Search results for: shared parameter model

Authors: M. Bouferhane, A. Boukhebza, L. Hatab

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In this paper, the position control of linear induction motor using fuzzy sliding mode controller design is proposed. First, the indirect field oriented control LIM is derived. Then, a designed sliding mode control system with an integral-operation switching surface is investigated, in which a simple adaptive algorithm is utilized for generalised soft-switching parameter. Finally, a fuzzy sliding mode controller is derived to compensate the uncertainties which occur in the control, in which the fuzzy logic system is used to dynamically control parameter settings of the SMC control law. The effectiveness of the proposed control scheme is verified by numerical simulation. The experimental results of the proposed scheme have presented good performances compared to the conventional sliding mode controller.

Keywords: linear induction motor, vector control, backstepping, fuzzy-sliding mode control

Procedia PDF Downloads 489

Authors: K. Jithendra Gowd, Ch. Sai Babu, S. Sivanagaraju

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Electric power systems are daily exposed to service interruption mainly due to faults and human accidental interference. Short circuit currents are responsible for several types of disturbances in power systems. The fault currents are high and the voltages are reduced at the time of fault. This paper presents two suitable methods, consideration of fault resistance and Distributed Generator are implemented and analyzed for the enhancement of voltage profile during fault conditions. Fault resistance is a critical parameter of electric power systems operation due to its stochastic nature. If not considered, this parameter may interfere in fault analysis studies and protection scheme efficiency. The effect of Distributed Generator is also considered. The proposed methods are tested on the IEEE 37 bus test systems and the results are compared.

Keywords: distributed generation, electrical distribution systems, fault resistance

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Authors: Rachid Zentar, Mokrane Bala, Pascal Boustingorry

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The prediction of the rheological behavior of concrete is at the center of current concerns of the concrete industry for different reasons. The shortage of good quality standard materials combined with variable properties of available materials imposes to improve existing models to take into account these variations at the design stage of concrete. The main reasons for improving the predictive models are, of course, saving time and cost at the design stage as well as to optimize concrete performances. In this study, we will highlight the different properties of the granular mixtures that affect the rheological properties of concrete. Our objective is to identify the intrinsic parameters of the aggregates which make it possible to predict the yield stress of concrete. The work was done using two typologies of grains: crushed and rolled aggregates. The experimental results have shown that the rheology of concrete is improved by increasing the packing density of the granular mixture using rolled aggregates. The experimental program realized allowed to model the yield stress of concrete by a modified model of Chateau et al. through a dimensionless parameter following Krieger-Dougherty law. The modelling confirms that the yield stress of concrete depends not only on the properties of cement paste but also on the packing density of the granular skeleton and the shape of grains.

Keywords: crushed aggregates, intrinsic viscosity, packing density, rolled aggregates, slump, yield stress of concrete

Procedia PDF Downloads 127

Authors: Donatella Giuliani

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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: Rakesh Kumar, A. K. Ghosh

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The paper presents the modeling of linear and nonlinear longitudinal aerodynamics using real flight data of Hansa-3 aircraft gathered at low and high angles of attack. The Neural-Gauss-Newton (NGN) method has been applied to model the linear and nonlinear longitudinal dynamics and estimate parameters from flight data. Unsteady aerodynamics due to flow separation at high angles of attack near stall has been included in the aerodynamic model using Kirchhoff’s quasi-steady stall model. NGN method is an algorithm that utilizes Feed Forward Neural Network (FFNN) and Gauss-Newton optimization to estimate the parameters and it does not require any a priori postulation of mathematical model or solving of equations of motion. NGN method was validated on real flight data generated at moderate angles of attack before application to the data at high angles of attack. The estimates obtained from compatible flight data using NGN method were validated by comparing with wind tunnel values and the maximum likelihood estimates. Validation was also carried out by comparing the response of measured motion variables with the response generated by using estimates a different control input. Next, NGN method was applied to real flight data generated by executing a well-designed quasi-steady stall maneuver. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics from real flight data at high angles of attack.

