Search results for: nested factor model

Authors: Wasila Lawan Gadanya

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This paper examines the problems of learning English vowel pronunciation. The objective is to identify some of the factors that affect the learning of English vowel sounds and their proper realization in words. The theoretical framework adopted is based on both error analysis and contrastive analysis. The data collection instruments used in the study are questionnaire and word list for the respondents (students) and observation of some of their lecturers. All the data collected were analyzed using simple percentage. The findings show that it is not a single factor that affects the learning of English vowel pronunciation rather many factors concurrently do so. Among the factors examined, it has been found that lack of correlation between English orthography and its pronunciation, not mother-tongue (which most people consider as a factor affecting learning of the pronunciation of a second language), has the greatest influence on students’ learning and realization of English vowel sounds since the respondents in this study are from different ethnic groups of Nigeria and thus speak different languages but having the same or almost the same problem when pronouncing the English vowel sounds.

Keywords: English vowels, learning, Nigeria, pronunciation

Procedia PDF Downloads 451

Authors: Victor N. Anisimov, Igor E. Serov, Ksenia M. Kolkova, Natalia V. Galkina

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The problem of memorability of the advertising content is closely connected with the key issues of neuromarketing. The memorability of the advertising content contributes to the marketing effectiveness of the promoted product. Significant directions of studying the phenomenon of memorability are the memorability of the brand (detected through the memorability of the logo) and the memorability of the product offer (detected through the memorization of dynamic audiovisual advertising content - commercial). The aim of this work is to reveal the predictors of memorization of static and dynamic audiovisual stimuli (logos and commercials). An important direction of the research was revealing differences in psychophysiological correlates of memorability between static and dynamic audiovisual stimuli. We assumed that static and dynamic images are perceived in different ways and may have a difference in the memorization process. Objective methods of recording psychophysiological parameters while watching static and dynamic audiovisual materials are well suited to achieve the aim. The electroencephalography (EEG) method was performed with the aim of identifying correlates of the memorability of various stimuli in the electrical activity of the cerebral cortex. All stimuli (in the groups of statics and dynamics separately) were divided into 2 groups – remembered and not remembered based on the results of the questioning method. The questionnaires were filled out by survey participants after viewing the stimuli not immediately, but after a time interval (for detecting stimuli recorded through long-term memorization). Using statistical method, we developed the classifier (statistical model) that predicts which group (remembered or not remembered) stimuli gets, based on psychophysiological perception. The result of the statistical model was compared with the results of the questionnaire. Conclusions: Predictors of the memorability of static and dynamic stimuli have been identified, which allows prediction of which stimuli will have a higher probability of remembering. Further developments of this study will be the creation of stimulus memory model with the possibility of recognizing the stimulus as previously seen or new. Thus, in the process of remembering the stimulus, it is planned to take into account the stimulus recognition factor, which is one of the most important tasks for neuromarketing.

Keywords: memory, commercials, neuromarketing, EEG, branding

Procedia PDF Downloads 251

Authors: A. Benmerkhi, A. Bounouioua, M. Bouchemat, T. Bouchemat

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In this paper, we present a numerical optical properties of a triangular periodic lattice of elliptical air holes. We report the influence of the ratio (semi-major axis length of elliptical hole to the filling ratio) on the photonic band gap. Then by using the finite difference time domain (FDTD) algorithm, the resonant wavelength of the point defect microcavities in a two-dimensional photonic crystal (PC) shifts towards the low wavelengths with significantly increased filing ratio. It can be noted that the Q factor is gradually changed to higher when the filling ratio increases. It is due to an increase in reflectivity of the PC mirror. Also we theoretically investigate the H1 cavity, where the value of semi-major axis (Rx) of the six holes surrounding the cavity are fixed at 0.5a and the Rx of the two edge air holes are fixed at the optimum value of 0.52a. The highest Q factor of 4.1359 × 10⁶ is achieved at the resonant mode located at λ = 1.4970 µm.

Keywords: photonic crystal, microcavity, filling ratio, elliptical holes

Procedia PDF Downloads 137

Authors: T. Watanabe

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Combustion analysis of suspended sodium droplet is performed by solving numerically the Navier-Stokes equations and the energy conservation equations. The combustion model consists of the pre-ignition and post-ignition models. The reaction rate for the pre-ignition model is based on the chemical kinetics, while that for the post-ignition model is based on the mass transfer rate of oxygen. The calculated droplet temperature is shown to be in good agreement with the existing experimental data. The temperature field in and around the droplet is obtained as well as the droplet shape variation, and the present numerical model is confirmed to be effective for the combustion analysis.

