作者：
S SudhaharC Ganesh BabuD SharmilaAssistant Professor
Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor
Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor
Jai Shriram Engineering College Department of Computer Science Engineering Erode Tamilnadu India
Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are ...
Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are whist focused on multi loop controllers, ignoring the feedback control attainment with advantages of centralized multivariable controllers. Due to their single loop nature, the plant interactions that are merely in use keen on relation in the controller tuning process cannot be suppressed by decentralized controllers. In many situations, therefore it would be beneficial to delimit the detrimental consequences in pairing among inputs and outputs of the closed loop system under certain context. The centralized model predictive controllers (MPC) and decentralized/multi-loop PI controllers are designed. In terms of integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Absolute Error (ITAE), the output of both controllers is then compared. The results of the simulation showed that the MIMO MPC is better than the other suggested control schemes. The projected central controllers minimize interactions superior than the multi loop controllers that have recently been published.
Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edg...
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The neural correlates of face individuation – the acquisition of memory representations for novel faces – have been studied only in coarse detail and disregarding individual differences between learners. In their se...
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In cellular network, the user's location is probably used as an authentication aspect similar to other elements like password, PIN range combined with the use of device that the person consisting of card or a mobi...
In cellular network, the user's location is probably used as an authentication aspect similar to other elements like password, PIN range combined with the use of device that the person consisting of card or a mobile. An encrypted and decrypted key is generated using RSA. The generated values are send to user's cellular as SMS. The user's mobile region is identified by the place of use far flung customer authentication protocol and its integrity is proven by Galileo Navigation satellite known as Local element. We proposed an LRAP based method for secure transactions.
The analysis of invasive team sports often concentrates on cooperative and competitive aspects of collective movement behavior. A main goal is the identification and explanation of strategies, and eventually the devel...
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Data science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from...
Data science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from the data to serve stakeholder's purposes. Python is commonly used and, along with being a versatile and open-source language, is a favorite tool in Data science studies. The vast libraries are being used to manipulate data and are very simple for even a beginner data scientist to understand. In the present work, we intend to apply the data science methodology to decision making and predictive analysis using the python programming language. We consider the problem of selecting the better mode of study concerning some of the impractical phenomena from physics for the exact understanding of the process. Data collection has been from an educational institute and the comparison has been made between theoretical learning and simulatory learning for selected topics from the vast fields like mechanics, thermodynamics, fluid dynamics, and radioactivity. The steps of data science methodology are germinated to achieve the insights into the data procured and the results are wangled concerning the teaching methodology that could be employed. In the present work, we undertake a comparative study between the theoretical and simulatory modes of teaching by exploring the modes individually through evaluating the responses imparted by a class of high school students. The analysis reported the more inclination of the student's responses towards the simulatory methods when compared to the theoretical method of learning.
As the data-intensive applications are increasing at an unprecedented rate, the need of efficient data computation in the today's world emerges as the most challenging task. So in this case, the computation resour...
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ISBN:
(数字)9781728112619
ISBN:
(纸本)9781728112626
As the data-intensive applications are increasing at an unprecedented rate, the need of efficient data computation in the today's world emerges as the most challenging task. So in this case, the computation resources may be available on demand because of the cloud computing architecture. In this paper, an analysis based on the previous study have been discussed and analyzed on the methodological prospects. It includes the attributes like computational parameters, data handling mechanism, data security and authentication. This study also explores the mechanism for the effective data computation in terms of the inter cloud environment. It includes the environment variability between public and private clouds.
Phononic metamaterials enabled the realization of many acoustic components analogous to their electronic counterparts, such as transistors, logic gates and calculators. A key component among these is the demultiplexer...
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作者：
S. JayapradaG. JayaLakshmiL. KanyaKumariAssociate Professor
Department of Computer Science & Engineering Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Assistant Professor
Department of Information Technology VR Siddhartha Engineering College Vijayawada Andhra Pradesh India Assistant Professor
Department of Information Technology Andhra Loyola Institute of Engineering and Technology Vijayawada Andhra Pradesh India
The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. computer-Aided tasks and its implementation of the cur...
The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. computer-Aided tasks and its implementation of the current models with their frameworks would suffice the design metrics to recognize and relate the different tumors including brain though the process with scanning of the brain would emphasize on MRI. The models with support vector machine-nearest neighbor, naïve bayes analysis are obtained aren't enough to produce the different scenario at each set of layers and its correlation values of the performance observed. We propose a design model with fast Adaboost binary classifier, ensemble approach with fast boosting algorithm with the pre-trained model's dataset to analyze the different problem which proposes a strategy for multiple application scenarios of feature extraction to provide a classification model for different tumors in the brain to improve different performance parametric values for each trained and test sets with the prediction algorithm with each design formulation.
Hybrid Techniques have been used widely to construct a predictive model for intelligent e learning system, a decision tree techniques is one that have been implemented a large area of Intelligent e learning systems. I...
Hybrid Techniques have been used widely to construct a predictive model for intelligent e learning system, a decision tree techniques is one that have been implemented a large area of Intelligent e learning systems. In this paper, we review the recent literature on various solutions to address different techniques to build intelligent e-learning system to serve the academic institutions and enhancing the students learning path. This paper we provide a general overview of methodologies and the machine learning techniques in area of education and learning purposes and gives main citations for the comprehensive understanding and further explorations of this area of research. To be specific, previous attempts to enable to construct intelligent model for e learning system are examined, (1) over view of design intelligent model for e learning system: Various techniques have been used to build predictive and intelligent model for e learning such decision tree techniques, Naive Bays, Neural Network and K-nearest neighbor.(2) the techniques that are used for building intelligent e learning system (3) the issues that have been faced through implementation of the proposed system (4) Problem and system analysis of the predictive model of intelligent e learning system.
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