Keywords: parameter estimation, NGN method, linear and nonlinear, aerodynamic modeling

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: Muhammad Gillfran Samual, Rinaldy Dalimi, Fauzan Hanif Jufri, Budi Sudiarto, Ismi Rosyiana Fitri

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Solar energy is increasingly recognized as an important future energy source due to its abundant availability and renewable nature. However, the intermittent nature of solar energy can cause fluctuations in the electricity produced, making it difficult to guarantee a stable and reliable electricity supply. One solution that can be implemented is to use batteries in a photovoltaic solar power plant system with a Moving Average control algorithm, which can help smooth and reduce fluctuations in solar power output power. The parameter that can be adjusted in the Moving Average algorithm is the window size or the arithmetic average width of the photovoltaic output power over time. This research evaluates the effect of a change of window size parameter in the Moving Average algorithm on the resulting smoothed photovoltaic output power and the technical effects on batteries, i.e., power and energy usage. Based on the evaluation, it is found that the increase of window size parameter will slow down the response of photovoltaic output power to changes in irradiation and increase the smoothing quality of the intermittent photovoltaic output power. In addition, increasing the window size will reduce the maximum power received on the load side, and the amount of energy used by the battery during the power smoothing process will increase, which, in turn, increases the required battery capacity.

Keywords: battery, intermittent, moving average, photovoltaic, power smoothing

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Authors: Hemant Kumar, Nilendu Das

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A study based on ANN structure gives us the information to predict the size of the future in realizing a past event. ANN, IMD (Indian meteorological department) data and remote sensing were used to enable a number of parameters for calculating the size that may occur in the future. A threshold selected specifically above the high-frequency harvest reached the area during the selected seismic activity. In the field of human and local biodiversity it remains to obtain the right parameter compared to the frequency of impact. But during the study the assumption is that predicting seismic activity is a difficult process, not because of the parameters involved here, which can be analyzed and funded in research activity.

Keywords: ANN, Bayesion class, earthquakes, IMD

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Authors: Kanda Runapongsa Saikaew, Chaluemwut Noyunsan

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Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

Keywords: facebook, social media, credibility measurement, internet

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Authors: Joyraj Chakraborty, Geoffrey Ottoy, Jean-Pierre Goemaere, Lieven De Strycker

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Sensor swarms can be a cost-effectieve and more user-friendly alternative for location based service systems in different application like health-care. To increase the lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we have investigated some parameter for energy model that couples localization accuracy to energy-related sensor parameters such as signal length,Bandwidth and sample frequency. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. we first give an overview of TDOA-based localization together with the primary sources of TDOA error (including reverberation effects, Noise). Then we show that in localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems.

Keywords: sensor swarms, localization, wireless sensor swarms, scalable energy

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Authors: Hichem Boulechfar, Mahfoud Djezzar

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Two-dimensional double diffusive natural convection in an annular elliptical space filled with fluid-saturated porous medium, is analyzed by solving numerically the mass balance, momentum, energy and concentration equations, using Darcy's law and Boussinesq approximation. Both walls delimiting the annular space are maintained at two uniform different temperatures and concentrations. The external parameter considered is the Lewis number. For the present work, the heat and mass transfer for natural convection is studied for the case of aiding buoyancies, where the flow is generated in a cooperative mode by both temperature and solutal gradients. The local Nusselt and Sherwood numbers are presented in term of the external parameter.

Keywords: double diffusive, natural convection, porous media, elliptical annulus

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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

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Authors: Nurudeen Oluwasola Lasisi

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Newcastle disease is a highly contagious disease of birds caused by a para-myxo virus. In this paper, we presented Novel quarantine-adjusted incident and linear incident of Newcastle disease model equations. We considered the dynamics of transmission and control of Newcastle disease. The existence and uniqueness of the solutions were obtained. The existence of disease-free points was shown, and the model threshold parameter was examined using the next-generation operator method. The sensitivity analysis was carried out in order to identify the most sensitive parameters of the disease transmission. This revealed that as parameters β,ω, and ᴧ increase while keeping other parameters constant, the effective reproduction number R_ev increases. This implies that the parameters increase the endemicity of the infection of individuals. More so, when the parameters μ,ε,γ,δ_1, and α increase, while keeping other parameters constant, the effective reproduction number R_ev decreases. This implies the parameters decrease the endemicity of the infection as they have negative indices. Analytical results were numerically verified by the Differential Transformation Method (DTM) and quantitative views of the model equations were showcased. We established that as contact rate (β) increases, the effective reproduction number R_ev increases, as the effectiveness of drug usage increases, the R_ev decreases and as the quarantined individual decreases, the R_ev decreases. The results of the simulations showed that the infected individual increases when the susceptible person approaches zero, also the vaccination individual increases when the infected individual decreases and simultaneously increases the recovery individual.

