International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...Kashif Mehmood
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Novel approach for hybrid MAC scheme for balanced energy and transmission in ...IJECEIAES
Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Classification of Churn and non-Churn Customers in Telecommunication CompaniesCSCJournals
Telecommunication is very important as it serves various activities, services of electronic systems to transmit messages via physical cables, telephones, or cell phones. The two main factors that affect the growth of telecommunications are the rapid growth of modern technology and the market demand and its competition. These two factors in return, create new technologies and products, which open a series of options and offers to customers, in order to satisfy their needs and requirements. However, one crucial problem that commercial companies in general and telecommunication in particular, suffer from is a loss of valuable customers to competitors; this is called customer churn prediction. In this paper, the dynamic training technique is introduced. The dynamic training is used to improve the prediction of performance. This technique is based on two ANN network configurations to minimise the total error of the network to predict two different classes; names churn and non-customers.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
Classification of electroencephalography using cooperative learning based on...IJECEIAES
Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
Fast and accurate primary user detection with machine learning techniques for...nooriasukmaningtyas
Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with orthogonal frequency division multiplexing and energy detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Y4502158163
1. Jitendra S. Pachbhai et al Int. Journal of Engineering Research and Applicationswww.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.158-163
www.ijera.com 158|P a g e
Application of ANN to Optimize the Performance of CI Engine
Fuelled With Cotton Seed Oil- A Review
Jitendra S. Pachbhai*
, Prof. M. M. Deshmukh**
*
M.Tech. student, Thermal Engineering, Govt. College of Engineering, Amravati, (M.S.) India.
**
Mechanical engineering department, Govt. College of Engineering, Amravati, (M.S.) India.
ABSTRACT
This study is deals with the Artificial Neural Network (ANN) modeling to optimize the performance of CI
engine. ANN can learn from example, are fault tolerant in the sense that this is able to handle noisy and
incomplete data and able to deal with non-linear problem. This is particularly useful in system modeling such as
in implementing complex mapping and system identification. The major objective of this paper is to illustrate
how ANN technique might play an important role in modeling and optimization of the performance of CI engine
fuelled with cotton seed oil. And also to understand the effect of cotton seed oil properties on CI engine
performance.
Keywords- Artificial Neural Network (ANN), Cotton seed oil, Optimization, Performance.
I. INTRODUCTION
1.1 Artificial Neural Network (ANN)
Neural networks obviate the need to use
complex mathematically explicit formulas, computer
models, and impractical and costly physical models..
They can learn from examples, and are able to deal
with non-linear problems. Furthermore, they exhibit
robustness and fault tolerance. The tasks that ANNs
cannot handle effectively are those requiring high
accuracy and precision as in logic and arithmetic.
ANNs have been applied successfully in a
various fields of mathematics, engineering, medicine,
economics, meteorology, psychology, neurology, and
many others.
Neural networks obviate the need to use
complex mathematically explicit formulas, computer
models, and impractical and costly physical models.
Some of the characteristics that support the success
of ANNs and distinguish them from the conventional
computational techniques are:
The direct manner in which ANNs acquire
information and knowledge about a given
problem domain (learning interesting and
possibly non-linear relationships) through the
„training‟ phase.
Neural networks can work with numerical or
analogue data that would be difficult to deal with
by other means because of the form of the data
or because there are so many variables.
Neural network analysis can be conceived of as a
„black box‟ approach and the user does not
require sophisticated mathematical knowledge.
The compact form in which the acquired
information and knowledge is stored within the
trained network and the ease with which it can be
accessed and used.
Neural network solutions can be robust even in
the presence of „noise‟ in the input data.
The high degree of accuracy reported when ANN
are used to generalize over a set of previously
unseen data (not used in the „training‟ process)
from the problem domain [1].
MODEL OF AN ARTIFICIAL NEURON:
The human brain no doubt is a highly
complex structure viewed as a massive, highly
interconnected network of simple processing
elements called Neurons. Every component of the
model bears a direct analogy to the actual
components of a biological neuron and hence is
termed as Artificial Neuron. It is this model which
forms the basis of the Artificial Networks as shown
in fig. 1.1.
Fig. 1.1 Structure of Biological Neuron [1]
RESEARCH ARTICLE OPEN ACCESS
2. Jitendra S. Pachbhai et al Int. Journal of Engineering Research and Applicationswww.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.158-163
www.ijera.com 159|P a g e
From fig. 1.2, x1, x2, x3,..…. care the n inputs
to the artificial neuron. w1, w2, w3, … wn are the
weights attached to the input links.
A biological neuron receives all inputs
through the dendrites, sums them and produces an
output if the sum is greater than the threshold value.