Keywords: analysis, combustion, droplet, sodium

Procedia PDF Downloads 211

Authors: Manjare Chandraprabha A., S. D. Shirbahadurkar, Manjare Anil S., Paithne Ajay N.

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This paper proposes Intonation Modeling for Prosody generation in Neutral speech for Marathi (language spoken in Maharashtra, India) story telling applications. Nowadays audio story telling devices are very eminent for children. In this paper, we proposed tilt model for stressed words in Marathi for speech modification. Tilt model predicts modification in tone of neutral speech. GMM is used to identify stressed words for modification.

Keywords: tilt model, fundamental frequency, statistical parametric speech synthesis, GMM

Procedia PDF Downloads 392

Authors: Sarra Roubi, Mohammed Erramdani, Samir Mbarki

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This paper presents an approach for the model-driven generating of Rich Internet Application (RIA) focusing on the graphical aspect. We used well known Model-Driven Engineering (MDE) frameworks and technologies, such as Eclipse Modeling Framework (EMF), Graphical Modeling Framework (GMF), Query View Transformation (QVTo) and Acceleo to enable the design and the code automatic generation of the RIA. During the development of the approach, we focused on the graphical aspect of the application in terms of interfaces while opting for the Model View Presenter pattern that is designed for graphics interfaces. The paper describes the process followed to define the approach, the supporting tool and presents the results from a case study.

Keywords: metamodel, model-driven engineering, MVP, rich internet application, transformation, user interface

Procedia PDF Downloads 353

Authors: Johannes J. Schneider, Mathias S. Weyland, Peter Eggenberger Hotz, William D. Jamieson, Oliver Castell, Alessia Faggian, Rudolf M. Füchslin

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In the description of the origin of life, there are still some open gaps, e.g., the formation of macromolecules cannot be fully explained so far. The Kauffman model proposes the existence of autocatalytic sets of macromolecules which mutually catalyze reactions leading to each other’s formation. Usually, this model is simulated in one well-stirred pot only, with a continuous inflow of small building blocks, from which larger molecules are created by a set of catalyzed ligation and cleavage reactions. This approach represents the picture of the primordial soup. However, the conditions on the early Earth must have differed geographically, leading to spatially different outcomes whether a specific reaction could be performed or not. Guided by this picture, the Kauffman model is simulated in a large number of containers in parallel, with neighboring containers being connected by diffusion. In each container, only a subset of the overall reaction set can be performed. Under specific conditions, this approach leads to a larger probability for the existence of an autocatalytic metabolism than in the original Kauffman model.

Keywords: agglomeration, autocatalytic set, differential equation, Kauffman model

Procedia PDF Downloads 58

Authors: Madhat Abu Al-Naeem, Ismail Yusoff, Ng Tham Fatt, Yatimah Alias

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Groundwater in Gaza strip is increasingly being exposed to anthropic and natural factors that seriously impacted the groundwater quality. Physiochemical data of groundwater can offer important information on changes in groundwater quality that can be useful in improving water management tactics. An integrative hydrochemical and statistical techniques (Hierarchical cluster analysis (HCA) and factor analysis (FA)) have been applied on the existence ten physiochemical data of 84 samples collected in (2000/2001) using STATA, AquaChem, and Surfer softwares to: 1) Provide valuable insight into the salinization sources and the hydrochemical processes controlling the chemistry of groundwater. 2) Differentiate the influence of natural processes and man-made activities. The recorded large diversity in water facies with dominance Na-Cl type that reveals a highly saline aquifer impacted by multiple complex hydrochemical processes. Based on WHO standards, only (15.5%) of the wells were suitable for drinking. HCA yielded three clusters. Cluster 1 is the highest in salinity, mainly due to the impact of Eocene saline water invasion mixed with human inputs. Cluster 2 is the lowest in salinity also due to Eocene saline water invasion but mixed with recent rainfall recharge and limited carbonate dissolution and nitrate pollution. Cluster 3 is similar in salinity to Cluster 2, but with a high diversity of facies due to the impact of many sources of salinity as sea water invasion, carbonate dissolution and human inputs. Factor analysis yielded two factors accounting for 88% of the total variance. Factor 1 (59%) is a salinization factor demonstrating the mixing contribution of natural saline water with human inputs. Factor 2 measure the hardness and pollution which explained 29% of the total variance. The negative relationship between the NO3- and pH may reveal a denitrification process in a heavy polluted aquifer recharged by a limited oxygenated rainfall. Multivariate statistical analysis combined with hydrochemical analysis indicate that the main factors controlling groundwater chemistry were Eocene saline invasion, seawater invasion, sewage invasion and rainfall recharge and the main hydrochemical processes were base ion and reverse ion exchange processes with clay minerals (water rock interactions), nitrification, carbonate dissolution and a limited denitrification process.

