Search results for: tomato yield prediction

Authors: Mahesh Chudasama, Harit Raval

Abstract:

Conical sections and shells of metal plates manufactured by 3-roller conical bending process are widely used in the industries. The process is completed by first bending the metal plates statically and then dynamic roller bending sequentially. It is required to have an analytical model to get maximum bending force, for optimum design of the machine, for static bending stage. Analytical models assuming various stress conditions are considered and these analytical models are compared considering various parameters and reported in this paper. It is concluded from the study that for higher bottom roller inclination, the shear stress affects greatly to the static bending force whereas for lower bottom roller inclination it can be neglected.

Keywords: roller-bending, static-bending, stress-conditions, analytical-modeling

Procedia PDF Downloads 251

Authors: Vladimir Messerle, Alfred Mosse, Alexandr Ustimenko, Oleg Lavrichshev

Abstract:

Hygiene and sanitary study of typical medical-biological waste made in Kazakhstan, Russia, Belarus and other countries show that their risk to the environment is much higher than that of most chemical wastes. For example, toxicity of solid waste (SW) containing cytotoxic drugs and antibiotics is comparable to toxicity of radioactive waste of high and medium level activity. This report presents the results of the thermodynamic analysis of thermal processing of SW and experiments at the developed plasma unit for SW processing. Thermodynamic calculations showed that the maximum yield of the synthesis gas at plasma gasification of SW in air and steam mediums is achieved at a temperature of 1600K. At the air plasma gasification of SW high-calorific synthesis gas with a concentration of 82.4% (СO – 31.7%, H2 – 50.7%) can be obtained, and at the steam plasma gasification – with a concentration of 94.5% (СO – 33.6%, H2 – 60.9%). Specific heat of combustion of the synthesis gas produced by air gasification amounts to 14267 kJ/kg, while by steam gasification - 19414 kJ/kg. At the optimal temperature (1600 K), the specific power consumption for air gasification of SW constitutes 1.92 kWh/kg, while for steam gasification - 2.44 kWh/kg. Experimental study was carried out in a plasma reactor. This is device of periodic action. The arc plasma torch of 70 kW electric power is used for SW processing. Consumption of SW was 30 kg/h. Flow of plasma-forming air was 12 kg/h. Under the influence of air plasma flame weight average temperature in the chamber reaches 1800 K. Gaseous products are taken out of the reactor into the flue gas cooling unit, and the condensed products accumulate in the slag formation zone. The cooled gaseous products enter the gas purification unit, after which via gas sampling system is supplied to the analyzer. Ventilation system provides a negative pressure in the reactor up to 10 mm of water column. Condensed products of SW processing are removed from the reactor after its stopping. By the results of experiments on SW plasma gasification the reactor operating conditions were determined, the exhaust gas analysis was performed and the residual carbon content in the slag was determined. Gas analysis showed the following composition of the gas at the exit of gas purification unit, (vol.%): СO – 26.5, H2 – 44.6, N2–28.9. The total concentration of the syngas was 71.1%, which agreed well with the thermodynamic calculations. The discrepancy between experiment and calculation by the yield of the target syngas did not exceed 16%. Specific power consumption for SW gasification in the plasma reactor according to the results of experiments amounted to 2.25 kWh/kg of working substance. No harmful impurities were found in both gas and condensed products of SW plasma gasification. Comparison of experimental results and calculations showed good agreement. Acknowledgement—This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.607.21.0118, project RFMEF160715X0118).

Keywords: coal, efficiency, ignition, numerical modeling, plasma-fuel system, plasma generator

Procedia PDF Downloads 250

Authors: İsmail İnce

Abstract:

The aim of this study is to determine index and mechanical properties of pyroclastic rock in a practical way by means of electrical resistivity method. For this purpose, electrical resistivity, uniaxial compressive strength, point load strength, P-wave velocity, density and porosity values of 10 different pyroclastic rocks were measured in the laboratory. A simple regression analysis was made among the index-mechanical properties of the samples compatible with electrical resistivity values. A strong exponentially relation was found between index-mechanical properties and electrical resistivity values. The electrical resistivity method can be used to assess the engineering properties of the rock from which it is difficult to obtain regular shaped samples as a non-destructive method.

