Search results for: peak estimation
2878 Fire Performance of Fly Ash Concrete with Pre-Fire Load
Authors: Kunjie Fan
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Fly ash has been widely used as supplemental cementitious material in concrete for decades, especially in the ready-mixed concrete industry. Addition of fly ash not only brings economic and environmental benefits but also improves the engineering properties of concrete. It is well known that the pre-fire load has significant impacts on mechanical properties of concrete at high temperatures, however, the fire performance of stressed fly ash concrete is still not clear. Therefore, an apparatus was specially designed for testing “hot” mechanical properties of fly ash concrete with different heating-loading regimes. Through the experimental research, the mechanical properties, including compressive strength, peak strain, elastic modulus, complete stress-strain relationship, and transient thermal creep of fly ash concrete under uniaxial compression at elevated temperatures, have been investigated. It was found that the compressive strength and the elastic modulus increase with the load level, while the peak strain decreases with the applied stress level. In addition, 25% replacement of OPC with FA in the concrete mitigated the deterioration of the compressive strength, the development of transient thermal creep, and the nonlinearity of stress-strain response at elevated temperatures but hardly influenced the value of the elastic modulus and the peak strain. The applicability of Eurocode EN1992-1-2 to normal strength concrete with 25% replacement of fly ash has been verified to be safe. Based on the experimental analysis, an advanced constitutive model for stressed fly ash concrete at high temperatures was proposed.Keywords: fire performance, fly ash concrete, pre-fire load, mechanical properties, transient thermal creep
Procedia PDF Downloads 722877 Robust Heart Rate Estimation from Multiple Cardiovascular and Non-Cardiovascular Physiological Signals Using Signal Quality Indices and Kalman Filter
Authors: Shalini Rankawat, Mansi Rankawat, Rahul Dubey, Mazad Zaveri
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Physiological signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) in the intensive care unit (ICU) are often seriously corrupted by noise, artifacts, and missing data, which lead to errors in the estimation of heart rate (HR) and incidences of false alarm from ICU monitors. Clinical support in ICU requires most reliable heart rate estimation. Cardiac activity, because of its relatively high electrical energy, may introduce artifacts in Electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG) recordings. This paper presents a robust heart rate estimation method by detection of R-peaks of ECG artifacts in EEG, EMG & EOG signals, using energy-based function and a novel Signal Quality Index (SQI) assessment technique. SQIs of physiological signals (EEG, EMG, & EOG) were obtained by correlation of nonlinear energy operator (teager energy) of these signals with either ECG or ABP signal. HR is estimated from ECG, ABP, EEG, EMG, and EOG signals from separate Kalman filter based upon individual SQIs. Data fusion of each HR estimate was then performed by weighing each estimate by the Kalman filters’ SQI modified innovations. The fused signal HR estimate is more accurate and robust than any of the individual HR estimate. This method was evaluated on MIMIC II data base of PhysioNet from bedside monitors of ICU patients. The method provides an accurate HR estimate even in the presence of noise and artifacts.Keywords: ECG, ABP, EEG, EMG, EOG, ECG artifacts, Teager-Kaiser energy, heart rate, signal quality index, Kalman filter, data fusion
Procedia PDF Downloads 6902876 Extended Kalman Filter Based Direct Torque Control of Permanent Magnet Synchronous Motor
Authors: Liang Qin, Hanan M. D. Habbi
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A robust sensorless speed for permanent magnet synchronous motor (PMSM) has been presented for estimation of stator flux components and rotor speed based on The Extended Kalman Filter (EKF). The model of PMSM and its EKF models are modeled in Matlab /Sirnulink environment. The proposed EKF speed estimation method is also proved insensitive to the PMSM parameter variations. Simulation results demonstrate a good performance and robustness.Keywords: DTC, Extended Kalman Filter (EKF), PMSM, sensorless control, anti-windup PI
Procedia PDF Downloads 6552875 DOA Estimation Using Golden Section Search
Authors: Niharika Verma, Sandeep Santosh
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DOA technique is a localization technique used in the communication field. Various algorithms have been developed for direction of arrival estimation like MUSIC, ROOT MUSIC, etc. These algorithms depend on various parameters like antenna array elements, number of snapshots and various others. Basically the MUSIC spectrum is evaluated and peaks obtained are considered as the angle of arrivals. The angles evaluated using this process depends on the scanning interval chosen. The accuracy of the results obtained depends on the coarseness of the interval chosen. In this paper, golden section search is applied to the MUSIC algorithm and therefore, more accurate results are achieved. Initially the coarse DOA estimations is done using the MUSIC algorithm in the range -90 to 90 degree at the interval of 10 degree. After the peaks obtained then fine DOA estimation is done using golden section search. Also, the partitioning method is applied to estimate the number of signals incident on the antenna array. Dependency of the algorithm on the number of snapshots is also being explained. Hence, the accurate results are being determined using this algorithm.Keywords: Direction of Arrival (DOA), golden section search, MUSIC, number of snapshots
