Search results for: vector error correction model
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 18095

Search results for: vector error correction model

17975 Efficient Antenna Array Beamforming with Robustness against Random Steering Mismatch

Authors: Ju-Hong Lee, Ching-Wei Liao, Kun-Che Lee

Abstract:

This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

Keywords: adaptive beamforming, antenna array, linearly constrained minimum variance, robustness, steering vector

Procedia PDF Downloads 162
17974 Hybrid Approach for Country’s Performance Evaluation

Authors: C. Slim

Abstract:

This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

Procedia PDF Downloads 295
17973 Machine Learning Approach for Automating Electronic Component Error Classification and Detection

Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

Abstract:

The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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17972 Reducing Uncertainty in Climate Projections over Uganda by Numerical Models Using Bias Correction

Authors: Isaac Mugume

Abstract:

Since the beginning of the 21st century, climate change has been an issue due to the reported rise in global temperature and changes in the frequency as well as severity of extreme weather and climatic events. The changing climate has been attributed to rising concentrations of greenhouse gases, including environmental changes such as ecosystems and land-uses. Climatic projections have been carried out under the auspices of the intergovernmental panel on climate change where a couple of models have been run to inform us about the likelihood of future climates. Since one of the major forcings informing the changing climate is emission of greenhouse gases, different scenarios have been proposed and future climates for different periods presented. The global climate models project different areas to experience different impacts. While regional modeling is being carried out for high impact studies, bias correction is less documented. Yet, the regional climate models suffer bias which introduces uncertainty. This is addressed in this study by bias correcting the regional models. This study uses the Weather Research and Forecasting model under different representative concentration pathways and correcting the products of these models using observed climatic data. This study notes that bias correction (e.g., the running-mean bias correction; the best easy systematic estimator method; the simple linear regression method, nearest neighborhood, weighted mean) improves the climatic projection skill and therefore reduce the uncertainty inherent in the climatic projections.

Keywords: bias correction, climatic projections, numerical models, representative concentration pathways

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17971 A Mathematical Model for 3-DOF Rotary Accuracy Measurement Method Based on a Ball Lens

Authors: Hau-Wei Lee, Yu-Chi Liu, Chien-Hung Liu

Abstract:

A mathematical model is presented for a system that measures rotational errors in a shaft using a ball lens. The geometric optical characteristics of the ball lens mounted on the shaft allows the measurement of rotation axis errors in both the radial and axial directions. The equipment used includes two quadrant detectors (QD), two laser diodes and a ball lens that is mounted on the rotating shaft to be evaluated. Rotational errors in the shaft cause changes in the optical geometry of the ball lens. The resulting deflection of the laser beams is detected by the QDs and their output signals are used to determine rotational errors. The radial and the axial rotational errors can be calculated as explained by the mathematical model. Results from system calibration show that the measurement error is within ±1 m and resolution is about 20 nm. Using a direct drive motor (DD motor) as an example, experimental results show a rotational error of less than 20 m. The most important features of this system are that it does not require the use of expensive optical components, it is small, very easy to set up, and measurements are highly accurate.

Keywords: ball lens, quadrant detector, axial error, radial error

Procedia PDF Downloads 418
17970 Relationship between Electricity Consumption and Economic Growth: Evidence from Nigeria (1971-2012)

Authors: N. E Okoligwe, Okezie A. Ihugba

Abstract:

Few scholars disagrees that electricity consumption is an important supporting factor for economy growth. However, the relationship between electricity consumption and economy growth has different manifestation in different countries according to previous studies. This paper examines the causal relationship between electricity consumption and economic growth for Nigeria. In an attempt to do this, the paper tests the validity of the modernization or depending hypothesis by employing various econometric tools such as Augmented Dickey Fuller (ADF) and Johansen Co-integration test, the Error Correction Mechanism (ECM) and Granger Causality test on time series data from 1971-2012. The Granger causality is found not to run from electricity consumption to real GDP and from GDP to electricity consumption during the year of study. The null hypothesis is accepted at the 5 per cent level of significance where the probability value (0.2251 and 0.8251) is greater than five per cent level of significance because both of them are probably determined by some other factors like; increase in urban population, unemployment rate and the number of Nigerians that benefit from the increase in GDP and increase in electricity demand is not determined by the increase in GDP (income) over the period of study because electricity demand has always been greater than consumption. Consequently; the policy makers in Nigeria should place priority in early stages of reconstruction on building capacity additions and infrastructure development of the electric power sector as this would force the sustainable economic growth in Nigeria.