Keywords: electrical resistivity, index-mechanical properties, pyroclastic rocks, regression analysis

Procedia PDF Downloads 473

Authors: Naseem Uddin

Abstract:

Impinging jets are used in variety of engineering and industrial applications. This paper is based on numerical simulations of heat transfer by turbulent impinging jet with velocity field excitations using different Reynolds Averaged Navier-Stokes Equations models. Also Detached Eddy Simulations are conducted to investigate the differences in the prediction capabilities of these two simulation approaches. In this paper the excited jet is simulated in non-commercial CFD code OpenFOAM with the goal to understand the influence of dynamics of impinging jet on heat transfer. The jet’s frequencies are altered keeping in view the preferred mode of the jet. The Reynolds number based on mean velocity and diameter is 23,000 and jet’s outlet-to-target wall distance is 2. It is found that heat transfer at the target wall can be influenced by judicious selection of amplitude and frequencies.

Keywords: excitation, impinging jet, natural frequency, turbulence models

Procedia PDF Downloads 273

Authors: Y. Galerkin, A. Rekstin, K. Soldatova

Abstract:

Universal modeling method well proven for industrial compressors was applied for design of the high flow rate supersonic stage. Results were checked by ANSYS CFX and NUMECA Fine Turbo calculations. The impeller appeared to be very effective at transonic flow velocities. Stator elements efficiency is acceptable at design Mach numbers too. Their loss coefficient versus inlet flow angle performances correlates well with Universal modeling prediction. The impeller demonstrated ability of satisfactory operation at design flow rate. Supersonic flow behavior in the impeller inducer at the shroud blade to blade surface Φdes deserves additional study.

Keywords: centrifugal compressor stage, supersonic impeller, inlet flow angle, loss coefficient, return channel, shock wave, vane diffuser

Procedia PDF Downloads 467

Authors: Tayeb Aissaoui, Inas M. AlNashef

Abstract:

In recent years, green solvents named deep eutectic solvents (DESs) have been found to possess significant properties and to be applicable in several technologies. Choline chloride (ChCl) mixed with urea at a ratio of 1:2 and 80 °C was the first discovered DES. In this article, chemical structure and combination mechanism of ChCl: urea based DES were investigated. Moreover, the implementation of this DES in water removal from natural gas was reported. Dehydration of natural gas by ChCl:urea shows significant absorption efficiency compared to triethylene glycol. All above operations were retrieved from COSMOthermX software. This article confirms the potential application of DESs in gas industry.

Keywords: COSMO-RS, deep eutectic solvents, dehydration, natural gas, structure, organic salt

Procedia PDF Downloads 292

Authors: Maryam Amiri, Leyli Mohammad-Khanli

Abstract:

Cloud computing is becoming increasingly popular and more business applications are moving to cloud. In this regard, services that provide similar functional properties are increasing. So, the ability to select a service with the best non-functional properties, corresponding to the user preference, is necessary for the user. This paper presents an Evaluation Framework of Service Trustworthiness (EFST) that evaluates the trustworthiness of equivalent services without need to additional invocations of them. EFST extracts user preference automatically. Then, it assesses trustworthiness of services in two dimensions of qualitative and quantitative metrics based on the experiences of past usage of services. Finally, EFST determines the overall trustworthiness of services using Fuzzy Inference System (FIS). The results of experiments and simulations show that EFST is able to predict the missing values of Quality of Service (QoS) better than other competing approaches. Also, it propels users to select the most appropriate services.