Procedia PDF Downloads 4402874 Exponentiated Transmuted Weibull Distribution: A Generalization of the Weibull Probability Distribution
Authors: Abd El Hady N. Ebraheim
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This paper introduces a new generalization of the two parameter Weibull distribution. To this end, the quadratic rank transmutation map has been used. This new distribution is named exponentiated transmuted Weibull (ETW) distribution. The ETW distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. Furthermore, eleven lifetime distributions such as the Weibull, exponentiated Weibull, Rayleigh and exponential distributions, among others follow as special cases. The properties of the new model are discussed and the maximum likelihood estimation is used to estimate the parameters. Explicit expressions are derived for the quantiles. The moments of the distribution are derived, and the order statistics are examined.Keywords: exponentiated, inversion method, maximum likelihood estimation, transmutation map
Procedia PDF Downloads 5632873 An Automated R-Peak Detection Method Using Common Vector Approach
Authors: Ali Kirkbas
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R peaks in an electrocardiogram (ECG) are signs of cardiac activity in individuals that reveal valuable information about cardiac abnormalities, which can lead to mortalities in some cases. This paper examines the problem of detecting R-peaks in ECG signals, which is a two-class pattern classification problem in fact. To handle this problem with a reliable high accuracy, we propose to use the common vector approach which is a successful machine learning algorithm. The dataset used in the proposed method is obtained from MIT-BIH, which is publicly available. The results are compared with the other popular methods under the performance metrics. The obtained results show that the proposed method shows good performance than that of the other. methods compared in the meaning of diagnosis accuracy and simplicity which can be operated on wearable devices.Keywords: ECG, R-peak classification, common vector approach, machine learning
Procedia PDF Downloads 532872 Analysis of Secondary Peak in Hα Emission Profile during Gas Puffing in Aditya Tokamak
Authors: Harshita Raj, Joydeep Ghosh, Rakesh L. Tanna, Prabal K. Chattopadhyay, K. A. Jadeja, Sharvil Patel, Kaushal M. Patel, Narendra C. Patel, S. B. Bhatt, V. K. Panchal, Chhaya Chavda, C. N. Gupta, D. Raju, S. K. Jha, J. Raval, S. Joisa, S. Purohit, C. V. S. Rao, P. K. Atrey, Umesh Nagora, R. Manchanda, M. B. Chowdhuri, Nilam Ramaiya, S. Banerjee, Y. C. Saxena
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Efficient gas fueling is a critical aspect that needs to be mastered in order to maintain plasma density, to carry out fusion. This requires a fair understanding of fuel recycling in order to optimize the gas fueling. In Aditya tokamak, multiple gas puffs are used in a precise and controlled manner, for hydrogen fueling during the flat top of plasma discharge which has been instrumental in achieving discharges with enhanced density as well as energy confinement time. Following each gas puff, we observe peaks in temporal profile of Hα emission, Soft X-ray (SXR) and chord averaged electron density in a number of discharges, indicating efficient gas fueling. Interestingly, Hα temporal profile exhibited an additional peak following the peak corresponding to each gas puff. These additional peak Hα appeared in between the two gas puffs, indicating the presence of a secondary hydrogen source apart from the gas puffs. A thorough investigation revealed that these secondary Hα peaks coincide with Hard X- ray bursts which come from the interaction of runaway electrons with vessel limiters. This leads to consider that the runaway electrons (REs), which hit the wall, in turn, bring out the absorbed hydrogen and oxygen from the wall and makes the interaction of REs with limiter a secondary hydrogen source. These observations suggest that runaway electron induced recycling should also be included in recycling particle source in the particle balance calculations in tokamaks. Observation of two Hα peaks associated with one gas puff and their roles in enhancing and maintaining plasma density in Aditya tokamak will be discussed in this paper.Keywords: fusion, gas fueling, recycling, Tokamak, Aditya
Procedia PDF Downloads 3952871 Comparison of Statistical Methods for Estimating Missing Precipitation Data in the River Subbasin Lenguazaque, Colombia
Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza
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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant
Procedia PDF Downloads 4632870 Age Estimation from Teeth among North Indian Population: Comparison and Reliability of Qualitative and Quantitative Methods
Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan
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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.Keywords: forensic odontology, age estimation, North India, teeth
Procedia PDF Downloads 2372869 Median-Based Nonparametric Estimation of Returns in Mean-Downside Risk Portfolio Frontier
Authors: H. Ben Salah, A. Gannoun, C. de Peretti, A. Trabelsi
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The Downside Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developped a new recurcive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents an appearance which is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which we had continuous observations. In this paper, taking advantage on the the robustness of the median, we replace the mean estimator in Athayde's model by a nonparametric median estimator of the returns. Then, we give a new version of the former algorithm (of Athayde (2001, 2003)). We eventually analyse the properties of this improved portfolio frontier and apply this new method on real examples.Keywords: Downside Risk, Kernel Method, Median, Nonparametric Estimation, Semivariance
Procedia PDF Downloads 4842868 Ratio Type Estimators for the Estimation of Population Coefficient of Variation under Two-Stage Sampling
Authors: Muhammad Jabbar
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In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information under two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator.Keywords: two-stage sampling, coefficient of variation, ratio type exponential estimator
Procedia PDF Downloads 5212867 The Evaluation of the Performance of Different Filtering Approaches in Tracking Problem and the Effect of Noise Variance
Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri
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Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results.Keywords: Gaussian approximation, Kalman smoother, parameter estimation, noise variance
Procedia PDF Downloads 4302866 Genetic Algorithm and Multi Criteria Decision Making Approach for Compressive Sensing Based Direction of Arrival Estimation
Authors: Ekin Nurbaş
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One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing
Procedia PDF Downloads 1392865 Simplified Measurement of Occupational Energy Expenditure
Authors: J. Wicks
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Aim: To develop a simple methodology to allow collected heart rate (HR) data from inexpensive wearable devices to be expressed in a suitable format (METs) to quantitate occupational (and recreational) activity. Introduction: Assessment of occupational activity is commonly done by utilizing questionnaires in combination with prescribed MET levels of a vast range of previously measured activities. However for any individual the intensity of performing a specific activity can vary significantly. Ideally objective measurement of individual activity is preferred. Though there are a wide range of HR recording devices there is a distinct lack methodology to allow processing of collected data to quantitate energy expenditure (EE). The HR index equation expresses METs in relation to relative HR i.e. the ratio of activity HR to resting HR. The use of this equation provides a simple utility for objective measurement of EE. Methods: During a typical occupational work period of approximately 8 hours HR data was recorded using a Polar RS 400 wrist monitor. Recorded data was downloaded to a Windows PC and non HR data was stripped from the ASCII file using ‘Notepad’. The HR data was exported to a spread sheet program and sorted by HR range into a histogram format. Three HRs were determined, namely a resting HR (the HR delimiting the lowest 30 minutes of recorded data), a mean HR and a peak HR (the HR delimiting the highest 30 minutes of recorded data). HR indices were calculated (mean index equals mean HR/rest HR and peak index equals peak HR/rest HR) with mean and peak indices being converted to METs using the HR index equation. Conclusion: Inexpensive HR recording devices can be utilized to make reasonable estimates of occupational (or recreational) EE suitable for large scale demographic screening by utilizing the HR index equation. The intrinsic value of the HR index equation is that it is independent of factors that influence absolute HR, namely fitness, smoking and beta-blockade.Keywords: energy expenditure, heart rate histograms, heart rate index, occupational activity
Procedia PDF Downloads 2912864 A Unified Deep Framework for Joint 3d Pose Estimation and Action Recognition from a Single Color Camera
Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin
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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.Keywords: human action recognition, pose estimation, D-CNN, deep learning
Procedia PDF Downloads 1382863 Urban Big Data: An Experimental Approach to Building-Value Estimation Using Web-Based Data
Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin
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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.Keywords: apartment complex, big data, life-cycle building value analysis, machine learning