Keywords: disease-free equilibrium, effective reproduction number, endemicity, Newcastle disease model, numerical, Sensitivity analysis

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Authors: I. Szer, J. Szer, K. Czarnocki, E. Błazik-Borowa

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People on construction scaffoldings work in dynamically changing, often unfavourable climate. Additionally, this kind of work is performed on low stiffness structures at high altitude, which increases the risk of accidents. It is therefore desirable to define the parameters of the work environment that contribute to increasing the construction worker occupational safety level. The aim of this article is to present how changes in microclimate parameters on scaffolding can impact the development of dangerous situations and accidents. For this purpose, indicators based on the human thermal balance were used. However, use of this model under construction conditions is often burdened by significant errors or even impossible to implement due to the lack of precise data. Thus, in the target model, the modified parameter was used – apparent environmental temperature. Apparent temperature in the proposed Scaffold Use Risk Assessment Model has been a perceived outdoor temperature, caused by the combined effects of air temperature, radiative temperature, relative humidity and wind speed (wind chill index, heat index). In the paper, correlations between component factors and apparent temperature for facade scaffolding with a width of 24.5 m and a height of 42.3 m, located at south-west side of building are presented. The distribution of factors on the scaffolding has been used to evaluate fitting of the microclimate model. The results of the studies indicate that observed ranges of apparent temperature on the scaffolds frequently results in a worker’s inability to adapt. This leads to reduced concentration and increased fatigue, adversely affects health, and consequently increases the risk of dangerous situations and accidental injuries

Keywords: apparent temperature, health, safety work, scaffoldings

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Authors: Wael M. Bazzi, Amir Rastegarnia, Azam Khalili

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In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.

Keywords: adaptive filter, distributed estimation, sensor network, IDLMS algorithm

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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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

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

Procedia PDF Downloads 230

Authors: Prakash Singh

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This study focused on the importance of collegiality to improve the effectiveness of a poorly resourced school (PRS). In an effective school that embraces collegiality as its culture, one can expect to find a teaching staff and a management team that shares responsibilities and accountabilities through the development of a common purpose and vision, regardless of whether the school is considered to be poorly resourced or not. Working together in collegial teams is a more effective way to accomplish tasks and to create a climate for effective learning, even for learners in PRSs from poor communities. The main aim of this study was therefore to determine whether collegiality as a leadership strategy could extract the best from people in a PRS, and consequently create the most effective and efficient educational climate possible. The responses received from the teachers and the principal at the PRS supports the notion that collegiality does have a positive influence on learning, as demonstrated by the improved academic achievement of the learners. The teachers were now more involved in the school. They agreed that this was a positive development. Their descriptions of increased involvement, shared accountability and shared decision-making identified important aspects of collegiality that transformed the school from being dysfunctional. Hence, it is abundantly clear that a collegial leadership style can help extract the best from people because the most effective and efficient educational climate can be created at a school when collegiality is employed. Collegial leadership demonstrates that even in PRSs, there are boundless opportunities to improve teaching and learning.

Keywords: collegiality, collegial leadership, effective educational climate, poorly resourced school

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Authors: N. Ben Si Ali, N. Benalia, N. Zerzouri

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Many electronic drivers for the induction motor control are based on sensorless technologies. Speed and torque control is usually attained by application of a speed or position sensor which requires the additional mounting space, reduce the reliability and increase the cost. This paper seeks to analyze dynamical performances and sensitivity to motor parameter changes of reactive power based technique used in sensorless control of induction motors. Validity of theoretical results is verified by simulation.

Keywords: adaptive observers, model reference adaptive system, RP-based estimator, sensorless control, stability analysis

Procedia PDF Downloads 547

Authors: Manabu Sasajima, Takao Yamaguchi, Yoshio Koike, Mitsuharu Watanabe

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This paper discusses the propagation of sound waves in air, specifically in narrow rectangular pathways of an occluded-ear simulator for acoustic measurements. In narrow pathways, both the speed of sound and the phase of the sound waves are affected by the damping of the air viscosity. Herein, we propose a new finite-element method (FEM) that considers the effects of the air viscosity. The method was developed as an extension of existing FEMs for porous, sound-absorbing materials. The results of a numerical calculation for a three-dimensional ear-simulator model using the proposed FEM were validated by comparing with theoretical lumped-parameter modeling analysis and standard values.