Keywords: dendrogram and cluster analysis, water facies, Eocene saline invasion and sea water invasion, nitrification and denitrification

Procedia PDF Downloads 365

Authors: Seon Soon Choi

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The essential purpose is to present the good fatigue crack propagation model describing a stochastic fatigue crack growth behavior in a rolled magnesium alloy, AZ31, under various load ratio conditions. Fatigue crack propagation experiments were carried out in laboratory air under four conditions of load ratio, R, using AZ31 to investigate the crack growth behavior. The stochastic fatigue crack growth behavior was analyzed using an interpolation of random variable, Z, introduced to an empirical fatigue crack propagation model. The empirical fatigue models used in this study are Paris-Erdogan model, Walker model, Forman model, and modified Forman model. It was found that the random variable is useful in describing the stochastic fatigue crack growth behaviors under various load ratio conditions. The good probabilistic model describing a stochastic fatigue crack growth behavior under various load ratio conditions was also proposed.

Keywords: magnesium alloys, fatigue crack propagation model, load ratio, interpolation of random variable

Procedia PDF Downloads 410

Authors: Nokwethaba Makhanya, Babatunde J. Abiodun, Piotr Wolski

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Climate change possibly intensifies hydrological droughts and reduces water availability in river basins. Despite this, most research on climate change effects in southern Africa has focused exclusively on meteorological droughts. This thesis projects the potential impact of climate change on the future characteristics of hydrological droughts in the Limpopo River Basin (LRB). The study uses regional climate model (RCM) measurements (from the Coordinated Regional Climate Downscaling Experiment, CORDEX) and a combination of hydrological simulations (using the Soil and Water Assessment Tool Plus model, SWAT+) to predict the impacts at four global warming levels (GWLs: 1.5℃, 2.0℃, 2.5℃, and 3.0℃) under the RCP8.5 future climate scenario. The SWAT+ model was calibrated and validated with a streamflow dataset observed over the basin, and the sensitivity of model parameters was investigated. The performance of the SWAT+LRB model was verified using the Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) have been used to detect meteorological droughts. The Soil Water Index (SSI) has been used to define agricultural drought, while the Water Yield Drought Index (WYLDI), the Surface Run-off Index (SRI), and the Streamflow Index (SFI) have been used to characterise hydrological drought. The performance of the SWAT+ model simulations over LRB is sensitive to the parameters CN2 (initial SCS runoff curve number for moisture condition II) and ESCO (soil evaporation compensation factor). The best simulation generally performed better during the calibration period than the validation period. In calibration and validation periods, NSE is ≤ 0.8, while PBIAS is ≥ ﹣80.3%, RMSE ≥ 11.2 m³/s, and R² ≤ 0.9. The simulations project a future increase in temperature and potential evapotranspiration over the basin, but they do not project a significant future trend in precipitation and hydrological variables. However, the spatial distribution of precipitation reveals a projected increase in precipitation in the southern part of the basin and a decline in the northern part of the basin, with the region of reduced precipitation projected to increase with GWLs. A decrease in all hydrological variables is projected over most parts of the basin, especially over the eastern part of the basin. The simulations predict meteorological droughts (i.e., SPEI and SPI), agricultural droughts (i.e., SSI), and hydrological droughts (i.e., WYLDI, SRI) would become more intense and severe across the basin. SPEI-drought has a greater magnitude of increase than SPI-drought, and agricultural and hydrological droughts have a magnitude of increase between the two. As a result, this research suggests that future hydrological droughts over the LRB could be more severe than the SPI-drought projection predicts but less severe than the SPEI-drought projection. This research can be used to mitigate the effects of potential climate change on basin hydrological drought.

