Search results for: reduce order aeroelastic model (ROAM)

Authors: Nicolò Vaiana, Filip C. Filippou, Giorgio Serino

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The solution of the nonlinear dynamic equilibrium equations of base-isolated structures adopting a conventional monolithic solution approach, i.e. an implicit single-step time integration method employed with an iteration procedure, and the use of existing nonlinear analytical models, such as differential equation models, to simulate the dynamic behavior of seismic isolators can require a significant computational effort. In order to reduce numerical computations, a partitioned solution method and a one dimensional nonlinear analytical model are presented in this paper. A partitioned solution approach can be easily applied to base-isolated structures in which the base isolation system is much more flexible than the superstructure. Thus, in this work, the explicit conditionally stable central difference method is used to evaluate the base isolation system nonlinear response and the implicit unconditionally stable Newmark’s constant average acceleration method is adopted to predict the superstructure linear response with the benefit in avoiding iterations in each time step of a nonlinear dynamic analysis. The proposed mathematical model is able to simulate the dynamic behavior of seismic isolators without requiring the solution of a nonlinear differential equation, as in the case of widely used differential equation model. The proposed mixed explicit-implicit time integration method and nonlinear exponential model are adopted to analyze a three dimensional seismically isolated structure with a lead rubber bearing system subjected to earthquake excitation. The numerical results show the good accuracy and the significant computational efficiency of the proposed solution approach and analytical model compared to the conventional solution method and mathematical model adopted in this work. Furthermore, the low stiffness value of the base isolation system with lead rubber bearings allows to have a critical time step considerably larger than the imposed ground acceleration time step, thus avoiding stability problems in the proposed mixed method.

Keywords: base-isolated structures, earthquake engineering, mixed time integration, nonlinear exponential model

Procedia PDF Downloads 281

Authors: Yves Lacroix, Van Van Than, Didier Léandri, Pierre Liardet

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The western Tombolo of the Giens peninsula in southern France, known as Almanarre beach, is subject to coastal erosion. We are trying to use computer simulation in order to propose solutions to stop this erosion. Our aim was first to determine the main factors for this erosion and successfully apply a coupled hydro-sedimentological numerical model based on observations and measurements that have been performed on the site for decades. We have gathered all available information and data about waves, winds, currents, tides, bathymetry, coastal line, and sediments concerning the site. These have been divided into two sets: one devoted to calibrating a numerical model using Mike 21 software, the other to serve as a reference in order to numerically compare the present situation to what it could be if we implemented different types of underwater constructions. This paper presents the first part of the study: selecting and melting different sources into a coherent data basis, identifying the main erosion factors, and calibrating the coupled software model against the selected reference period. Our results bring calibration of the numerical model with good fitting coefficients. They also show that the winter South-Western storm events conjugated to depressive weather conditions constitute a major factor of erosion, mainly due to wave impact in the northern part of the Almanarre beach. Together, current and wind impact is shown negligible.

Keywords: Almanarre beach, coastal erosion, hydro-sedimentological, numerical model

Procedia PDF Downloads 377

Authors: Ghaleb A. Husseini, Salma E. Ahmed, Hesham G. Moussa, Ana M. Martins, Mohammad Al-Sayah, Nasser Qaddoumi

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This abstract aims to investigate the use of targeted liposomes as anticancer drug carriers in vitro in combination with ultrasound applied as drug trigger; in order to reduce the side effects caused by traditional chemotherapy. Pegylated liposomes were used to encapsulate calcein and then release this model drug when 20-kHz, 40-kHz, 1-MHz and 3-MHz ultrasound were applied at different acoustic power densities. Fluorescence techniques were then used to measure the percent drug release of calcein from these targeted liposomes. Results showed that as the power density increases, at the four frequencies studied, the release of calcein also increased. Based on these results, we believe that ultrasound can be used to increase the rate and amount of chemotherapeutics release from liposomes.