Keywords: ear simulator, FEM, simulation, viscosity

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Authors: Khaled R. Khater

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The weight of the SPT hammer is standard (0.623kN). The locally manufacturer drilling rigs use hammers, sometimes deviating off the standard weight. This affects the field measured blow counts (Nf) consequentially, affecting most of correlations previously obtained, as they were obtained based on standard hammer weight. The literature presents energy corrections factor (η2) to be applied to the SPT total input energy. This research investigates the effect of the hammer weight variation, as a single parameter, on the field measured blow counts (Nf). The outcome is a correction factor (ηk), equation, and correction chart. They are recommended to adjust back the measured misleading (Nf) to the standard one as if the standard hammer is used. This correction is very important to be done in such cases where a non-standard hammer is being used because the bore logs in any geotechnical report should contain true and representative values (Nf), let alone the long records of correlations, already in hand. The study here-in is achieved by using laboratory physical model to simulate the SPT dripping hammer mechanism. It is designed to allow different hammer weights to be used. Also, it is manufactured to avoid and eliminate the energy loss sources. This produces a transmitted efficiency up to 100%.

Keywords: correction factors, hammer weight, physical model, standard penetration test

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Authors: Basem Elarbe, Ibrahim Elganidi, Norida Ridzuan, Norhyati Abdullah

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Wax deposition in production pipelines and transportation tubing from offshore to onshore is critical in the oil and gas industry due to low-temperature conditions. It may lead to a reduction in production, shut-in, plugging of pipelines and increased fluid viscosity. The most significant popular approach to solve this issue is by injection of a wax inhibitor into the channel. This research aims to determine the amount of wax deposition of Malaysian crude oil by estimating the effective parameters using (Design-Expert version 7.1.6) by response surface methodology (RSM) method. Important parameters affecting wax deposition such as cold finger temperature, inhibitor concentration and experimental duration were investigated. It can be concluded that SA-co-BA copolymer had a higher capability of reducing wax in different conditions where the minimum point of wax reduction was found at 300 rpm, 14℃, 1h, 1200 ppmThe amount of waxes collected for each parameter were 0.12g. RSM approach was applied using rotatable central composite design (CCD) to minimize the wax deposit amount. The regression model’s variance (ANOVA) results revealed that the R2 value of 0.9906, indicating that the model can be clarified 99.06% of the data variation, and just 0.94% of the total variation were not clarified by the model. Therefore, it indicated that the model is extremely significant, confirming a close agreement between the experimental and the predicted values. In addition, the result has shown that the amount of wax deposit decreased significantly with the increase of temperature and the concentration of poly (stearyl acrylate-co-behenyl acrylate) (SABA), which were set at 14°C and 1200 ppm, respectively. The amount of wax deposit was successfully reduced to the minimum value of 0.01 g after the optimization.

Keywords: wax deposition, SABA inhibitor, RSM, operation factors

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Authors: Wu Sa, Zhang Qian, Li Qi, Wang Ye

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Currently in humidity tests having not put the Dew point temperature as a control parameter, this paper selects wet and dry bulb thermometer to measure the vapor pressure, and introduces several the saturation vapor pressure formulas easily calculated on the controller. Then establish the Dew point temperature calculation model to obtain the relationship between the Dew point temperature and vapor pressure. Finally check through the 100 groups of sample in the range of 0-100 ℃ from "Psychrometric handbook", find that the average error is small. This formula can be applied to calculate the Dew point temperature in the humidity test.

Keywords: dew point temperature, psychrometric handbook, saturation vapor pressure, wet and dry bulb thermometer

Procedia PDF Downloads 489

Authors: Paolo Russu

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This paper investigates the economic and ecological dynamics that emerge in Protected Areas (PAs) due to interactions between visitors and the animals that live there. The PAs contain two species whose interactions are determined by the Lotka-Volterra equations system. Visitors' decisions to visit PAs are influenced by the entrance cost required to enter the park and the chance of witnessing the species living there. Visitors have contradictory effects on the species and thus on the sustainability of the protected areas: on the one hand, an increase in the number of tourists damages the natural habitat of the regions and thus the species living there; on the other hand, it increases the total amount of entrance fees that the managing body of the PAs can use to perform defensive expenditures that protect the species from extinction. For a given set of parameter values, saddle-node bifurcation, Hopf bifurcation, homoclinic orbits, and a Bogdanov–Takens bifurcation of codimension two has been investigated. The system displays periodic doubling and chaotic solutions, as numerical examples demonstrate. Pontryagin's Maximum Principle was utilised to develop an optimal admission charge policy that maximised social gain and ecosystem conservation.