Keywords: climate change, CORDEX, drought, hydrological modelling, Limpopo River Basin

Procedia PDF Downloads 128

Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: Tudor Barbu

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We present a robust nonlinear parabolic partial differential equation (PDE)-based denoising scheme in this article. Our approach is based on a second-order anisotropic diffusion model that is described first. Then, a consistent and explicit numerical approximation algorithm is constructed for this continuous model by using the finite-difference method. Finally, our restoration experiments and method comparison, which prove the effectiveness of this proposed technique, are discussed in this paper.

Keywords: anisotropic diffusion, finite differences, image denoising and restoration, nonlinear PDE model, anisotropic diffusion, numerical approximation schemes

Procedia PDF Downloads 313

Authors: Mark Berry

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A number of studies have identified several factors involved in the malignant progression of cancer cells. The Primary modulator in driving inflammation to these transformed cells has been identified as the transcription factor known as nuclear factor-κB. This essential regulator of inflammation and the development of cancer, combined with a microenvironment of inflammation and signaling molecules, plays a major role in the malignant progression of cancer, and this progression is the result of the mutagenic predisposition of persistent substances that combat infection at tumor sites and other areas of chronic inflammation. Inflammation-induced tumors, and their inflammatory cells and regulators may be the primary source of metastasis of tumor cells through angiogenesis. Previous research on cytokines and chemokines, including their downstream targets, has been the focus of the cancer/inflammation connection. The identification of the biological mechanisms of other proteins vital to the inflammation cascade and their interactions are crucial to novel and effective therapeutic protocols for the treatment of inflammation-induced cancers. The Ancelim HSRP Protocol is just such a therapeutic intervention.

Keywords: ancelim, cancer, inflammation, tumor

Procedia PDF Downloads 545

Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

Procedia PDF Downloads 74

Authors: Shahroz Khan, Osman Taha Şen

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In automotive brake systems, brake noise has been a major problem, and brake squeal is one of the critical ones which is an instability issue. The brake squeal produces an audible sound at high frequency that is irritating to the human ear. To study this critical problem, first a nonlinear mathematical model with three degree of freedom is developed. This model consists of a point mass that simulates the brake pad and a sliding surface that simulates the brake rotor. The model exposes kinematic and clearance nonlinearities, but no friction nonlinearity. In the formulation, the friction coefficient is assumed to be constant and the friction force does not change direction. The nonlinear governing equations of the model are first obtained, and numerical solutions are sought for different cases. Second, a computational model for the squeal problem is developed with a commercial software, and computational solutions are obtained with two different types of contact cases (solid-to-solid and sphere-to-plane). This model consists of three rigid bodies and several elastic elements that simulate the key characteristics of a brake system. The response obtained from this model is compared with numerical solutions in time and frequency domain.

Keywords: contact force, nonlinearities, brake squeal, vehicle brake

Procedia PDF Downloads 246

Authors: Jerome T. Tolentino, Ma. Victoria Rejuso, Jara Kaye Villanueva, Loureal Camille Inocencio, Ma. Rosario Concepcion O. Ang

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Wind energy is rapidly emerging as the primary source of electricity in the Philippines, although developing an accurate wind resource model is difficult. In this study, Weather Research and Forecasting (WRF) Model, an open source mesoscale Numerical Weather Prediction (NWP) model, was used to produce a 1-year atmospheric simulation with 4 km resolution on the Ilocos Region of the Philippines. The WRF output (netCDF) extracts the annual mean wind speed data using a Python-based Graphical User Interface. Lastly, wind resource assessment was produced using a GIS software. Results of the study showed that it is more flexible to use Python scripts than using other post-processing tools in dealing with netCDF files. Using WRF Model, Python, and Geographic Information Systems, a reliable wind resource map is produced.

Keywords: wind resource assessment, weather research and forecasting (WRF) model, python, GIS software

Procedia PDF Downloads 442

Authors: M. Mikušová

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The main aim of this paper is to present a clear and comprehensive picture of the process of a crisis in the organization which will help to better understand its possible developments. For a description of the sequence of individual steps and an indication of their causation and possible variants of the developments, a detailed flow diagram with verbal comment is applied. For simplicity, the process of the crisis is observed in four basic phases called: symptoms of the crisis, diagnosis, action and prevention. The model highlights the complexity of the phenomenon of the crisis and that the various phases of the crisis are interweaving.

