Search results for: loss models

Authors: Amos Olajide Oluyege, Emmanuel Uchechukwu Opara, Sunday Adeniyi Adedutan, Joseph Adeola Fuwape

Abstract:

This study was conducted to determine the durability of wood-cement particleboards when exposed to attack by subterranean termites, Macrotermes subhylinus. The boards were made from Terminalia superba and Gmelina arborea wood sawdust at nominal board densities (BD) of 1000, 900, and 800 kg/m³ using wood-cement mixing ratios (MR) of 3:1, 2.5:1, 2:1, and 1:1. Above ground durability tests against termite attack were carried out according to ASTM D 2017 for 14 weeks. Results of visual assessment of the wood cement particleboards show that all the board samples had a visual rating that was not less than 7 (i.e., moderate attack) for both species irrespective of the MR and BD. T. superba boards were found to have higher resistance to termite attack compared to their G. arborea counterparts. The mean values for weight loss following exposure ranged from 1.93 to 6.13% and 3.24 to 12.44%. Analysis of variance (ANOVA) results of the weight loss assessment revealed a significant (p < 0.05) effect of species and mixing ratio on the weight loss of the boards due to termite attack with F(₁,₇₂) = 92.890 and P = 0.000 and F(₃,₇₂) = 8.318 and p = 0.000, while board density did not have any significant effect (p > 0.05) with F (₂,₇₂) = 1.307 and p = 0.277. Thus, boards made from a higher mixing ratio had better resistance against termite attacks. Thus, it can be concluded that the durability of cement-bonded particleboards when exposed to subterranean termite attack is not only dependent on the quality of the wood raw material (species) but also on the enhanced protection imparted by the cement matrix; the protection increased with increase in cement/wood mixing ratio.

Keywords: cement-bonded particleboard, mixing ratio, board density, Gmelina arborea, Terminalia superba

Procedia PDF Downloads 214

Authors: Aminah Muchdar, Nuraeni, Eddy

Abstract:

The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

Procedia PDF Downloads 178

Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

Abstract:

This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

Procedia PDF Downloads 410

Authors: Abdullah E. Al-Mazrooei

Abstract:

In this paper, we present a new kind of the bilinear systems in the form of state space model. The evolution of this system depends on the product of state vector by its self. The well known Lotak Volterra and Lorenz models are special cases of this new model. We also present here a generalization of Kalman filter which is suitable to work with the new bilinear model. An application to real measurements is introduced to illustrate the efficiency of the proposed algorithm.

Keywords: bilinear systems, state space model, Kalman filter, application, models

Procedia PDF Downloads 439

Authors: A. Bahmanpour, I. Eames, C. Klettner, A. Dimakopoulos

Abstract:

We develop a new set of diagnostic tools to analyze inundation into a model district using three-dimensional CFD simulations, with a view to generating a database against which to test simpler models. A three-dimensional model of Oregon city with different-sized groups of building next to the coastline is used to run calculations of the movement of a long period wave on the shore. The initial and boundary conditions of the off-shore water are set using a nonlinear inverse method based on Eulerian spatial information matching experimental Eulerian time series measurements of water height. The water movement is followed in time, and this enables the pressure distribution on every surface of each building to be followed in a temporal manner. The three-dimensional numerical data set is validated against published experimental work. In the first instance, we use the dataset as a basis to understand the success of reduced models - including 2D shallow water model and reduced 1D models - to predict water heights, flow velocity and forces. This is because models based on the shallow water equations are known to underestimate drag forces after the initial surge of water. The second component is to identify critical flow features, such as hydraulic jumps and choked states, which are flow regions where dissipation occurs and drag forces are large. Finally, we describe how future tsunami inundation models should be modified to account for the complex effects of buildings through drag and blocking.Financial support from UCL and HR Wallingford is greatly appreciated. The authors would like to thank Professor Daniel Cox and Dr. Hyoungsu Park for providing the data on the Seaside Oregon experiment.

Keywords: computational fluid dynamics, extreme events, loading, tsunami

Procedia PDF Downloads 114

Authors: The Danh Phan

Abstract:

House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

Procedia PDF Downloads 228

Authors: Ibrahim Ahmed Jajere

Abstract:

Resource use conflicts capable of undermining of pastoralists’ livelihoods are on the increase in the North East Arid Zone of Nigeria. In order to expose socio - economic effects of conflicts and benefits of peace, this study assessed cost implications of farmers/pastoralists conflicts over natural resources. Interviews were conducted with 94 farmers, 90 agro-pastoralists and 91 pastoralists’ household heads. The farmers and agro-pastoralists were systematically sampled while pastoralists were located using snowballing. Both farmers and pastoralists suffered losses in the form of injuries to, and even death of household members, and loss of shelter. Farmers sustained losses of facilities and farm produce while pastoralists suffered loss and seizure of livestock, arrest of household members and forced migrations. The material losses in monetary terms amounted to 14,242,200.00 nairas for farmers, a figure higher than the 10,915,500.00 nairas incurred by pastoralists.