Keywords: chaos, bifurcation points, dynamical model, optimal control

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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

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Timilehin Martins Oyinloye, Won Byong Yoon

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Simulation technology is being adopted in many industries, with research focusing on the development of new ways in which technology becomes embedded within production, services, and society in general. 3D printing (3DP) technology is fast developing in the food industry. However, the limited processability of high-performance material restricts the robustness of the process in some cases. Significantly, the printability of materials becomes the foundation for extrusion-based 3DP, with residual stress being a major challenge in the printing of complex geometry. In many situations, the trial-a-error method is being used to determine the optimum printing condition, which results in time and resource wastage. In this report, the analysis of 3 moisture levels for surimi paste was investigated for an optimum 3DP material and printing conditions by probing its rheology, flow characteristics in the nozzle, and post-deposition process using the finite element method (FEM) model. Rheological tests revealed that surimi pastes with 82% moisture are suitable for 3DP. According to the FEM model, decreasing the nozzle diameter from 1.2 mm to 0.6 mm, increased the die swell from 9.8% to 14.1%. The die swell ratio increased due to an increase in the pressure gradient (1.15107 Pa to 7.80107 Pa) at the nozzle exit. The nozzle diameter influenced the fluid properties, i.e., the shear rate, velocity, and pressure in the flow field, as well as the residual stress and the deformation of the printed sample, according to FEM simulation. The post-printing stability of the model was investigated using the additive layer manufacturing (ALM) model. The ALM simulation revealed that the residual stress and total deformation of the sample were dependent on the nozzle diameter. A small nozzle diameter (0.6 mm) resulted in a greater total deformation (0.023), particularly at the top part of the model, which eventually resulted in the sample collapsing. As the nozzle diameter increased, the accuracy of the model improved until the optimum nozzle size (1.0 mm). Validation with 3D-printed surimi products confirmed that the nozzle diameter was a key parameter affecting the geometry accuracy of 3DP of surimi paste.

Keywords: 3D printing, deformation analysis, die swell, numerical simulation, surimi paste

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Authors: I. Brandys, M. Levy, K. Harush, Y. Haim, M. Korngold

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Theoretical optimization of a copper-water negative inclination heat pipe with internal composite wick structure has been performed, regarding a new introduced parameter: the ratio between the coarse mesh wraps and the fine mesh wraps of the composite wick. Since in many cases, the design of a heat pipe matches specific thermal requirements and physical limitations, this work demonstrates the optimization of a 1 m length, 8 mm internal diameter heat pipe without an adiabatic section, at a negative inclination angle of -10º. The optimization is based on a new introduced parameter, LR: the ratio between the coarse mesh wraps and the fine mesh wraps.

Keywords: heat pipe, inclination, optimization, ratio

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Authors: Mireya Mateo Gomez

Abstract:

The purpose of this study was to examine the perceptions first-generation Mexican origin mothers living in San Diego have on postpartum depression (PPD), with a special focus on social influences and cultural beliefs towards those meanings. This study also aimed to examine possible PPD help-seeking behaviors that first-generation Mexican origin mothers can perform. The Health Belief Model (HBM) and Social Ecological Model (SEM) were the guiding theoretical frameworks for this study. Data for this study were collected from three focus groups, four in-depth interviews, and the distribution of an acculturation survey (ARSMA II). There were a total of 15 participants, in which participant’s mean age was 45, and the mean age migrated to the United States being 22. Most participants identified as being married, born in Southern or Western Mexico, and with a strong Mexican identity in relation to the ARSMA survey. Participants identified four salient PPD perceptions corresponding to the interpersonal level of SEM. These four main perceptions were: 1) PPD affecting the identity of motherhood; 2) PPD being a natural part of a mother’s experience but mitigated by networks; 3) PPD being a U.S. phenomenon due to family and community breakdown; and 4) natural remedies as a preferred PPD treatment. In regard to themes relating to help seeking behaviors, participants identified seven being: 1) seeking help from immediate family members; 2) practicing home remedies; 3) seeking help from a medical professional; 4) obtaining help from a clinic or organization; 5) seeking help from God; 6) participating in PPD support groups; and 7) talking to a friend. It was evident in this study that postpartum depression is not a well discussed topic within the Mexican immigrant population. In relation to the role culture and social influences have on PPD perceptions, most participants shared hearing or learning about PPD from their family members or friends. Participants also stated seeking help from family members if diagnosed with PPD and seeking out home remedies. This study as well provides suggestions to increase the awareness of PPD among the Mexican immigrant community.

Keywords: cultural beliefs, health belief model, Mexican origin mothers, perceptions, postpartum depression social ecological model

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