Keywords: liposomes, calcein release, high frequency ultrasound, low frequency ultrasound, fluorescence techniques

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Authors: Vijay Kumar Kolagani, Yingcai Xiao

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Adaptation of Natural User Interface (NUI) has been slow and limited. NUI devices like Microsoft’s Kinect and Ultraleap’s Leap Motion can only interact with a handful applications that were specifically designed and implemented for them. A NUI device just can’t be used to directly control millions of applications that are not designed to take NUI input. This is in the similar situation like the adaptation of color TVs. At the early days of color TV, the broadcasting format was in RGB, which was not viewable by blackand-white TVs. TV broadcasters were reluctant to produce color programs due to limited viewership. TV viewers were reluctant to buy color TVs because there were limited programs to watch. Color TV’s breakthrough moment came after the adaptation of NTSC standard which allowed color broadcasts to be compatible with the millions of existing black-and-white TVs. This research presents a framework to use NUI devices to control existing non-NUI applications without reprogramming them. The methodology is to create an adapter to convert input from NUI devices into input compatible with that generated by CLI (Command Line Input) and GUI (Graphical User Interface) devices. The CLI/GUI compatible input is then sent to the active application through the operating system just like any input from a CLI/GUI device to control the non-NUI program that the user is controlling. A sample adapter has been created to convert input from Kinect to keyboard strokes, so one can use the input from Kinect to control any applications that take keyboard input, such as Microsoft’s PowerPoint. When the users use the adapter to control their PowerPoint presentations, they can free themselves from standing behind a computer to use its keyboard and can roam around in front of the audience to use hand gestures to control the PowerPoint. It is hopeful such adapters can accelerate the adaptation of NUI devices.

Keywords: command line input, graphical user interface, human computer interaction, natural user interface, NUI adapter

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Authors: Mahdy ‎Esmailian, Mahdi ‎Doostparast, Ahmad ‎Parsian

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‎In this article‎, ‎sequential order statistics (SOS) censoring type II samples coming from the generalized Pareto distribution are considered‎. ‎Maximum likelihood (ML) estimators of the unknown parameters are derived on the basis of the available multiple SOS data‎. ‎Necessary conditions for existence and uniqueness of the derived ML estimates are given‎. Due to complexity in the proposed likelihood function‎, ‎a useful re-parametrization is suggested‎. ‎For illustrative purposes‎, ‎a Monte Carlo simulation study is conducted and an illustrative example is analysed‎.

Keywords: bayesian estimation‎, generalized pareto distribution‎, ‎maximum likelihood estimation‎, sequential order statistics

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Authors: F. Moghtaderi, M. Burke, J. Troelsen

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There has been a significant decline in active travel as well as the massive increase use of car-dependent travel mode in many countries during past two decades. Evidential risks for people’s physical and mental health problems are followed by this increased use of motorized travel mode. These problems range from overweight and obesity to increasing air pollution. In response to these rising concerns, local councils and other interested organizations around the world have introduced a variety of initiatives regarding reduce the dominance of cars for the daily journeys. However, the nature of these kinds of interventions, which related to the human behavior, make lots of complexities. People’s travel behavior and changing this behavior, has two different aspects. People’s attitudes and perceptions toward the sustainable and healthy modes of travel, and motorized travel modes (especially private car use) is one these two aspects. The other one related to people’s behavior change processes. There are no comprehensive model in order to guide policy interventions to increase the level of succeed of such interventions. A comprehensive theoretical framework is required in accordance to facilitate and guide the processes of data collection and analysis to achieve the best possible guidelines for policy makers. Regarding this gaps in the travel behavior change research, this paper attempted to identify and suggest a multidimensional framework in order to facilitate planning interventions. A structured mixed-method is suggested regarding the expand the scope and improve the analytic power of the result according to the complexity of human behavior. In order to recognize people’s attitudes, a theory with the focus on people’s attitudes towards a particular travel behavior was needed. The literature around the theory of planned behavior (TPB) was the most useful, and had been proven to be a good predictor of behavior change. Another aspect of the research, related to the people’s decision-making process regarding explore guidelines for the further interventions. Therefore, a theory was needed to facilitate and direct the interventions’ design. The concept of the transtheoretical model of behavior change (TTM) was used regarding reach a set of useful guidelines for the further interventions with the aim to increase active travel and sustainable modes of travel. Consequently, a combination of these two theories (TTM and TPB) had presented as an appropriate concept to identify and design implemented travel behavior change interventions.