Procedia PDF Downloads 3682862 Bayesian Estimation under Different Loss Functions Using Gamma Prior for the Case of Exponential Distribution
Authors: Md. Rashidul Hasan, Atikur Rahman Baizid
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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function
Procedia PDF Downloads 3802861 Performance Improvement of Electric Vehicle Using K - Map Constructed Rule Based Energy Management Strategy for Battery/Ultracapacitor Hybrid Energy Storage System
Authors: Jyothi P. Phatak, L. Venkatesha, C. S. Raviprasad
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The performance improvement of Hybrid Energy Storage System (HESS) in Electric Vehicle (EV) has been in discussion over the last decade. The important issues in terms of performance parameters addressed are, range of vehicle and battery (BA) peak current. Published literature has either addressed battery peak current reduction or range improvement in EV. Both the issues have not been specifically discussed and analyzed. This paper deals with both range improvement in EV and battery peak current reduction by applying a new Karnaugh Map (K-Map) constructed rule based energy management strategy to proposed HESS. The strategy allows Ultracapacitor (UC) to assist battery when the vehicle accelerates there by reducing the burden on battery. Simulation is carried out for various operating modes of EV considering both urban and highway driving conditions. Simulation is done for different values of UC by keeping battery rating constant for each driving cycle and results are presented. Feasible value of UC is selected based on simulation results. The results of proposed HESS show an improvement in performance parameters compared to Battery only Energy Storage System (BESS). Battery life is improved to considerable extent and there is an overall development in the performance of electric vehicle.Keywords: electric vehicle, PID controller, energy management strategy, range, battery current, ultracapacitor
Procedia PDF Downloads 1112860 A Comprehensive Study of a Hybrid System Integrated Solid Oxide Fuel cell, Gas Turbine, Organic Rankine Cycle with Compressed air Energy Storage
Authors: Taiheng Zhang, Hongbin Zhao
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Compressed air energy storage become increasingly vital for solving intermittency problem of some renewable energies. In this study, a new hybrid system on a combination of compressed air energy storage (CAES), solid oxide fuel cell (SOFC), gas turbine (GT), and organic Rankine cycle (ORC) is proposed. In the new system, excess electricity during off-peak time is utilized to compress air. Then, the compressed air is stored in compressed air storage tank. During peak time, the compressed air enters the cathode of SOFC directly instead of combustion chamber of traditional CAES. There is no air compressor consumption of SOFC-GT in peak demand, so SOFC- GT can generate power with high-efficiency. In addition, the waste heat of exhaust from GT is recovered by applying an ORC. Three different organic working fluid (R123, R601, R601a) of ORC are chosen to evaluate system performance. Based on Aspen plus and Engineering Equation Solver (EES) software, energy and exergoeconomic analysis are used to access the viability of the combined system. Besides, the effect of two parameters (fuel flow and ORC turbine inlet pressure) on energy efficiency is studied. The effect of low-price electricity at off-peak hours on thermodynamic criteria (total unit exergy cost of products and total cost rate) is also investigated. Furthermore, for three different organic working fluids, the results of round-trip efficiency, exergy efficiency, and exergoeconomic factors are calculated and compared. Based on thermodynamic performance and exergoeconomic performance of different organic working fluids, the best suitable working fluid will be chosen. In conclusion, this study can provide important guidance for system efficiency improvement and viability.Keywords: CAES, SOFC, ORC, energy and exergoeconomic analysis, organic working fluids
Procedia PDF Downloads 1162859 Quantitative Analysis of Caffeine in Pharmaceutical Formulations Using a Cost-Effective Electrochemical Sensor
Authors: Y. T. Gebreslassie, Abrha Tadesse, R. C. Saini, Rishi Pal
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Caffeine, known chemically as 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione, is a naturally occurring alkaloid classified as an N-methyl derivative of xanthine. Given its widespread use in coffee and other caffeine-containing products, it is the most commonly consumed psychoactive substance in everyday human life. This research aimed to develop a cost-effective, sensitive, and easily manufacturable sensor for the detection of caffeine. Antraquinone-modified carbon paste electrode (AQMCPE) was fabricated, and the electrochemical behavior of caffeine on this electrode was investigated using cyclic voltammetry (CV) and square wave voltammetry (SWV) in a solution of 0.1M perchloric acid at pH 0.56. The modified electrode displayed enhanced electrocatalytic activity towards caffeine oxidation, exhibiting a two-fold increase in peak current and an 82 mV shift of the peak potential in the negative direction compared to an unmodified carbon paste electrode (UMCPE). Exploiting the electrocatalytic properties of the modified electrode, SWV was employed for the quantitative determination of caffeine. Under optimized experimental conditions, a linear relationship between peak current and concentration was observed within the range of 2.0 x 10⁻⁶ to 1.0× 10⁻⁴ M, with a correlation coefficient of 0.998 and a detection limit of 1.47× 10⁻⁷ M (signal-to-noise ratio = 3). Finally, the proposed method was successfully applied to the quantitative analysis of caffeine in pharmaceutical formulations, yielding recovery percentages ranging from 95.27% to 106.75%.Keywords: antraquinone-modified carbon paste electrode, caffeine, detection, electrochemical sensor, quantitative analysis