Keywords: user preference, cloud service, trustworthiness, QoS metrics, prediction

Procedia PDF Downloads 287

Authors: Tarek Litim, Ouahiba Taamallah

Abstract:

The present paper is an investigation of the effect of burnishing on the surface integrity of a component made of 15NiCr6 steel. This work shows a statistical study based on regression, and Taguchi's design has allowed the development of mathematical models to predict the output responses as a function of the technological parameters studied. The response surface methodology (RSM) showed a simultaneous influence of the burnishing parameters and observe the optimal processing parameters. ANOVA analysis of the results resulted in the validation of the prediction model with a determination coefficient R=90.60% and 92.41% for roughness and hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=10kgf, i=3passes, and f=0.074mm/rev, which favours minimum roughness and maximum hardness. The result was validated by the desirability of D= (0.99 and 0.95) for roughness and hardness, respectively.

Keywords: 15NiCr6 steel, burnishing, surface integrity, Taguchi, RSM, ANOVA

Procedia PDF Downloads 191

Authors: Tariq Mansoor, Lubos Hes, Vladimir Bajzik

Abstract:

Socks comfort has great significance in our daily life. This significance even increased when we have undergone a work of low or high activity. It causes the sweating of our body with different rates. In this study, plain socks with differential fibre composition were wetted to saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in dry and wet states. Theoretical thermal resistance is predicted by using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fibre) in different models. By this modification, different mathematical models could predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance by different models has reasonable correlation range between (0.84 -0.98) with experimental results in both dry (lab conditions moisture) and wet states. "This work is supported by Technical University of Liberec under SGC-2019. Project number is 21314".

Keywords: thermal resistance, mathematical model, plain socks, moisture loss rate

Procedia PDF Downloads 198

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 127

Authors: Lamzira Ebralidze, Aleksandre Tsertsvadze, Dali Berashvili, Aliosha Bakuridze

Abstract:

Nowadays, there is an increasing demand for biologically active substances from vegetable, animal, and mineral resources. In terms of the use of natural compounds, pharmaceutical, cosmetic, and nutrition industry has big interest. The biggest drawback of conventional extraction methods is the need to use a large volume of organic extragents. The removal of the organic solvent is a multi-stage process. And their absolute removal cannot be achieved, and they still appear in the final product as impurities. A large amount of waste containing organic solvent damages not only human health but also has the harmful effects of the environment. Accordingly, researchers are focused on improving the extraction methods, which aims to minimize the use of organic solvents and energy sources, using alternate solvents and renewable raw materials. In this context, green extraction principles were formed. Green Extraction is a need of today’s environment. Green Extraction is the concept, and it totally corresponds to the challenges of the 21st century. The extraction of biologically active compounds based on green extraction principles is vital from the view of preservation and maintaining biodiversity. Novel technologies of green extraction are known, such as "cold methods" because during the extraction process, the temperature is relatively lower, and it doesn’t have a negative impact on the stability of plant compounds. Novel technologies provide great opportunities to reduce or replace the use of organic toxic solvents, the efficiency of the process, enhance excretion yield, and improve the quality of the final product. The objective of the research is the development of green technologies of flavonoids containing preparations. Methodology: At the first stage of the research, flavonoids containing preparations (Tincture Herba Leonuri, flamine, rutine) were prepared based on conventional extraction methods: maceration, bismaceration, percolation, repercolation. At the same time, the same preparations were prepared based on green technologies, microwave-assisted, UV extraction methods. Product quality characteristics were evaluated by pharmacopeia methods. At the next stage of the research technological - economic characteristics and cost efficiency of products prepared based on conventional and novel technologies were determined. For the extraction of flavonoids, water is used as extragent. Surface-active substances are used as co-solvent in order to reduce surface tension, which significantly increases the solubility of polyphenols in water. Different concentrations of water-glycerol mixture, cyclodextrin, ionic solvent were used for the extraction process. In vitro antioxidant activity will be studied by the spectrophotometric method, using DPPH (2,2-diphenyl-1- picrylhydrazyl) as an antioxidant assay. The advantage of green extraction methods is also the possibility of obtaining higher yield in case of low temperature, limitation extraction process of undesirable compounds. That is especially important for the extraction of thermosensitive compounds and maintaining their stability.