The input signals are passed on to the cell body
through the synapse, which may accelerate or retard
an arriving signal. It is this acceleration or retardation
of the input signals modelled by the weights. An
effective synapse, which transmits a stronger signal,
will have a correspondingly larger weight while a
weak synapse will have smaller weights. Thus,
weights here are Multicative factors of the inputs to
account for the strength of the synapse. Hence, the
total input I received by the soma of the artificial
neuron is
I= w1 x1 + w2 x2 + …… + wn xn = wi xi ……………(1)
To generate the final output y, the sum is
passed on to the non-linear filter Φ called as
Activation function or Transfer function or Squash
function, which releases the output [7].
Y= Φ(I) ……..(2)
Fig 1.2 Simplified Structure of Artificial Neuron [1]
A learning algorithm is defined as a
procedure that consists of adjusting the weights and
biases of a network, to minimize an error function
between the network outputs, for a given set of
inputs, and the correct outputs. Basically, a biological
neuron receives inputs from other sources, combines
them in some way, performs generally a nonlinear
operation on the result, and then outputs the final
result. The network usually consists of an input layer,
some hidden layers, and an output layer. Each input
is multiplied by a connection weight. In the simplest
case, products and biases are simply summed, then
transformed through a transfer function to generate a
result, and finally obtained output. Networks with
biases can represent relationships between inputs and
outputs more easily than networks without biases. A
transfer function consisted generally of algebraic
equations is linear or nonlinear. An important subject
of a neural network is the training step. There are
essentially two types of ANN learning models
supervised learning and unsupervised learning [3].
Supervised training is the process of providing the
network with a series of sample of inputs and
comparing the outputs with the expected responses.
The training continues until the network is able to
provide the expected responses. The weights will be
adjusted according to learning algorithm till it
reaches the actual outputs. In the neural network if
for the training input vectors, the target output is not
known the training method adopted is called as
unsupervised training. The net may modify the
weight so that the most similar input vector is
assigned to the same output unit. The net is found to
form a exemplar or code book vector for each cluster
formed. Various training functions can be used to
train the networks reach from a particular input to a
specific target output. The error between the network
output and the actual output is minimized by
modifying the network weights and biases [2].
Feasibility of ANN for CI engine optimization
From above mentioned techniques ANN is
use to optimize the performance of CI engine for the
following reasons:
Learning capability non-linear relationships
between input and output parameters through the
„training‟ phase.
The ANN possess the capability to generalize,
thus, they can predict new outcomes from past
trends.
The ANN is robust systems and is fault tolerant.
They can, therefore, recall full patterns from
incomplete, partial or noisy patterns.
The ANN can process information in parallel, at
high speed, and in a distributed manner.
There has been considerable interest in
recent years in the use of neural networks for the
modelling and controlling of IC engine because of
their ability to represent non-linear systems and their
self-learning capabilities. Most of the researchers use
ANN technique for controlling and prediction of IC
engine parameter [1].
Advanced engine control systems require
accurate dynamic models of the combustion process,
which are substantially non-linear. Then apply the
fast neural network models for engine control design
purpose. These neuro-models are then integrated into
an upper-level emission optimization tool, which
calculates a cost function for exhaust versus
consumption/torque and determines optimal engine
settings.
Since the introduction of microprocessor
control, increase in number of sensors, actuators and
digital control functions were introduced, replacing
mechanical devices like ignition breaker and
injection. Due to the use of such electromechanical
configuration complex data is available. As the
calibration and parameter tuning has become a
3. Jitendra S. Pachbhai et al Int. Journal of Engineering Research and Applicationswww.ijera.com
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crucial part in the timely development because of
complex interactions and the many degrees of
freedom, it is shown how by specially designed
experiments for the identification of the engine a
considerable improvement can be reached with the
aid of local linear neural networks. The engine
models are then used for the optimization of static
and dynamic engine control, using a multi-objective
performance criterion for fuel consumption and
emissions.
Close control of combustion in these engines
will be essential to achieve ever-increasing efficiency
improvements while meeting increasingly stringent
emissions standards. The engines of the future will
require significantly more complex control than
existing map-based control strategies, having many
more degrees of freedom than those of today. Neural
network-based engine modelling offers the potential
for a multi-dimensional, adaptive, learning control
system that does not require knowledge of the
governing equations for engine performance or the
combustion kinetics of emissions formation that a
conventional map-based engine model requires.
Applications of such a neural network model include
emissions virtual sensing, on-board diagnostics and
engine control strategy optimization [1]. Hence, from
above discussion it is clear that an ANN technique is
widely used for CI engine than other AI techniques.