Keywords: economic growth, electricity consumption, error correction mechanism, granger causality test

Procedia PDF Downloads 267
17969 A Low Order Thermal Envelope Model for Heat Transfer Characteristics of Low-Rise Residential Buildings

Authors: Nadish Anand, Richard D. Gould

Abstract:

A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

Procedia PDF Downloads 229
17968 Tests for Zero Inflation in Count Data with Measurement Error in Covariates

Authors: Man-Yu Wong, Siyu Zhou, Zhiqiang Cao

Abstract:

In quality of life, health service utilization is an important determinant of medical resource expenditures on Colorectal cancer (CRC) care, a better understanding of the increased utilization of health services is essential for optimizing the allocation of healthcare resources to services and thus for enhancing the service quality, especially for high expenditure on CRC care like Hong Kong region. In assessing the association between the health-related quality of life (HRQOL) and health service utilization in patients with colorectal neoplasm, count data models can be used, which account for over dispersion or extra zero counts. In our data, the HRQOL evaluation is a self-reported measure obtained from a questionnaire completed by the patients, misreports and variations in the data are inevitable. Besides, there are more zero counts from the observed number of clinical consultations (observed frequency of zero counts = 206) than those from a Poisson distribution with mean equal to 1.33 (expected frequency of zero counts = 156). This suggests that excess of zero counts may exist. Therefore, we study tests for detecting zero-inflation in models with measurement error in covariates. Method: Under classical measurement error model, the approximate likelihood function for zero-inflation Poisson regression model can be obtained, then Approximate Maximum Likelihood Estimation(AMLE) can be derived accordingly, which is consistent and asymptotically normally distributed. By calculating score function and Fisher information based on AMLE, a score test is proposed to detect zero-inflation effect in ZIP model with measurement error. The proposed test follows asymptotically standard normal distribution under H0, and it is consistent with the test proposed for zero-inflation effect when there is no measurement error. Results: Simulation results show that empirical power of our proposed test is the highest among existing tests for zero-inflation in ZIP model with measurement error. In real data analysis, with or without considering measurement error in covariates, existing tests, and our proposed test all imply H0 should be rejected with P-value less than 0.001, i.e., zero-inflation effect is very significant, ZIP model is superior to Poisson model for analyzing this data. However, if measurement error in covariates is not considered, only one covariate is significant; if measurement error in covariates is considered, only another covariate is significant. Moreover, the direction of coefficient estimations for these two covariates is different in ZIP regression model with or without considering measurement error. Conclusion: In our study, compared to Poisson model, ZIP model should be chosen when assessing the association between condition-specific HRQOL and health service utilization in patients with colorectal neoplasm. and models taking measurement error into account will result in statistically more reliable and precise information.

Keywords: count data, measurement error, score test, zero inflation

Procedia PDF Downloads 251
17967 Parallel Vector Processing Using Multi Level Orbital DATA

Authors: Nagi Mekhiel

Abstract:

Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory Organization, Parallel Processors, Serial Code, Vector Processing

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17966 Numerical Analysis of 3D Electromagnetic Fields in Annular Induction Plasma

Authors: Abderazak Guettaf

Abstract:

The mathematical models of the physical phenomena interacting in inductive plasma were described by the physics equations of the continuous mediums. A 3D model based on magnetic potential vector and electric scalar potential (A, V) formulation is used. The finished volume method is applied to electromagnetic equation, to obtain the field distribution inside the plasma. The numerical results of the method developed on a basic model designed starting from a real three-dimensional model were exposed. From the mathematical model 3D spreading assumptions and boundary conditions, we evaluated the electric field in the load and we have developed a numerical code made under the MATLAB environment, all verifying the effectiveness and validity of this code.