Keywords: extraction, green technologies, natural resources, flavonoids

Procedia PDF Downloads 130

Authors: Hamid Alihosseini, Kamran Dehghani

Abstract:

In this study, the effects of grain size of AA6061 aluminum on the bake hardening have been investigated. The grains of sample sheets refined by applying 4, 8, and 12 passes of ECAP and their microstructures and mechanical properties were investigated. EBSD and TEM studies of the sheets showed grain refinement, and the EBSD micrograph of the alloy ECAPed for 12 passes showed nano-grained (NG) ∼95nm in size. Then, the bake hardenability of processed sheet was compared by pre-straining to 6% followed by baking at 200°C for 20 min. The results show that in case of baking at 200°C, there was an increase about 108%, 93%, and 72% in the bake hardening for 12, 8, and 4 passes, respectively. The maximum in bake hardenability (120 MPa) and final yield stress (583 MPa) were pertaining to the ultra-fine grain specimen pre-strained 6% followed by baking at 200◦C.

Keywords: bake hardening, ultrafine grain, nano grain, AA6061 aluminum,

Procedia PDF Downloads 342

Authors: M. Kubilay Önal

Abstract:

The objective of this study was to determine the adaptabilities of native apricot materials collected from Southeast Anatolia region of Turkey to Aegean Region conditions. Different phenological and pomological characteristics of the cultivars were observed during study. Determination of promising types for adaptation trials were performed employing the 'weighed-ranking' method. To determine them the relative points were given to the characteristics such as yield, average fruit weight, attractiveness, soluble solid, seed ratio by weight and aroma. As a result of two-year evaluation studies on the phenological and pomological characteristics of 22 types, 9 out of them, viz., nos. 2235, 2236, 2237, 2239, 2242, 2244, 2246, 2249, 2257 were selected as promising ones.

Keywords: apricot, phenological characters, pomological characters, weight-ranking method

Procedia PDF Downloads 281

Authors: Damiaa Saad Khudor

Abstract:

The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. It outlines the strategy for the fluidization heat transfer mode and its implementation in industry. The thermal design for fluidized bed cooler is used to furnish a complete design for a fluidized bed cooler of Sodium Bicarbonate. The total thermal load distribution between the air-solid and water-solid along the cooler is calculated according to the thermal equilibrium. The step by step technique was used to accomplish the thermal design of the fluidized bed cooler. It predicts the load, air, solid and water temperature along the trough. The thermal design for fluidized bed cooler revealed to the installation of a heat exchanger consists of (65) horizontal tubes with (33.4) mm diameter and (4) m length inside the bed trough.

Keywords: fluidization, powder technology, thermal design, heat exchangers

Procedia PDF Downloads 513

Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

Abstract:

The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

Procedia PDF Downloads 352

Authors: Roshan Jelal, Charles Mbohwa

Abstract:

In today’s world, internal fraud remains one of the most challenging problems within companies worldwide and despite investment in controls and attention given to the problem, the instances of internal fraud has not abated. To the contrary it appears that internal fraud is on the rise especially in the wake of the economic downturn. Leadership within companies believes that the more sophisticated the controls employed the less likely it would be for employees to pilfer. This is a very antiquated view as investment in controls may not be enough to curtail internal fraud; however, ensuring that a company drives the correct culture and behaviour within the organisation is likely to yield desired results. This research aims to understand how creating a strong ethical culture and embedding the principle of good corporate governance impacts on levels of internal fraud with an organization (a South African Bank).

Keywords: internal fraud, corporate governance, ethics, reserve bank, the King Code

Procedia PDF Downloads 416

Authors: Gao Fangting

Abstract:

With the accelerated pace of industrialization and urbanization, the economic level has been significantly improved, and at the same time, the air quality situation has also become a focus of attention, which not only affects people's health but also has certain impacts on the economy and ecology. As the capital city of China, the air quality situation in Beijing has attracted much attention. In this paper, based on the day-by-day PM2.5, PM10, CO, NO₂, SO₂ and O₃ conditions in Beijing from 2018 to 2022, the characterization of pollutants is launched, and the seasonal decomposition and prediction of the main pollutants, PM2.5, PM10 and O3, are performed in STL. The results of the study show that (1) the overall air quality of Beijing has significantly improved from 2018 to 2022, and the main pollutants are PM2.5, PM10, and O₃; (2) the seasonal intensities of the main pollutants are higher, and they are influenced by seasonal factors; and (3) it is predicted that the O₃ concentration will have a trend of slowly increasing from 2023 to 2026, and the PM10 and PM2.5 pollution situation slowly improves.