1.2 Application of ANN for CI engine fuelled with
cotton seed oil
Diesel engines are widely used for their low
fuel consumption and better efficiency. In wake of
the present energy environment crises it has become
essential to identify renewable and alternative clean
burning fuels. One of the significant routes to tackle
the problem of increasing prices and the pollution
problems of petroleum fuels is by the use of
vegetable oil fuels known as biodiesels [5].
In recent years, efforts have been made by
several researchers to use vegetable oils of
Sunflower, Peanut, Soyabean, Rapeseed, Olive,
Cottonseed, Jatropha, Pongomia, Rubber seed, Jajoba
etc. as alternative fuel for diesel engine. Among
those, cottonseed oil is used for study because
cottonseed oil is non-edible oil, thus food versus fuel
conflict will not arise if this is used for biodiesel
production [5].
Performance of CI engine greatly depends
upon the properties of fuel among which viscosity,
density, cetane number, volatility, lubricity, calorific
value etc are very important. Among those, viscosity
of cotton seed oil creates major problems in pumping,
atomization and fuel jet penetration.
Transesterification process is use to reduce the
viscosity. Also blending of cotton seed oil with diesel
may reduce the viscosity and improve the volatility
of oil, but its molecular structure remains unchanged.
Then making of blends of cotton seed methyl ester
and diesel in various concentrations [5].
An ANN model for the biodiesel engine is
proposed to be developed by using the steady state
experimental data. In this model, some of the
experimental data will be used for the training set,
some for the validation set while the remaining one
will be used for the test purpose. The inputs to the
ANN are cotton seed oil blend percentage (B), load
percentage (W), mass flow rate of fuel (mf) and mass
flow rate of air (ma) and the output parameters to the
ANN are Brake power (BP), Brake thermal
efficiency (BTE), Brake specific fuel consumption,
(BSFC), Exhaust gas Temperature (T
exh
). To ensure
that each input provides an equal contribution in the
ANN, we have to select proper hidden layer,
activation function and learning algorithm will be
selected. After development of ANN model the back
propagation method to train ANN network will be
applied. The output parameters can then be predicted
and compared with experimental output parameters
and minimize the error by giving epochs to ANN
whereas optimum output parameter can be determine
from those predicted output [6].
II. LITERATURE SURVEY
2.1 Soteris A. Kalogirou(2003): The major objective
of this paper is to illustrate how AI techniques might
play an important role in modelling and prediction of
the performance and control of combustion process.
The paper outlines an understanding of how AI
systems operate by way of presenting a number of
problems in the different disciplines of combustion
engineering. The various applications of AI are
presented in a thematic rather than a chronological or
any other order.
From the description of the various
applications presented in this paper, one can see that
AI techniques have been applied in a wide range of
fields for modeling, prediction and control in
combustion processes. What is required for setting up
such a system is data that represents the past history
and performance of the real system and a selection of
a suitable model. The selection of this model is done
empirically and after testing various alternative
solutions. The performance of the selected models is
tested with the data of the past history of the real
system.
Especially the ANN has wide application in
IC engine combustion, monitoring and controlling.
This system has ability to represent the non linear
system and self learning capability advanced engine
system required accurate dynamic models, which are
substantially non linear and does not required
knowledge of the governing equation of engine.
These neuro-models are then integrated into an upper
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level emission optimization tool, which calculates a
cost function for exhaust verses consumption/torque
and determining optimal engine settings[1].
2.2Shivakumar, Srinivas Pai P., Shrinivasa Rao B.
R.and Samaga B. S. (2010):In the present work
biodiesel was prepared from Honge oil (Pongomia)
and used as a fuel in C.I. engine. Experimental
investigation of the Performance parameters and
Exhaust emissions from the engine were done on a
single cylinder four-stroke water-cooled compression
ignition engine connected to an eddy current
dynamometer. Experiments were conducted for
different percentage of blends of Honge oil with
diesel at various compression ratios.
Artificial Neural Networks (ANNs) were
used to predict the Engine performance and emission
characteristics of the engine. An ANN model for the
biodiesel engine was developed by using the steady
state experimental data. In the model 70% of the
experimental data were used for the training set, 15%
for the validation set, remaining 15% were employed
for the test purpose. The inputs to the ANN are WCO
blend percentage (B), load percentage (W), and the
compression ratio (CR). The output parameters from
the ANN are Brake thermal efficiency (BTE), Brake
specific energy consumption, (BSEC), Exhaust gas
Temperature (T
exh
) and the emissions which include
Oxides of nitrogen (NOx), Smoke (SN), Unburnt
Hydrocarbon (UBHC), and Carbon Monoxide (CO).