Keywords: electric field, 3D magnetic potential vector and electric scalar potential (A, V) formulation, finished volumes, annular plasma

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17965 Energy Consumption, Population and Economic Development Dynamics in Nigeria: An Empirical Evidence

Authors: Evelyn Nwamaka Ogbeide-Osaretin, Bright Orhewere

Abstract:

This study examined the role of the population in the linkage between energy consumption and economic development in Nigeria. Time series data on energy consumption, population, and economic development were used for the period 1995 to 2020. The Autoregressive Distributed Lag -Error Correction Model (ARDL-ECM) was engaged. Economic development had a negative substantial impact on energy consumption in the long run. Population growth had a positive significant effect on energy consumption. Government expenditure was also found to impact the level of energy consumption, while energy consumption is not a function of oil price in Nigeria.

Keywords: dynamic analysis, energy consumption, population, economic development, Nigeria

Procedia PDF Downloads 138
17964 Multiclass Support Vector Machines with Simultaneous Multi-Factors Optimization for Corporate Credit Ratings

Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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17963 High Performance of Direct Torque and Flux Control of a Double Stator Induction Motor Drive with a Fuzzy Stator Resistance Estimator

Authors: K. Kouzi

Abstract:

In order to have stable and high performance of direct torque and flux control (DTFC) of double star induction motor drive (DSIM), proper on-line adaptation of the stator resistance is very important. This is inevitably due to the variation of the stator resistance during operating conditions, which introduces error in estimated flux position and the magnitude of the stator flux. Error in the estimated stator flux deteriorates the performance of the DTFC drive. Also, the effect of error in estimation is very important especially at low speed. Due to this, our aim is to overcome the sensitivity of the DTFC to the stator resistance variation by proposing on-line fuzzy estimation stator resistance. The fuzzy estimation method is based on an on-line stator resistance correction through the variations of the stator current estimation error and its variations. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of the suggested algorithm control is to avoid the drive instability that may occur in certain situations and ensure the tracking of the actual stator resistance. The validity of the technique and the improvement of the whole system performance are proved by the results.

Keywords: direct torque control, dual stator induction motor, Fuzzy Logic estimation, stator resistance adaptation

Procedia PDF Downloads 286
17962 Two Concurrent Convolution Neural Networks TC*CNN Model for Face Recognition Using Edge

Authors: T. Alghamdi, G. Alaghband

Abstract:

In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

Procedia PDF Downloads 111
17961 A Generalized Weighted Loss for Support Vextor Classification and Multilayer Perceptron

Authors: Filippo Portera

Abstract:

Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

Procedia PDF Downloads 52
17960 Application of Residual Correction Method on Hyperbolic Thermoelastic Response of Hollow Spherical Medium in Rapid Transient Heat Conduction

Authors: Po-Jen Su, Huann-Ming Chou

Abstract:

In this article we uses the residual correction method to deal with transient thermoelastic problems with a hollow spherical region when the continuum medium possesses spherically isotropic thermoelastic properties. Based on linear thermoelastic theory, the equations of hyperbolic heat conduction and thermoelastic motion were combined to establish the thermoelastic dynamic model with consideration of the deformation acceleration effect and non-Fourier effect under the condition of transient thermal shock. The approximate solutions of temperature and displacement distributions are obtained using the residual correction method based on the maximum principle in combination with the finite difference method, making it easier and faster to obtain upper and lower approximations of exact solutions. The proposed method is found to be an effective numerical method with satisfactory accuracy. Moreover, the result shows that the effect of transient thermal shock induced by deformation acceleration is enhanced by non-Fourier heat conduction with increased peak stress. The influence on the stress increases with the thermal relaxation time.

Keywords: maximum principle, non-Fourier heat conduction, residual correction method, thermo-elastic response

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17959 Using Support Vector Machines for Measuring Democracy

Authors: Tommy Krieger, Klaus Gruendler

Abstract:

We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies.