Procedia PDF Downloads 512858 Improvement of Direct Torque and Flux Control of Dual Stator Induction Motor Drive Using Intelligent Techniques
Authors: Kouzi Katia
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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table
Procedia PDF Downloads 3362857 Feasibility Conditions for Wind and Hydraulic Energy Coupling
Authors: Antonin Jolly, Bertrand Aubry, Corentin Michel, Rebecca Freva
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Wind energy depends on wind strength and varies largely in time. When it is above the demand, it generates a loss while in the opposite case; energy needs are not fully satisfied. To overcome this problem specific to irregular energies, the process of pumped-storage hydroelectricity (PSH) is studied in present paper. A combination of wind turbine and pumped storage system is more predictable and is more compliant to provide electricity supply according to daily demand. PSH system is already used in several countries to accumulate electricity by pumping water during off-peak times into a storage reservoir, and to use it during peak times to produce energy. Present work discusses a feasibility study on size and financial productivity of PSH system actuated with wind turbines specific power.Keywords: wind turbine, hydroelectricity, energy storage, pumped-storage hydroelectricity
Procedia PDF Downloads 3692856 Robustified Asymmetric Logistic Regression Model for Global Fish Stock Assessment
Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa
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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score
Procedia PDF Downloads 2602855 Design and Performance Analysis of Advanced B-Spline Algorithm for Image Resolution Enhancement
Authors: M. Z. Kurian, M. V. Chidananda Murthy, H. S. Guruprasad
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An approach to super-resolve the low-resolution (LR) image is presented in this paper which is very useful in multimedia communication, medical image enhancement and satellite image enhancement to have a clear view of the information in the image. The proposed Advanced B-Spline method generates a high-resolution (HR) image from single LR image and tries to retain the higher frequency components such as edges in the image. This method uses B-Spline technique and Crispening. This work is evaluated qualitatively and quantitatively using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The method is also suitable for real-time applications. Different combinations of decimation and super-resolution algorithms in the presence of different noise and noise factors are tested.Keywords: advanced b-spline, image super-resolution, mean square error (MSE), peak signal to noise ratio (PSNR), resolution down converter
Procedia PDF Downloads 3942854 Vulnerability Risk Assessment of Non-Engineered Houses Based on Damage Data of the 2009 Padang Earthquake 2009 in Padang City, Indonesia
Authors: Rusnardi Rahmat Putra, Junji Kiyono, Aiko Furukawa
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Several powerful earthquakes have struck Padang during recent years, one of the largest of which was an M 7.6 event that occurred on September 30, 2009 and caused more than 1000 casualties. Following the event, we conducted a 12-site microtremor array investigation to gain a representative determination of the soil condition of subsurface structures in Padang. From the dispersion curve of array observations, the central business district of Padang corresponds to relatively soft soil condition with Vs30 less than 400 m/s. because only one accelerometer existed, we simulated the 2009 Padang earthquake to obtain peak ground acceleration for all sites in Padang city. By considering the damage data of the 2009 Padang earthquake, we produced seismic risk vulnerability estimation of non-engineered houses for rock, medium and soft soil condition. We estimated the loss ratio based on the ground response, seismic hazard of Padang and the existing damaged to non-engineered structure houses due to Padang earthquake in 2009 data for several return periods of earthquake events.Keywords: profile, Padang earthquake, microtremor array, seismic vulnerability
Procedia PDF Downloads 4012853 Further Development in Predicting Post-Earthquake Fire Ignition Hazard
Authors: Pegah Farshadmanesh, Jamshid Mohammadi, Mehdi Modares