The computer codes for training the network using
the back-propagation algorithm. Mean Relative error
and the regression analysis was carried out for the
trained as well as the test data are considered to be
within the acceptable limits. The emission of smoke
and CO shows slightly higher values. Hence ANN
approach can be used for the prediction of engine
performance and emission characteristics of I.C
engines by performing a limited number of tests
instead of detailed experimental study thus saving
both engineering effort and funds [2].
2.3 Gholamhassan Najafi, Barat Ghobadian, Talal
F. Yusaf and Hadi Rahimi(2007): The presented
combustion analysis has been conducted to evaluate
the performance of a commercial DI engine, water
cooled two cylinders, in-line, naturally aspirated
diesel engine using waste vegetable cooking oil as an
alternative fuel. In order to compare the brake power
and the torques values of the engine, it has been
tested under same operating conditions with diesel
fuel and waste cooking biodiesel fuel blends. The
results were found to be very comparable. The total
sulphur content of the produced biodiesel fuel was 18
ppm which is 28 times lesser than the existing diesel
fuel sulphur content used in the diesel vehicles
operating in Tehran city (500 ppm). By adding 20%
of waste vegetable oil methyl ester, the maximum
power and torque increased by 2.7 and 2.9%
respectively, also the concentration of the CO and
HC emissions have significantly decreased when
biodiesel was used.
An artificial neural network (ANN) was
developed based on the collected data of this work.
The backpropagation algorithm was utilized in
training of all ANN models. This algorithm uses the
supervised training technique where the network
weights and biases are initialized randomly at the
beginning of the training phase. The error
minimization process is achieved using gradient
descent rule. To ensure that each input variable
provides an equal contribution in the ANN, the inputs
of the model were pre-processed and scaled into a
common numeric range (-1,1). The activation
function for hidden layer was selected to be logic.
Linear function suited best for the output layer. This
arrangement of functions in function approximation
problems or modelling is common and yields to
better results. The results showed that the training
algorithm of Back Propagation was sufficient enough
in predicting the engine torque, specific fuel
consumption and exhaust gas components for
different engine speeds and different fuel blends
ratios. It was found that the R (the coefficient of
correlaion) values are 0.99994, 1, 1 and 0.99998 for
the engine torque, specific fuel consumption, CO and
HC emissions, respectively [3].
2.4Sakir Tasdemir, Ismail Saritas, Murat Ciniviz,
Novruz Allahverdi (2011): This study is deals with
artificial neural network (ANN) and fuzzy expert
system (FES) modelling of a gasoline engine to
predict engine power, torque, specific fuel
consumption and hydrocarbon emission. In this
study, experimental data, which were obtained from
experimental studies in a laboratory environment,
have been used.
An ANN model can accommodate multiple
input variables to predict multiple output variables.
The available data set is partitioned into two parts,
one corresponding to training and the other
corresponding to validation of the model. The
purpose of training is to determine the set of
connection weights and nodal thresholds that cause
the ANN to estimate outputs that are sufficiently
close to target values. The system adjusts the weights
of the internal connections to minimize errors
between the network output and target output.
In FES model the input and output values of
the system are crisp values. By fuzzification these
crisp input values, its fuzzy membership values and
degrees are obtained. In the inference mechanism
fuzzy results are inferred from the memberships of
fuzzy sets with the aid of knowledge base. In the
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inference sub process, the truth value for the premise
of each rule is computed, and applied to the
conclusion part of each rule. The knowledge base is
built on a collection of fuzzy IF-THEN rules.
The biggest advantages of the ANN and FES
compared to classical methods are speed of
calculations, simplicity, and capacity to learn from
examples and also do not require more experimental
study [4].
2.5Md. Nurun Nabi, Md. Mustafizur Rahman,
Md. Shamim Akhter (2009): Different parameters
for the optimization of biodiesel production were
investigated in the first phase of this study, while in
the next phase of the study performance test of a
diesel engine with neat diesel fuel and biodiesel
mixtures were carried out. Biodiesel was made by the
well known transesterification process. Cottonseed is
non-edible oil, thus food versus fuel conflict will not
arise if this is used for biodiesel production.
However, the optimum conditions for biodiesel
production are suggested in this paper.
A maximum of 77% biodiesel production was
observed at 20% methanol and at a temperature of
550
C. The crude CSO was used in the present
investigation; therefore, biodiesel yield was less than
90%. A maximum of 77% biodiesel yield was found
at 0.5 wt-% catalyst and at a reaction temperature of
550
C. Thus 20% methanol and 0.5% NaOH were
chosen as the optimum percentages for biodiesel
production. Biodiesel mixtures showed less CO, PM,
smoke emissions than those of neat diesel fuel. NOx
emission with biodiesel mixtures showed higher
values when compared with neat diesel fuel.
