIG and UPFC on the grid is formulated as a multi-objective problem, namely maximizing load bus system (Max. LBS) while minimizing active power loss (Min. Ploss) by pleasing numerous security and stability constraints. The non-dominated sorting genetic algorithm II (NSGA-II) have been utilized to settle this opposed bi-objective enhancement problem. The validity of the suggested method was examined on a modified IEEE 14-bus and a utilitarian examine system connected to DFIG with UPFC in power system analysis toolbox (PSAT) software. The optimal placement of DFIG and UPFC on the grid has increased the system's dynamic performance, with all the specified particular constraints being encountered.
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