11,666 research outputs found
Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid
[Abstract]: A comprehensive combustion analysis has been conducted to evaluate the performance of a
commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini
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
properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards.
The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the
existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The
maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400
rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that 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. Multi layer perceptron network (MLP) was
used for nonlinear mapping between the input and the output parameters. Different activation functions
and several rules were used to assess the percentage error between the desired and the predicted values. 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 R2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption,CO and HC emissions, respectively
Incremental construction of LSTM recurrent neural network
Long Short--Term Memory (LSTM) is a recurrent neural network that
uses structures called memory blocks to allow the net remember
significant events distant in the past input sequence in order to
solve long time lag tasks, where other RNN approaches fail.
Throughout this work we have performed experiments using LSTM
networks extended with growing abilities, which we call GLSTM.
Four methods of training growing LSTM has been compared. These
methods include cascade and fully connected hidden layers as well
as two different levels of freezing previous weights in the
cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five
controllers of the Central Nervous System control has to be
modelled. We have compared growing LSTM results against other
neural networks approaches, and our work applying conventional
LSTM to the task at hand.Postprint (published version
Reducing the loss of information through annealing text distortion
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Granados, A. ;Cebrian, M. ; Camacho, D. ; de Borja Rodriguez, F. "Reducing the Loss of Information through Annealing Text Distortion". IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 7 pp. 1090 - 1102, July 2011Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.This work was supported by the Spanish Ministry of Education and Science under TIN2010-19872 and TIN2010-19607 projects
Machine Learning-Based Prediction of Compressive Performance in Circular Concrete Columns Confined with FRP
This article presents a comprehensive investigation, focusing on the prediction and formulation of the design equation of compressive strength of circular concrete columns confined with Fiber Reinforced Polymer (FRP) using advanced machine learning models. Through an extensive analysis of 170 experimental data specimens, the study examines the effects of six key parameters, including concrete cylinder diameter, concrete cylinder-FRP thickness, compressive strength of concrete without FRP, initial compressive strain of concrete without FRP, elastic modulus and tensile strength of FRP, on the compressive strength of the circular concrete columns confined with FRP. The predictive model and design equation of compressive strength is developed using a machine learning technique, specifically the artificial neural networks (ANN) model. The results demonstrates strong correlations between the compressive strength of the circular concrete columns confined with FRP and certain factors, such as the compressive strength of the concrete and compressive strain of the concrete column without FRP, elastic modulus of FRP, and tensile strength of FRP. The ANN model specifically developed using Neural Designer, exhibits superior predictive accuracy compared to other constitutive models, showcasing its potential for practical implementation. The study's findings contribute valuable insights into accurately predicting the compressive performance of circular concrete columns confined with FRP, which can aid in optimizing and designing civil engineering structures for enhanced performance and efficiency
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