Keywords: behavior change theories, theoretical framework, travel behavior change interventions, urban research

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Authors: Huang Bingling, Jia Yuhong, Liu Yanhui

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As an important part of aircraft, landing gears are responsible for taking-off and landing function. In recent years, big airplane's structural quality increases a lot. As a result, landing gears have stricter technical requirements than ever before such as structure strength and etc. If the structural strength of the landing gear is enhanced through traditional methods like increasing structural quality, the negative impacts on the landing gear's function would be very serious and even counteract the positive effects. Thus, in order to solve this problem, the impact of pre-rotating of landing gears on performance of landing gears is studied from the theoretical and experimental verification in this paper. By increasing the pre-rotating speed of the wheel, it can improve the performance of the landing gear and reduce the structural quality, the force of joint parts and other properties. In addition, the pre-rotating of the wheels also has other advantages, such as reduce the friction between wheels and ground and extend the life of the wheel. In this paper, the impact of the pre-rotating speed on landing gears and the connecting between landing gears performance and pre-rotating speed would be researched in detail. This paper is divided into three parts. In the first part, large airplane landing gear model is built by CATIA and LMS. As most general landing gear type in big plane, four-wheel landing gear is picked as model. The second part is to simulate the process of landing in LMS motion, and study the impact of pre-rotating of wheels on the aircraft`s properties, including the buffer stroke, efficiency, power; friction, displacement and relative speed between piston and sleeve; force and load distribution of tires. The simulation results show that the characteristics of the different pre-rotation speed are understood. The third part is conclusion. Through the data of the previous simulation and the relationship between the pre-rotation speed of the aircraft wheels and the performance of the aircraft, recommended speed interval is proposed. This paper is of great theoretical value to improve the performance of large airplane. It is a very effective method to improve the performance of aircraft by setting wheel pre-rotating speed. Do not need to increase the structural quality too much, eliminating the negative effects of traditional methods.

Keywords: large airplane, landing gear, pre-rotating, simulation

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Authors: Sadegh Abebi

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There are problems with estimating time of product process of products, especially when there is variable serving time, like control stage. These problems will cause overestimation of process time. Layout constraints, reworking constraints and inflexible product schedule in multi product lines, needs a precise planning to reduce volume in particular situation of line stock. In this article, by analyzing real queue systems with layout constraints and by using concepts and principles of Markov chain in queue theory, a hybrid model has been presented. This model can be a base to assess queue systems with probable parameters of service. Here by presenting a case study, the proposed model will be described. so, production lines of a home application manufacturer will be analyzed.

Keywords: Queuing theory, Markov Chain, layout, line balance

Procedia PDF Downloads 626

Authors: Khanyisile Mnguni, Muthukrishnavellaisamy Kumarasamy, Jeff C. Smithers

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Umgeni Water is a water board that supplies most parts of KwaZulu Natal with bulk portable water. Currently, Umgeni Water is running its distribution system based on required reservoir levels and demands and does not consider the energy cost at different times of the day, number of pump switches, and background leakages. Including these constraints can reduce operational cost, energy usage, leakages, and increase performance. Optimising pump schedules can reduce energy usage and costs while adhering to hydraulic and operational constraints. Umgeni Water has installed an online hydraulic software, WaterNet Advisor, that allows running different operational scenarios prior to implementation in order to optimise the distribution system. This study will investigate operation scenarios using optimisation techniques and WaterNet Advisor for a local water distribution system. Based on studies reported in the literature, introducing pump scheduling optimisation can reduce energy usage by approximately 30% without any change in infrastructure. Including tariff structures in an optimisation problem can reduce pumping costs by 15%, while including leakages decreases cost by 10%, and pressure drop in the system can be up to 12 m. Genetical optimisation algorithms are widely used due to their ability to solve nonlinear, non-convex, and mixed-integer problems. Other methods such as branch and bound linear programming have also been successfully used. A suitable optimisation method will be chosen based on its efficiency. The objective of the study is to reduce energy usage, operational cost, and leakages, and the feasibility of optimal solution will be checked using the Waternet Advisor. This study will provide an overview of the optimisation of hydraulic networks and progress made to date in multi-objective optimisation for a selected sub-system operated by Umgeni Water.

Keywords: energy usage, pump scheduling, WaterNet Advisor, leakages

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Nader Khalafian, Mohsen Ghaderi

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Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

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Authors: Madina Hamiane

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Modelling and simulation of an analogy function generator is presented based on a polynomial expansion model. The proposed function generator model is based on a 10th order polynomial approximation of any of the required functions. The polynomial approximations of these functions can then be implemented using basic CMOS circuit blocks. In this paper, a circuit model is proposed that can simultaneously generate many different mathematical functions. The circuit model is designed and simulated with HSPICE and its performance is demonstrated through the simulation of a number of non-linear functions.