Keywords: crisis, management, model, organization

Procedia PDF Downloads 291

Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

Procedia PDF Downloads 198

Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas Weyh

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The tendency to use the Battery-Electric vehicle (BEV) for the low and medium driving range or even high driving range has been growing more and more. As a result, higher safety, reliability, and durability of the battery pack as a component of electric vehicles, which has a great share of cost and weight of the final product, are the topics to be considered and investigated. Battery aging can be considered as the predominant factor regarding the reliability and durability of BEV. To better understand the aging process, offline battery characterization has been widely used, which is time-consuming and needs very expensive infrastructures. This paper presents the substitute method for the conventional battery characterization methods, which is based on battery Modular Multilevel Management (BM3). According to this Topology, the battery cells can be drained and charged concerning their capacity, which allows varying battery pack structures. Due to the integration of the power electronics, the output voltage of the battery pack is no longer fixed but can be dynamically adjusted in small steps. In other words, each cell can have three different states, namely series, parallel, and bypass in connection with the neighbor cells. With the help of MATLAB/Simulink and by using the BM3 modules, the battery string model is created. This model allows us to switch two cells with the different SoC as parallel, which results in the internal balancing of the cells. But if the parallel switching lasts just for a couple of ms, we can have a perturbation pulse which can stimulate the cells out of the relaxation phase. With the help of modeling the voltage response pulse of the battery, it would be possible to characterize the cell. The Online EIS method, which is discussed in this paper, can be a robust substitute for the conventional battery characterization methods.

Keywords: battery characterization, SoH estimation, RLS, BEV

Procedia PDF Downloads 149

Authors: Zouhair Issa Ahmed, Ahmed Abdulrasool Ahmed, Falah Hassan Abdulsada

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This paper presents a layout of a factory using Pro-Model simulation by choosing the best layout that gives the highest productivity and least work in process. The general problem is to find the best sequence in which jobs pass between the machines which are compatible with the technological constraints and optimal with respect to some performance criteria. The best simulation with Pro-Model program increased productivity and reduced work in process by balancing lines of production compared with the current layout of factory when productivity increased from 45 products to 180 products through 720 hours.

Keywords: scheduling, Pro-Model, simulation, balancing lines of production, layout planning, WIP

Procedia PDF Downloads 636

Authors: Saurabh Ahluwalia, Sul Kassicieh

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The conventional wisdom has maintained that being in proximity to entrepreneurial ecosystems helps startups to raise financing, develop and grow. In this paper, we examine the effect of a major component of an entrepreneurial ecosystem- financial or venture capital clusters on the exit of a startup through mergers and acquisitions (M&A). We find that the presence of a venture capitalist in a venture capital (VC) cluster is a major success factor for M&A exits. The location of startups in the top VC clusters did not turn out to be significant for success. Our results are robust to different specifications of the model that use different time periods, types of success, the reputation of VC, industry and the quality of the startup company. Our results provide evidence for VCs, startups and policymakers who want to better understand the components of entrepreneurial ecosystems and their relation to the M&A exits of startups.

Keywords: financial institution, mergers and acquisitions, startup financing, venture capital

Procedia PDF Downloads 201

Authors: Hui-Chieh Li

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Circular economy promotes greater resource productivity and avoids pollution through greater recycling and re-use which bring benefits for both the environment and the economy. The concept is contrast to a linear economy which is ‘take, make, dispose’ model of production. A well-design reverse logistics service strategy could enhance the willingness of recycling of the users and reduce the related logistics cost as well as carbon emissions. Moreover, the recycle brings the manufacturers most advantages as it targets components for closed-loop reuse, essentially converting materials and components from worn-out product into inputs for new ones at right time and right place. This study considers demand-supply interaction, time-dependent recycle demand, time-dependent surplus value of recycled product and constructs models on recycle service strategy for the recyclable waste collector. A crucial factor in optimizing a recycle service strategy is consumer demand. The study considers the relationships between consumer demand towards recycle and product characteristics, surplus value and user behavior. The study proposes a recycle service strategy which differs significantly from the conventional and typical uniform service strategy. Periods with considerable demand and large surplus product value suggest frequent and short service cycle. The study explores how to determine a recycle service strategy for recyclable waste collector in terms of service cycle frequency and duration and vehicle type for all service cycles by considering surplus value of recycled product, time-dependent demand, transportation economies and demand-supply interaction. The recyclable waste collector is responsible for the collection of waste product for the manufacturer. The study also examines the impacts of utilization rate on the cost and profit in the context of different sizes of vehicles. The model applies mathematical programming methods and attempts to maximize the total profit of the distributor during the study period. This study applies the binary logit model, analytical model and mathematical programming methods to the problem. The model specifically explores how to determine a recycle service strategy for the recycler by considering product surplus value, time-dependent recycle demand, transportation economies and demand-supply interaction. The model applies mathematical programming methods and attempts to minimize the total logistics cost of the recycler and maximize the recycle benefits of the manufacturer during the study period. The study relaxes the constant demand assumption and examines how service strategy affects consumer demand towards waste recycling. Results of the study not only help understanding how the user demand for recycle service and product surplus value affects the logistics cost and manufacturer’s benefits, but also provide guidance such as award bonus and carbon emission regulations for the government.