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In nearly all earthquakes of the past century that resulted in moderate to significant damage, the occurrence of postearthquake fire ignition (PEFI) has imposed a serious hazard and caused severe damage, especially in urban areas. In order to reduce the loss of life and property caused by post-earthquake fires, there is a crucial need for predictive models to estimate the PEFI risk. The parameters affecting PEFI risk can be categorized as: 1) factors influencing fire ignition in normal (non-earthquake) condition, including floor area, building category, ignitability, type of appliance, and prevention devices, and 2) earthquake related factors contributing to the PEFI risk, including building vulnerability and earthquake characteristics such as intensity, peak ground acceleration, and peak ground velocity. State-of-the-art statistical PEFI risk models are solely based on limited available earthquake data, and therefore they cannot predict the PEFI risk for areas with insufficient earthquake records since such records are needed in estimating the PEFI model parameters. In this paper, the correlation between normal condition ignition risk, peak ground acceleration, and PEFI risk is examined in an effort to offer a means for predicting post-earthquake ignition events. An illustrative example is presented to demonstrate how such correlation can be employed in a seismic area to predict PEFI hazard.Keywords: fire risk, post-earthquake fire ignition (PEFI), risk management, seismicity
Procedia PDF Downloads 3752852 New Analytical Current-Voltage Model for GaN-based Resonant Tunneling Diodes
Authors: Zhuang Guo
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In the field of GaN-based resonant tunneling diodes (RTDs) simulations, the traditional Tsu-Esaki formalism failed to predict the values of peak currents and peak voltages in the simulated current-voltage(J-V) characteristics. The main reason is that due to the strong internal polarization fields, two-dimensional electron gas(2DEG) accumulates at emitters, resulting in 2D-2D resonant tunneling currents, which become the dominant parts of the total J-V characteristics. By comparison, based on the 3D-2D resonant tunneling mechanism, the traditional Tsu-Esaki formalism cannot predict the J-V characteristics correctly. To overcome this shortcoming, we develop a new analytical model for the 2D-2D resonant tunneling currents generated in GaN-based RTDs. Compared with Tsu-Esaki formalism, the new model has made the following modifications: Firstly, considering the Heisenberg uncertainty, the new model corrects the expression of the density of states around the 2DEG eigenenergy levels at emitters so that it could predict the half width at half-maximum(HWHM) of resonant tunneling currents; Secondly, taking into account the effect of bias on wave vectors on the collectors, the new model modifies the expression of the transmission coefficients which could help to get the values of peak currents closer to the experiment data compared with Tsu-Esaki formalism. The new analytical model successfully predicts the J-V characteristics of GaN-based RTDs, and it also reveals more detailed mechanisms of resonant tunneling happened in GaN-based RTDs, which helps to design and fabricate high-performance GaN RTDs.Keywords: GaN-based resonant tunneling diodes, tsu-esaki formalism, 2D-2D resonant tunneling, heisenberg uncertainty
Procedia PDF Downloads 722851 Carbohydrate Intake Estimation in Type I Diabetic Patients Described by UVA/Padova Model
Authors: David A. Padilla, Rodolfo Villamizar
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In recent years, closed loop control strategies have been developed in order to establish a healthy glucose profile in type 1 diabetic mellitus (T1DM) patients. However, the controller itself is unable to define a suitable reference trajectory for glucose. In this paper, a control strategy Is proposed where the shape of the reference trajectory is generated bases in the amount of carbohydrates present during the digestive process, due to the effect of carbohydrate intake. Since there no exists a sensor to measure the amount of carbohydrates consumed, an estimator is proposed. Thus this paper presents the entire process of designing a carbohydrate estimator, which allows estimate disturbance for a predictive controller (MPC) in a T1MD patient, the estimation will be used to establish a profile of reference and improve the response of the controller by providing the estimated information of ingested carbohydrates. The dynamics of the diabetic model used are due to the equations described by the UVA/Padova model of the T1DMS simulator, the system was developed and simulated in Simulink, taking into account the noise and limitations of the glucose control system actuators.Keywords: estimation, glucose control, predictive controller, MPC, UVA/Padova
Procedia PDF Downloads 2582850 Estimation of Missing Values in Aggregate Level Spatial Data
Authors: Amitha Puranik, V. S. Binu, Seena Biju
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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis
Procedia PDF Downloads 3712849 Relevancy Measures of Errors in Displacements of Finite Elements Analysis Results
Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif
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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence
Procedia PDF Downloads 489