Keywords: modelling and simulation, analog function generator, polynomial approximation, CMOS transistors

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Maha Bakkari, R.Tadili

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The present work is a study of the performance of a solar copper coil heating system in a greenhouse microclimate. Our system is based on the circulation of a Heat transfer fluid, which is water in our case, in a closed loop under the greenhouse's roof in order to store heat all day, and then this heat will supply the greenhouse during the night. In order to evaluate our greenhouse, we made an experimental study in two identical greenhouses, where the first one is equipped with a heating system and the second (without heating) is used for control. The heating system allows the establishment of the thermal balance and determines the mass of water necessary for the process in order to ensure its functioning during the night. The results obtained showed that this solar heating system and the climatic parameters inside the experimental greenhouse were improved, and it presents a significant gain compared to a controlled greenhouse without a heating system. This research is one of the solutions that help to reduce the greenhouse effect of the planet Earth, a problem that worries the world.

Keywords: solar energy, energy storage, greenhouse, environment

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Authors: Abeer S. Elsherbiny, Ali H. Gemeay

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In this study, graphene oxide (GO) nanosheets have been synthesized and characterized using different spectroscopic tools such as X-ray diffraction spectroscopy, infrared Fourier transform (FT-IR) spectroscopy, BET specific surface area and Transmission Electronic Microscope (TEM). The prepared GO was investigated for the removal of malachite green, a cationic dye from aqueous solution. The removal methods of malachite green has been proceeded via adsorption process. GO nanosheets can be predicted as a good adsorbent material for the adsorption of cationic species. The adsorption of the malachite green onto the GO nanosheets has been carried out at different experimental conditions such as adsorption kinetics, concentration of adsorbate, pH, and temperature. The kinetics of the adsorption data were analyzed using four kinetic models such as the pseudo first-order model, pseudo second-order model, intraparticle diffusion, and the Boyd model to understand the adsorption behavior of malachite green onto the GO nanosheets and the mechanism of adsorption. The adsorption isotherm of adsorption of the malachite green onto the GO nanosheets has been investigated at 25, 35 and 45 °C. The equilibrium data were fitted well to the Langmuir model. Various thermodynamic parameters such as the Gibbs free energy (ΔG°), enthalpy (ΔH°), and entropy (ΔS°) change were also evaluated. The interaction of malachite green onto the GO nanosheets has been investigated by infrared Fourier transform (FT-IR) spectroscopy.

Keywords: adsorption, graphene oxide, kinetics, malachite green

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: Khaled Sellami, Lynda Sellami, Pierre F. Tiako

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Ubiquitous computing helps make data and services available to users anytime and anywhere. This makes the cooperation of devices a crucial need. In return, such cooperation causes an overload of the devices and/or networks, resulting in network malfunction and suspension of its activities. Our goal in this paper is to propose an approach of devices reconfiguration in order to help to reduce the energy consumption in ubiquitous environments. The idea is that when high-energy consumption is detected, we proceed to a change in component distribution on the devices to reduce and/or balance the energy consumption. We also investigate the possibility to detect high-energy consumption of devices/network based on devices abilities. As a result, our idea realizes a reconfiguration of devices aimed at reducing the consumption of energy and/or load balancing in ubiquitous environments.

Keywords: ubiquitous computing, load balancing, device energy consumption, reconfiguration

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Authors: Alokananda Nath

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Land is an important issue anywhere in the world as it is one of the fundamental elements in human settlements. Since the urban areas are considered to be the drivers of economy for any country across the world and the phenomenon of ‘urbanization’ happening everywhere, there is always a greater pressure on urban land and its management. Many states in India have realized the importance of land as a valuable resource and have implemented certain framework for managing and developing land. But in West Bengal no such statutory framework has been formulated till now and a very out dated model of land acquisition for public purpose is practiced. Due to the lop-sided character of urban growth in the entire eastern region of India, the city of Kolkata continues to bear the burden of excessive growth of population and consequent urbanization of the adjoining areas at a rapid pace. This research tries to look into these conflicts with respect to the present pattern of development in the context of Kolkata and suggest a system for land management in order to implement the planning processes. For this purpose, five case study areas were taken up within the Kolkata Metropolitan Area and subsequent analysis of their present land management and development techniques was done. The findings reveal that there is a lack of political will as well as administrative inefficiency on part of both the development authority and the local bodies. Mostly the local bodies lack the financial resources and technical expertise to work out any kind of land management framework or work out any kind of model in order to manage the development that is happening. All these place undue strain on city infrastructure systems and reduce the potential of cities to contribute as engines of economic growth. The focus of reforms, therefore, ought to be on streamlining the urban planning process, judicious and optimal land use, efficient plan implementation mechanisms, improvement of titling and registration processes.

