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Tool condition monitoring is one of the key issues in mechanical micromachining for efficient manufacturing of the micro-parts in several industries. In the present study, a tool condition monitoring system for micro-drilling is developed using a tri-axial accelerometer, a data acquisition and signal processing module and an artificial neural network. Micro-drilling experiments were carried out on...
Machining parameters influence the energy consumed during machining processes. A reliable prediction model for energy consumption will enable industry to achieving energy saving by optimizing the machining parameters during process planning stage. This paper presents a two-level optimization artificial neural network modelling method to characterizing the relationship between energy consumption and...
Closing prices of the financial stock market change daily at the end of each session. These changes happen because of many factors that affect the prices of the stocks. This study attempts to accurately predict closing prices by applying a data mining approach and investigate and identify the most influential factors of Dubai Financial Stock Market prices. The main objective of this study is to help...
Traffic flow prediction is a fundamental functionality of intelligent transportation systems. After presenting the state of the art, we focus on nearest neighbor regression methods, which are data-driven algorithms that are effective yet simple to implement. We try to strengthen their efficacy in two ways that are little explored in literature, i.e., by adopting a multivariate approach and by adding...
Multilayer perceptron (MLP) based artificial neural network (ANN) equalizers, deploying back propagation (BP) training algorithm, have been profusely used for equalization earlier. However this algorithm suffers from slow convergence rate, depending on the size of network. In this paper, Levenberg-Marquardt and Scaled Conjugate algorithms are proposed to train an MLP based ANN for least square (LS)...
The global trend of population aging and the continuing maturity of the Internet of Things (IoT) technology drives the rapid development of health care. In the comprehensive applications of IoT technology, developing and constructing a prediction model for chronic diseases is a great improvement to healthcare technology as well as an exploration of IoT technology on the data-analysis and decision-making...
Estimating the wake losses in a wind farm is critical in the short term forecast of wind power, following the Numerical Weather Prediction (NWP) approach. Understanding the intensity of the wakes and the nature of its propagation within the wind farm still remains a challenge to scientist, engineers and utility operators. In this paper, five different machine learning methods are used to estimate...
This paper presented three types of models for forecasting the supply and demand of Thai ethanol, so called MR, ANN, and MR-ANN models. MR models were formulated using stepwise multiple regression analysis, which were statistically significant. However, MR models provided low performance in forecasting. ANN models were constructed using artificial neural networks, which provided satisfactory results...
Global illumination is important and hard to render in computer graphics. We propose a novel technique to generate high quality global illumination images using regression analysis. Our algorithm has two stages. During sampling stage, we get the indirect light field and use it to train an artificial neural network model. In reconstruction stage, the neural network model is used to synthesize the final...
Power System planning starts with Electric load (demand) forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity is volatile in nature; it cannot be stored and has to be consumed immediately. Artificial Neural Network (ANN) is applied to predict the annual electricity consumption in India for...
In this paper, we evaluate realizations for implementing an RFID reflected electro-material signature (REMS) sensor. REMS sensors allow passive measurement, recording, and reading of environmental data such as temperature in a small, low cost device. This paper presents results from two configurations: a three-section lossless microstrip transmission line and a monopole probe inserted into a lossy...
Strong correlation exists between river discharge and suspended sediment load. The relationship was used to estimate suspended sediment load by using linear regression model, power regression model, artificial neural network and support vector machine in this study. Records of river discharges and suspended sediment loads in Kaoping river basin were investigated as case study. Eighty-five percent...
The cost of experimental setup during an assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under fill process consists of only a few samples. As a result, existing machine learning algorithms for predictive modeling cannot be applied to this setting. Despite...
This study aims to replicate knowledge of operation for water supply system. The proposed method is able to extract knowledge of operation of experienced operators, and regenerate a plan of operation by using support vector regression. The proposed method can contribute to compensating for reduction of experienced operators.
Software development effort estimation is one of the most major activities in software project management. A number of models have been proposed to make software effort estimations but still no single model can predict the effort accurately. The need for accurate effort estimation in software industry is still a challenge. Artificial Neural Network models can be used for carrying out the effort estimations...
Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem,...
In this paper mathematical models for transmission line are expressed in second-order partial differential equations derived by analyzing magnetic field distribution around a 500-kV power transmission line under normal loading and short-circuit conditions. The problem of study is intentionally two-dimensional due to the property of long line field distribution. To verify its use, i) single-circuit...
We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. It was found that infrared spectroscopy is an effective tool to detect melamine in liquid milk. The limit of detection (LOD) below 1 ppm (0.75 ppm) can be reached if a correct spectrum pre-processing (pre-treatment)...
Feature extraction methods in pattern recognition tasks seek to transform data variables to abstract mathematical variables such that their scores (called features) reveal hidden data structure of high cognitive value. Various feature extraction methods process raw data from different perspectives. Some depend on statistical correlation or independence such as principal component analysis (PCA), independent...
In the current power and energy scenario, distributed generation (DG) has generated a lot of interest across the globe due to the growing concerns about gradual depletion of fossil fuels, steep load growth, environmental pollution and global warming caused by greenhouse gas emissions. Renewable DGs such as wind generators and solar photovoltaic are well-recognized now-a-days as sources of clean energy...
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