Keywords: democracy, democracy index, machine learning, support vector machines

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17958 The Effect of the Acquisition and Reconstruction Parameters in Quality of Spect Tomographic Images with Attenuation and Scatter Correction

Authors: N. Boutaghane, F. Z. Tounsi

Abstract:

Many physical and technological factors degrade the SPECT images, both qualitatively and quantitatively. For this, it is not always put into leading technological advances to improve the performance of tomographic gamma camera in terms of detection, collimation, reconstruction and correction of tomographic images methods. We have to master firstly the choice of various acquisition and reconstruction parameters, accessible to clinical cases and using the attenuation and scatter correction methods to always optimize quality image and minimized to the maximum dose received by the patient. In this work, an evaluation of qualitative and quantitative tomographic images is performed based on the acquisition parameters (counts per projection) and reconstruction parameters (filter type, associated cutoff frequency). In addition, methods for correcting physical effects such as attenuation and scatter degrading the image quality and preventing precise quantitative of the reconstructed slices are also presented. Two approaches of attenuation and scatter correction are implemented: the attenuation correction by CHANG method with a filtered back projection reconstruction algorithm and scatter correction by the subtraction JASZCZAK method. Our results are considered as such recommandation, which permits to determine the origin of the different artifacts observed both in quality control tests and in clinical images.

Keywords: attenuation, scatter, reconstruction filter, image quality, acquisition and reconstruction parameters, SPECT

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17957 Structural Damage Detection Using Modal Data Employing Teaching Learning Based Optimization

Authors: Subhajit Das, Nirjhar Dhang

Abstract:

Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

Procedia PDF Downloads 179
17956 Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Authors: Jiangbo Shi, Zeyu Gao, Chen Li

Abstract:

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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17955 Advanced Model for Calculation of the Neutral Axis Shifting and the Wall Thickness Distribution in Rotary Draw Bending Processes

Authors: B. Engel, H. Hassan

Abstract:

Rotary draw bending is a method which is being used in tube forming. In the tube bending process, the neutral axis moves towards the inner arc and the wall thickness distribution changes for tube’s cross section. Thinning takes place in the outer arc of the tube (extrados) due to the stretching of the material, whereas thickening occurs in the inner arc of the tube (intrados) due to the comparison of the material. The calculations of the wall thickness distribution, neutral axis shifting, and strain distribution have not been accurate enough, so far. The previous model (the geometrical model) describes the neutral axis shifting and wall thickness distribution. The geometrical of the tube, bending radius and bending angle are considered in the geometrical model, while the influence of the material properties of the tube forming are ignored. The advanced model is a modification of the previous model using material properties that depends on the correction factor. The correction factor is a purely empirically determined factor. The advanced model was compared with the Finite element simulation (FE simulation) using a different bending factor (Bf=bending radius/ diameter of the tube), wall thickness (Wf=diameter of the tube/ wall thickness), and material properties (strain hardening exponent). Finite element model of rotary draw bending has been performed in PAM-TUBE program (version: 2012). Results from the advanced model resemble the FE simulation and the experimental test.

Keywords: rotary draw bending, material properties, neutral axis shifting, wall thickness distribution

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17954 Experimental Implementation of Model Predictive Control for Permanent Magnet Synchronous Motor

Authors: Abdelsalam A. Ahmed

Abstract:

Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is drived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control technique is investigated by comparing to the cascaded PI control based on Space Vector Pulse Width Modulation (SVPWM). MPC and SVPWM-based FOC are implemented with the TMS320F2812 DSP and its power driver circuits. The designed MPC for a PMSM drive is experimentally validated on a laboratory test bench. The performances are compared with those obtained by a conventional PI-based system in order to highlight the improvements, especially regarding speed tracking response.