Keywords: urbanization, land management framework, land development, policy reforms, land-use planning processes

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Authors: B. Güney, Ç. Teke

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In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

Keywords: food industry, fuzzy linear programming, fuzzy logic, linear programming

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Authors: Rachel Baruch

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This article describes the implementation of an experimental model for teaching ICT tools and digital environments in teachers training college. In most educational systems in the Western world, new programs were developed in order to bridge the digital gap between teachers and students. In spite of their achievements, these programs are limited due to several factors: The teachers in the schools implement new methods incorporating technological tools into the curriculum, but meanwhile the technology changes and advances. The interface of tools changes frequently, some tools disappear and new ones are invented. These conditions require an experimental model of training the pre-service teachers. The appropriate method for instruction within the domain of ICT tools should be based on exposing the learners to innovations, helping them to gain experience, teaching them how to deal with challenges and difficulties on their own, and training them. This study suggests some principles for this approach and describes step by step the implementation of this model.

Keywords: ICT tools, e-learning, pre-service teachers, new model

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

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Authors: Ksenija Dumičić, Anita Čeh Časni, Berislav Žmuk

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The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates.

Keywords: European Union countries, exponential smoothing methods, forecast accuracy unemployment rate

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Authors: George N. Zaimes, Valasia Iakovoglou, Paschalis Koutalakis, Konstantinos Ioannou, Ioannis Kosmadakis, Panagiotis Tsardaklis, Theodoros Laopoulos

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The advancements in technology allow the development of a new system that can continuously measure surface soil erosion. Continuous soil erosion measurements are required in order to comprehend the erosional processes and propose effective and efficient conservation measures to mitigate surface erosion. Mitigating soil erosion, especially in Mediterranean countries such as Greece, is essential in order to maintain environmental and agricultural sustainability. In this paper, we present the Automated Soil Erosion Monitoring System (ASEMS) that measures surface soil erosion along with other factors that impact erosional process. Specifically, this system measures ground level changes (surface soil erosion), rainfall, air temperature, soil temperature and soil moisture. Another important innovation is that the data will be collected by remote communication. In addition, stakeholder’s awareness is a key factor to help reduce any environmental problem. The different dissemination activities that were utilized are described. The overall outcomes were the development of an innovative system that can measure erosion very accurately. These data from the system help study the process of erosion and find the best possible methods to reduce erosion. The dissemination activities enhance the stakeholder's and public's awareness on surface soil erosion problems and will lead to the adoption of more effective soil erosion conservation practices in Greece.

Keywords: soil management, climate change, new technologies, conservation practices

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Authors: Youn-Sung Kim, Jin-Ho Jo, Mi-Sung Kim, Jae-Kun Lee

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Accelerated life cycle test is applied to various products or components in order to reduce the time of life cycle test in industry. It must be considered for many test conditions according to the product characteristics for the test and the selection of acceleration parameter is especially very important. We have carried out the general life cycle test and the accelerated life cycle test by applying the acceleration factor (AF) considering the characteristics of brushless DC (BLDC) motor for washing machine. The final purpose of this study is to verify the validity by analyzing the results of the general life cycle test and the accelerated life cycle test. It will make it possible to reduce the life test time through the reasonable accelerated life cycle test.

Keywords: accelerated life cycle test, reliability test, motor for washing machine, brushless dc motor test

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Authors: Lie-Fen Lin, Tzu-Hsuan Lin, Ching-Heng Lin

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Hospice care for terminally ill patients provides not only a better quality of life but also cost-saving benefits. However, the utilization of home-based hospice care (HBH care) remains low even for countries covered by National Health Insurance (NHI) programs in Taiwan. In the current commercial insurance policy, only hospital-based hospice benefits were covered. It may have an influence on the insureds chosen to receive end-of-life care in a hospitalized manner. Thus, how to propose a feasible method to advocate HBH care utilization rate of public health policies is an important issue. A total of 130,219 cancer decedents in the year 2011-2013 from the National Health Insurance Research Database (NHIRD) in Taiwan were included in this study. By adding a day volume pays benefits of HBH care as a commercial insurance rider, will provide alternative benefits for the insureds. A multiple-state Markov chain model was incorporated to estimate the transition intensities of patients in different states at the end of their lives (Non-hospice, HBH, hospital-based hospice), and the premiums were estimated. HBH care insurance benefits provide financial support and reduce the burden of care for patients. The rate-making of this product is very sensitive while the utilization rate is rising, especially for high ages. The proposed HBH care insurance is a feasible way to reduce the financial burden, enhance the care quality and family satisfaction of insureds. Meanwhile, insurance companies can participate in advocating a good medical policy to enhance the social image. In addition, the medical costs of NHI can reduce effectively.