Keywords: circular economy, consumer demand, product surplus value, recycle service strategy

Procedia PDF Downloads 392

Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

Procedia PDF Downloads 76

Authors: Pranjal Johri, Misbah Ul-Islam

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Power Transformers are one of the most critical and failure prone entities in an electrical power system. It is an established practice that each design of a power transformer has to undergo numerous type tests for design validation and routine tests are performed on each and every power transformer before dispatch from manufacturer’s works. Different countries follow different standards for testing the transformers. Most common and widely followed standard for Power Transformers is IEC 60076 series. Though these standards put up a strict testing requirements for power transformers, however, few aspects of transformer characteristics and guaranteed parameters can be ensured by some additional tests. Based on certain observations during routine test of a transformer and analyzing the data of a large fleet of transformers, three propositions have been discussed and put forward to be included in test schedules and standards. The observations in the routine test raised questions on design flux density of transformer. In order to ensure that flux density in any part of the core & yoke does not exceed 1.9 tesla at 1.1 pu as well, following propositions need to be followed during testing:  From the data studied, it was evident that generally NLC at 1.1 pu is apporx. 3 times of No Load Current at 1 pu voltage.  During testing the power factor at 1.1 pu excitation, it must be comparable to calculated values from the Cold Rolled Grain Oriented steel material curves, including building factor.  A limit of 3 % to be extended for higher than rated voltages on difference in Vavg and Vrms, during no load testing.  Extended over excitation test to be done in case above propositions are observed to be violated during testing.

Keywords: power transfoemrs, no load current, DGA, power factor

Procedia PDF Downloads 104

Authors: I. Behroyan, P. Ganesan, S. He, S. Sivasankaran

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This study compares the predictions of five types of Computational Fluid Dynamics (CFD) models, including two single-phase models (i.e. Newtonian and non-Newtonian) and three two-phase models (Eulerian-Eulerian, mixture and Eulerian-Lagrangian), to investigate turbulent forced convection of Cu-water nanofluid in a tube with a constant heat flux on the tube wall. The Reynolds (Re) number of the flow is between 10,000 and 25,000, while the volume fraction of Cu particles used is in the range of 0 to 2%. The commercial CFD package of ANSYS-Fluent is used. The results from the CFD models are compared with results from experimental investigations from literature. According to the results of this study, non-Newtonian single-phase model, in general, does not show a good agreement with Xuan and Li correlation in prediction of Nu number. Eulerian-Eulerian model gives inaccurate results expect for φ=0.5%. Mixture model gives a maximum error of 15%. Newtonian single-phase model and Eulerian-Lagrangian model, in overall, are the recommended models. This work can be used as a reference for selecting an appreciate model for future investigation. The study also gives a proper insight about the important factors such as Brownian motion, fluid behavior parameters and effective nanoparticle conductivity which should be considered or changed by the each model.

Keywords: heat transfer, nanofluid, single-phase models, two-phase models

Procedia PDF Downloads 484

Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

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For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

Procedia PDF Downloads 212

Authors: L. Slimani, A. Bousri, A. Hamadouche, H. Ben Hamed

Abstract:

The aim of this experimental and numerical study is to analyze the effects of acoustic streaming generated by 40 kHz ultrasonic waves on heat transfer in forced convection, with and without 40 PPI aluminum metal foam. Preliminary dynamic and thermal studies were done with COMSOL Multiphase, to see heat transfer enhancement degree by inserting a 40PPI metal foam (10 × 2 × 3 cm) on a heat sink, after having determined experimentally its permeability and Forchheimer's coefficient. The results obtained numerically are in accordance with those obtained experimentally, with an enhancement factor of 205% for a velocity of 0.4 m/s compared to an empty channel. The influence of 40 kHz ultrasound on heat transfer was also tested with and without metallic foam. Results show a remarkable increase in Nusselt number in an empty channel with an enhancement factor of 37,5%, while no influence of ultrasound on heat transfer in metal foam presence.