Keywords: permanent magnet synchronous motor, model-based predictive control, DC source utilization, cascaded PI control, space vector pulse width modulation, TMS320F2812 DSP

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17953 On Hyperbolic Gompertz Growth Model (HGGM)

Authors: S. O. Oyamakin, A. U. Chukwu,

Abstract:

We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

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17952 Core Loss Influence on MTPA Current Vector Variation of Synchronous Reluctance Machine

Authors: Huai-Cong Liu, Tae Chul Jeong, Ju Lee

Abstract:

The aim of this study was to develop an electric circuit method (ECM) to ascertain the core loss influence on a Synchronous Reluctance Motor (SynRM) in the condition of the maximum torque per ampere (MTPA). SynRM for fan usually operates on the constant torque region, at synchronous speed the MTPA control is adopted due to current vector. However, finite element analysis (FEA) program is not sufficient exactly to reflect how the core loss influenced on the current vector. This paper proposed a method to calculate the current vector with consideration of core loss. The precision of current vector by ECM is useful for MTPA control. The result shows that ECM analysis is closer to the actual motor’s characteristics by testing with a 7.5kW SynRM drive System.

Keywords: core loss, SynRM, current vector, magnetic saturation, maximum torque per ampere (MTPA)

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17951 Modeling of Diurnal Pattern of Air Temperature in a Tropical Environment: Ile-Ife and Ibadan, Nigeria

Authors: Rufus Temidayo Akinnubi, M. O. Adeniyi

Abstract:

Existing diurnal air temperature models simulate night time air temperature over Nigeria with high biases. An improved parameterization is presented for modeling the diurnal pattern of air temperature (Ta) which is applicable in the calculation of turbulent heat fluxes in Global climate models, based on Nigeria Micrometeorological Experimental site (NIMEX) surface layer observations. Five diurnal Ta models for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature were validated using root-mean-square error (RMSE), Mean Error Bias (MBE) and scatter graphs. The original Fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated with a large error. The model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion that occurs during the nocturnal boundary layer condition. The MBE and RMSE estimated by the modified Fourier series model reduced by 4.45 oC and 3.12 oC during the transitional period from dry to wet stable atmospheric conditions. The modified Fourier series model gave good estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

Keywords: air temperature, mean bias error, Fourier series analysis, surface energy balance,

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17950 A Hierarchical Method for Multi-Class Probabilistic Classification Vector Machines

Authors: P. Byrnes, F. A. DiazDelaO

Abstract:

The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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17949 The Relationships between Carbon Dioxide (CO2) Emissions, Energy Consumption and GDP per capita for Oman: Time Series Analysis, 1980–2010

Authors: Jinhoa Lee

Abstract:

The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfil the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption, carbon dioxide (CO2) emissions and gross domestic product (GDP) for Oman using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey Fuller (ADF) test for stationary, Johansen maximum likelihood method for co-integration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests positive long-run causalities from CO2 emissions to GDP. Conversely, negative impacts of energy consumption on GDP are found to be significant in Oman during the period. In the short run, there exist negative unidirectional causalities among GDP, CO2 emissions and energy consumption running from GDP to CO2 emissions and from energy consumption to CO2 emissions. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output in Oman over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Oman, time series analysis

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17948 Concrete Cracking Simulation Using Vector Form Intrinsic Finite Element Method

Authors: R. Z. Wang, B. C. Lin, C. H. Huang

Abstract:

This study proposes a new method to simulate the crack propagation under mode-I loading using Vector Form Intrinsic Finite Element (VFIFE) method. A new idea which is expected to combine both VFIFE and J-integral is proposed to calculate the stress density factor as the crack critical in elastic crack. The procedure of implement the cohesive crack propagation in VFIFE based on the fictitious crack model is also proposed. In VFIFIE, the structure deformation is described by numbers of particles instead of elements. The strain energy density and the derivatives of the displacement vector of every particle is introduced to calculate the J-integral as the integral path is discrete by particles. The particle on the crack tip separated into two particles once the stress on the crack tip satisfied with the crack critical and then the crack tip propagates to the next particle. The internal force and the cohesive force is applied to the particles.

Keywords: VFIFE, crack propagation, fictitious crack model, crack critical

Procedia PDF Downloads 297
17947 Developed Text-Independent Speaker Verification System

Authors: Mohammed Arif, Abdessalam Kifouche

Abstract:

Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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17946 Artificial Intelligence Based Predictive Models for Short Term Global Horizontal Irradiation Prediction

Authors: Kudzanayi Chiteka, Wellington Makondo

Abstract:

The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

Procedia PDF Downloads 238