Keywords: home-based hospice care, commercial insurance, Markov chain model, the day volume pays

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Authors: Suparman Suparman

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A white noise in autoregressive (AR) model is often assumed to be normally distributed. In application, the white noise usually do not follows a normal distribution. This paper aims to estimate a parameter of AR model that has a exponential white noise. A Bayesian method is adopted. A prior distribution of the parameter of AR model is selected and then this prior distribution is combined with a likelihood function of data to get a posterior distribution. Based on this posterior distribution, a Bayesian estimator for the parameter of AR model is estimated. Because the order of AR model is considered a parameter, this Bayesian estimator cannot be explicitly calculated. To resolve this problem, a method of reversible jump Markov Chain Monte Carlo (MCMC) is adopted. A result is a estimation of the parameter AR model can be simultaneously calculated.

Keywords: autoregressive (AR) model, exponential white Noise, bayesian, reversible jump Markov Chain Monte Carlo (MCMC)

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Authors: Retius Chifurira

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Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

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Authors: Andras Gyorfi, Andras Torma, Adrienn Buruzs

Abstract:

Mobility has been evolved to a determining field of science. The continuously developing segment involves a variety of affected issues such as public and economic sectors. Beside the changes in mobility the state of environment had also changed in the last period. Alternative mobility as a separate category and the idea of its widespread appliance is such a new field that needs to be studied deeper. Alternative mobility implies finding new types of propulsion, using innovative kinds of power and energy resources, revolutionizing the approach to vehicular control. Including new resources and excluding others has such a complex effect which cannot be unequivocally confirmed by today’s scientific achievements. Changes in specific parameters will most likely reduce the environmental impacts, however, the production of new substances or even their subtraction of the system will cause probably energy deficit as well. The aim of this research is to elaborate the environmental impact matrix of alternative mobility and cognize the factors that are yet unknown, analyse them, look for alternative solutions and conclude all the above in a coherent system. In order to this, we analyse it with a method called ‘the system of systems (SoS) method’ to model the effects and the dynamics of the system. A part of the research process is to examine its impacts on the environment, and to decide whether the newly developed versions of alternative mobility are affecting the environmental state. As a final result, a complex approach will be used which can supplement the current scientific studies. By using the SoS approach, we create a framework of reference containing elements in which we examine the interactions as well. In such a way, a flexible and modular model can be established which supports the prioritizing of effects and the deeper analysis of the complex system.

Keywords: environment, alternative mobility, complex model, element analysis, multidimensional map

Procedia PDF Downloads 327

Authors: Niya Chen, Jennifer Chan

Abstract:

In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum.

Keywords: expectile, CARE Model, CARR Model, quantile, cryptocurrency, Value at Risk

Procedia PDF Downloads 110

Authors: Akinola Ikudayisi, Josiah Adeyemo

Abstract:

The correct estimation of reference evapotranspiration (ETₒ) is required for effective irrigation water resources planning and management. However, there are some variables that must be considered while estimating and modeling ETₒ. This study therefore determines the multivariate analysis of correlated variables involved in the estimation and modeling of ETₒ at Vaalharts irrigation scheme (VIS) in South Africa using Principal Component Analysis (PCA) technique. Weather and meteorological data between 1994 and 2014 were obtained both from South African Weather Service (SAWS) and Agricultural Research Council (ARC) in South Africa for this study. Average monthly data of minimum and maximum temperature (°C), rainfall (mm), relative humidity (%), and wind speed (m/s) were the inputs to the PCA-based model, while ETₒ is the output. PCA technique was adopted to extract the most important information from the dataset and also to analyze the relationship between the five variables and ETₒ. This is to determine the most significant variables affecting ETₒ estimation at VIS. From the model performances, two principal components with a variance of 82.7% were retained after the eigenvector extraction. The results of the two principal components were compared and the model output shows that minimum temperature, maximum temperature and windspeed are the most important variables in ETₒ estimation and modeling at VIS. In order words, ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity are less important and cannot be used to provide enough information about ETₒ estimation at VIS. The outcome of this study has helped to reduce input variable dimensionality from five to the three most significant variables in ETₒ modelling at VIS, South Africa.

Keywords: irrigation, principal component analysis, reference evapotranspiration, Vaalharts

Procedia PDF Downloads 259