Keywords: acoustic streaming, enhancing heat transfer, laminar flow, metal foam, ultrasound

Procedia PDF Downloads 138

Authors: Hossein Hassani, Ali Rezaei

Abstract:

In recent years, the study of soil contamination problems surrounding mines and smelting plants has attracted some serious attention of the environmental experts. These elements due to the non- chemical disintegration and nature are counted as environmental stable and durable contaminants. Variability of these contaminants in the soil and the time and financial limitation for the favorable environmental application, in order to reduce the risk of their irreparable negative consequences on environment, caused to apply the favorable grading of these contaminant for the further success of the risk management processes. In this study, we use the contaminants factor risk indices, average concentration, enrichment factor and geoaccumulation indices for evaluating the metal contaminant of including Pb, Ni, Se, Mo and Zn in the soil of Sarcheshmeh copper mine area. For this purpose, 120 surface soil samples up to the depth of 30 cm have been provided from the study area. And the metals have been analyzed using ICP-MS method. Comparison of the heavy and potentially toxic elements concentration in the soil samples with the world average value of the uncontaminated soil and shale average indicates that the value of Zn, Pb, Ni, Se and Mo is higher than the world average value and only the Ni element shows the lower value than the shale average. Expert opinions on the relative importance of each indicators were used to assign a final weighting of the metals and the heavy metals were ranked using the TOPSIS approach. This allows us to carry out efficient environmental proceedings, leading to the reduction of environmental ricks form the contaminants. According to the results, Ni, Pb, Mo, Zn, and Se have the highest rate of risk contamination in the soil samples of the study area.

Keywords: contamination coefficient, geoaccumulation factor, TOPSIS techniques, Sarcheshmeh copper complex

Procedia PDF Downloads 274

Authors: Pompa Marcello, Mauro Capocelli, Vincenzo Piemonte

Abstract:

This work focuses on a mathematical model able to describe the gastro-intestinal physiology and providing a rational tool for the design of an artificial gastro-intestinal system. This latter is mainly devoted to analyse the absorption and bioavailability of drugs and nutrients through in vitro tests in order to overcome (or, at least, to partially replace) in vivo trials. The provided model realizes a conjunction ring (with extended prediction capability) between in vivo tests and mechanical-laboratory models emulating the human body. On this basis, no empirical equations controlling the gastric emptying are implemented in this model as frequent in the cited literature and all the sub-unit and the related system of equations are physiologically based. More in detail, the model structure consists of six compartments (stomach, duodenum, jejunum, ileum, colon and blood) interconnected through pipes and valves. Paracetamol, Ketoprofen, Irbesartan and Ketoconazole are considered and analysed in this work as reference drugs. The mathematical model has been validated against in vivo literature data. Results obtained show a very good model reliability and highlight the possibility to realize tailored simulations for different couples patient-drug, including food adsorption dynamics.

Keywords: gastro-intestinal model, drugs bioavailability, paracetamol, ketoprofen

Procedia PDF Downloads 168

Authors: Yilei Song, Linlin Tian, Ning Zhao

Abstract:

Turbulence energetics and structures in the wake of large-scale wind turbines under the stably-stratified atmospheric boundary layer (SABL) can be complicated due to the presence of low-level jets (LLJs), a region of higher wind speeds than the geostrophic wind speed. With a modified one-k-equation, eddy viscosity model specified for atmospheric flows as the sub-grid scale (SGS) model, a realistic atmospheric state of the stable ABL is well reproduced by large-eddy simulation (LES) techniques. Corresponding to the precursor stably stratification, the detailed wake properties of a standard 5-MW wind turbine represented as an actuator line model are provided. An engineering model is proposed for wake prediction based on the simulation statistics and gets validated. Results confirm that the proposed wake model can provide good predictions for wind turbines under the SABL.

Keywords: large-eddy simulation, stably-stratified atmospheric boundary layer, wake model, wind turbine wake

Procedia PDF Downloads 174