Keywords: air pollutants, Beijing, characteristic analysis, STL

Procedia PDF Downloads 21

Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

Abstract:

The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

Procedia PDF Downloads 132

Authors: A. Hocine, A. Ghouaoula, S. M. Medjdoub, M. Cherifi

Abstract:

The present paper proposed a meso–macro model describing the mechanical behaviour composite laminates of staking sequence [+θ/-θ]s under tensil loading. The behaviour of a layer is ex-pressed through elasticity coupled to damage. The elastic strain is due to the elasticity of the layer and can be modeled by using the classical laminate theory, and the laminate is considered as an orthotropic material. This means that no coupling effect between strain and curvature is considered. In the present work, the damage is associated to cracking of the matrix and parallel to the fibers and it being taken into account by the changes in the stiffness of the layers. The anisotropic damage is completely described by a single scalar variable and its evolution law is specified from the principle of maximum dissipation. The stress/strain relationship is investigated in plane stress loading.

Keywords: damage, behavior modeling, meso-macro model, composite laminate, membrane loading

Procedia PDF Downloads 476

Authors: Adrien Marque, Daniel Delahaye, Pierre Maréchal, Isabelle Berry

Abstract:

Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and a specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty.

Keywords: wind direction, uncertainty level, unmanned aerial vehicle, convolution neural network, SPD matrices

Procedia PDF Downloads 50

Authors: Kais A. Kadhim

Abstract:

This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches.

Keywords: modals, meaning, persuasion, speech

Procedia PDF Downloads 405

Authors: Mabrouka Morri, Amel Soualmia, Philippe Belleudy

Abstract:

Vegetation affects the mean and turbulent flow structure. It may increase flood risks and sediment transport. Therefore, it is important to develop analytical approaches for the bed shear stress on vegetated bed, to predict resistance caused by vegetation. In the recent years, experimental and numerical models have both been developed to model the effects of submerged vegetation on open-channel flow. In this paper, different analytic models are compared and tested using the criteria of deviation, to explore their capacity for predicting the mean velocity and select the suitable one that will be applied in real case of rivers. The comparison between the measured data in vegetated flume and simulated mean velocities indicated, a good performance, in the case of rigid vegetation, whereas, Huthoff model shows the best agreement with a high coefficient of determination (R2=80%) and the smallest error in the prediction of the average velocities.

Keywords: analytic models, comparison, mean velocity, vegetation

Procedia PDF Downloads 276

Authors: Gianna Zou

Abstract:

Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

Procedia PDF Downloads 147

Authors: Pragyan Paramita Das, Vishwas N. Khatri

Abstract:

By using the lower- and upper- bound finite elements to limit analysis in conjunction with second-order conic programming (SOCP), the effect of seismic forces on the bearing capacity of surface strip footing resting on dense sand underlain by loose sand deposit is explored. The soil is assumed to obey the Mohr-Coulomb’s yield criterion and an associated flow rule. The angle of internal friction (ϕ) of the top and the bottom layer is varied from 42° to 44° and 32° to 34° respectively. The coefficient of seismic acceleration is varied from 0 to 0.3. The variation of bearing capacity with different thickness of top layer for various seismic acceleration coefficients is generated. A comparison will be made with the available solutions from literature wherever applicable.

Keywords: bearing capacity, conic programming, finite elements, seismic forces

Procedia PDF Downloads 170

Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat

Abstract:

This paper presents the study of hydro mechanical behavior of this optimal mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying- wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.