Compared to the neat diesel fuel, 10% biodiesel
mixtures reduced PM, smoke emissions by 24% and
14%, respectively. Biodiesel mixtures (30%) reduced
CO emissions by 24%, while 10% increase in the
NOx emission was experienced with the same
blend[5].
2.6 C.V. Subba Reddy, C. Eswara Reddy & K.
Hemachandra Reddy (2012): In the present work,
experiments are conducted on 3.72 kW(5 BHP)
single cylinder, four stroke, water-cooled diesel
engine using cotton seed oil methyl esters blended
with diesel in various proportions to study the engine
performance and emissions at different injection
pressures..
It could be concluded from the results that at the
injection pressure of 200 bar, the performance and
emission characteristics of 20BD are better as
compared to180 bar for diesel fuel operation. The
blends of biodiesel with diesel substantially reduced
unburnt hydrocarbons, carbon monoxide and smoke
opacity in exhaust gases, and slightly higher NOx due
to higher combustion temperature than diesel fuel.
Therefore, it is established that the blend 20BD is
better alternative fuels for D.I. diesel engines at
higher fuel injection pressure of 200 bar [6].
III. THE FUTURE SCOPE
Scope for future work as suggested in above
review is to design ANN model by selecting proper
hidden layer, activation function and learning
algorithm and to determine the set of connection
weight and nodal thresholds regarding given
problem. Also there is a scope to optimize the
performance and emission characteristics of CI
engine by using ANN, so that the controlling of
performance parameter of engine can be achieve in
well manner.
IV. CONCLUSION
ANN is collection of small individually
interconnected processing units. Information is
passed between these units with the help of
interconnection weights. ANN requires some past
data to train with respect to that data set until the
learning of network. Once ANN trained then it will
predict new pattern of data. From this it is clear that
ANN technique is very useful to predict and optimize
the performance of CI engine.
The use of cotton seed oil as biodiesel
reduces the CO, PM and smoke emission but there is
slight increase in NOX emission when compared to
neat diesel fuel. Thermal efficiency with cotton seed
oil was slightly lower than that of neat diesel fuel due
to lower heating value of cotton seed oil.
REFERENCES
[1] Soteris A. Kalogirou, “Artificial intelligence
for the modelling and control of combustion
processes: a review”, Progress in Energy
and Combustion Science, pp.515-566, 2003.
[2] Shivakumar, Srinivas Pai P., Shrinivasa Rao
B. R; “Artificial Neural Network based
prediction of performance and emission
characteristics of a variable compression
ratio CI engine using WCO as a biodiesel at
different injection timings”, Journal of
Applied Energy, pp. 2344–2354, 2011.
[3] Gholamhassan Najafi, Barat Ghobadian,
Talal F. Yusaf and Hadi Rahimi, ”
“Combustion Analysis of a CI Engine
Performance Using Waste Cooking
Biodiesel Fuel with an Artificial Neural
Network Aid”, American Journal of Applied
Sciences, ISSN 1546-9239, pp- 756-764,
2007.
[4] Sakir Tasdemir, Ismail Saritas, Murat
Ciniviz, Novruz Allahverdi, “Artificial
neural network and fuzzy expert system
comparison for prediction of performance
6. Jitendra S. Pachbhai et al Int. Journal of Engineering Research and Applicationswww.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 2), May 2014, pp.158-163
www.ijera.com 163|P a g e
and emission parameters on a gasoline
engine”, Journal of Expert Systems with
Applications, pp. 13912–13923, 2011.
[5] Md. Nurun Nabi, Md. Mustafizur Rahman,
Md. Shamim Akhter, “Biodiesel from cotton
seed oil and its effect on engine performance
and exhaust emissions”, Journal of Applied
Thermal Engineering, pp. 2265–2270, 2009.
[6] C.V. Subba Reddy, C. Eswara Reddy & K.
Hemachandra Reddy, “Effect Of Fuel
Injection Pressures On The Performance and
Emission Characteristics Of D. I. Diesel
Engine With Biodiesel Blends Cotton Seed
Oil Methyl Ester”, Journal of IJRRAS, Vol.
III, pp. 139-149, 2012.
[7] S. Rajasekaran, “Neural Networks, Fuzzy
Logic, and Genetic Algorithms”, PHI
Learning Ltd; New Delhi, pp.12-84, 2008.
[8] Simon Haykin, “Neural Network and
Learning Machines”, PHI Learning Ltd;
New Delhi, pp. 1-221, 2010.