Keywords: tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction

Procedia PDF Downloads 507

Authors: Pei-Chann Chang, Jhen-Fu Liao, Chin-Hung Teng, Meng-Hui Chen

Abstract:

This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset.

Keywords: artificial immune system, collaborative filtering, recommendation system, similarity

Procedia PDF Downloads 536

Authors: Majid Forghani, Michael Khachay

Abstract:

In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

Procedia PDF Downloads 157

Authors: Maria F. Seran

Abstract:

In second language interaction, an EFL student always carries his knowledge of targeted language and sometimes gets influenced by his first language cultures which makes him transfer his utterances from the first language to the second language. The influence of L1 cultures somehow can lead to face-threatening act when it comes to responding on speech act, for instance, compliment. A speaker praises a compliment to show gratitude, and in return, he expects for compliment respond uttered by the hearer. While Western people use more acceptance continuum on compliment response, Indonesians utter more denial continuum which can somehow put the speakers into a face-threating situation and offense. This study investigated compliment response employed by EFL students and English native speakers. The study was distinct as none compliment response studies had been conducted to compare the compliment response between English native speakers and two different Indonesian EFL proficiency groups in which this research sought to meet this need. This study was significant for EFL teachers because it gave insight on cross-cultural understanding and brought pedagogical implication on explicit pragmatic instruction. Two research questions were set, 1. How do Indonesian EFL students and English native speakers respond compliments? 2. Is there any correlation between Indonesia EFL students’ proficiency and their compliment response use in English? The study involved three groups of participants; 5 English native speakers, 10 high-proficiency and 10 low-proficiency Indonesian EFL university students. The research instruments used in this study were as follows, an online TOEFL prediction test, focusing on grammar skill which was modified from Barron TOEFL exercise test, and a discourse completion task (DCT), consisting of 10 compliment respond items. Based on the research invitation, 20 second-year university students majoring in English education at Widya Mandira Catholic University, Kupang, East Nusa Tenggara, Indonesia who willingly participated in the research took the TOEFL prediction test online from the link provided. Students who achieved score 75-100 in test were categorized as high-proficiency students, while, students who attained score below 74 were considered as low-proficiency students. Then, the DCT survey was administered to these EFL groups and the native speaker group. Participants’ responses were coded and analyzed using categories of compliment response framework proposed by Tran. The study found out that 5 native speakers applied more compliment upgrades and appreciation token in compliment response, whereas, Indonesian EFL students combined some compliment response strategies in their utterance, such as, appreciation token, return and compliment downgrade. There is no correlation between students’ proficiency level and their CR responds as most EFL students in both groups produced less varied compliment responses and only 4 Indonesian high-proficiency students uttered more varied and were similar to the native speakers. The combination strategies used by EFL students can be explained as the influence of pragmatic transfer from L1 to L2; therefore, EFL teachers should explicitly teach more compliment response strategies to raise students’ awareness on English culture and elaborate their speaking to be more competence as close to native speakers as possible.

Keywords: compliment response, English native speakers, Indonesian EFL students, speech acts

Procedia PDF Downloads 148

Authors: Ihsane Modhaffar

Abstract:

Fiber orientation is essential for the physical properties of composite materials. The theoretical parameters of a given reinforcement are usually known and widely used to predict the behavior of the material. In this work, we propose an image processing approach to estimate true principal directions and fiber orientation during injection molding processes of short fiber reinforced thermoplastics. Generally, a group of fibers are described in terms of probability distribution function or orientation tensor. Numerical techniques for the prediction of fiber orientation are also considered for concentrated situations. The flow was considered to be incompressible, and behave as Newtonian fluid containing suspensions of short-fibers. The governing equations, of this problem are: the continuity, the momentum and the energy. The obtained results were compared to available experimental findings. A good agreement between the numerical results and the experimental data was achieved.

Keywords: injection, composites, short-fiber reinforced thermoplastics, fiber orientation, incompressible fluid, numerical simulation

Procedia PDF Downloads 532

Authors: Liu Zhiyuan, Sun Zongdi

Abstract:

In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

Procedia PDF Downloads 352