Keywords: cost, conflicts, farmers, pastoralists

Procedia PDF Downloads 264

Authors: Surapol Raadnui

Abstract:

This particular work describes an experimental investigation on gear wear in which wear and pitting were intentionally allowed to occur, namely, moisture corrosion pitting, acid-induced corrosion pitting, hard contaminant-related pitting and mechanical induced wear. A back to back spur gear test rig and a grease lubricated worm gear rig were used. The tests samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to pitting and wear degradation of the worn gears. In addition, weight loss from all test gear pairs were assessed with utilization of statistical design of experiment. It can be deduced that wear debris characteristics from both cases exhibited a direct relationship with different pitting and wear modes. Thus, it should be possible to detect and diagnose gear pitting and wear utilization of worn surfaces, generated wear debris and quantitative measurement such as weight loss.

Keywords: predictive maintenance, worm gear, spur gear, wear debris analysis, problem diagnostic

Procedia PDF Downloads 151

Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

Abstract:

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

Procedia PDF Downloads 151

Authors: Jayaletchumi T. Sambantha Moorthy, Suhaimi bin Ibrahim, Mohd Naz’ri Mahrin

Abstract:

Most quality models have defined usability as a significant factor that leads to improving product acceptability, increasing user satisfaction, improving product reliability, and also financially benefiting companies. Usability is also the best factor that acts as a balance for both the technical and human aspects of a software product, which is an important aspect in defining quality during software development process. A usability risk can be defined as a potential usability risk factor that a chosen action or activity may lead to a possible loss or an undesirable outcome. This could impact the usability of a software product thereby contributing to negative user experiences and causing a possible software product failure. Hence, it is important to mitigate and reduce usability risks in the software development process itself. By managing possible involved usability risks in software development process, failure of software product could be reduced. Therefore, this research uses the Delphi method to identify mitigation plans to reduce potential usability risks. The Delphi method is conducted with seven experts from the field of risk management and software development.

Keywords: usability, usability risk, risk management, risk mitigation, delphi study

Procedia PDF Downloads 465

Authors: Jose Juan Peña, J. Morales, J. García-Ravelo

Abstract:

In the search of potential models applied in the theoretical treatment of diatomic molecules, some of them have been constructed by using standard hyperbolic functions as well as from the so-called q-deformed hyperbolic functions (sc q-dhf) for displacing and modifying the shape of the potential under study. In order to transcend the scope of hyperbolic functions, in this work, a kind of generalized q-deformed hyperbolic functions (g q-dhf) is presented. By a suitable transformation, through the q deformation parameter, it is shown that these g q-dhf can be expressed in terms of their corresponding standard ones besides they can be reduced to the sc q-dhf. As a useful application of the proposed approach, and considering a class of exactly solvable multi-parameter exponential-type potentials, some new q-deformed quantum interactions models that can be used as interesting alternative in quantum physics and quantum states are presented. Furthermore, due that quantum potential models are conditioned on the q-dependence of the parameters that characterize to the exponential-type potentials, it is shown that many specific cases of q-deformed potentials are obtained as particular cases from the proposal.

Keywords: diatomic molecules, exponential-type potentials, hyperbolic functions, q-deformed potentials

Procedia PDF Downloads 181

Authors: Fu Jinyu, Lin Jinguan

Abstract:

This paper introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency data, and the other is discrete-time GQARCH employed to model low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named “GQARCH-It\^{o} -Jumps mode.” We adopt the realized range-based threshold estimation for high-frequency financial data rather than the realized return-based volatility estimators, which entail the loss of intra-day information of the price movement. Meanwhile, a quasi-likelihood function for the low-frequency GQARCH structure with jumps is developed for the parametric estimate. The asymptotic theories are mainly established for the proposed estimators in the case of finite activity jumps. Moreover, simulation studies are implemented to check the finite sample performance of the proposed methodology. Specifically, it is demonstrated that how our proposed approaches can be practically used on some financial data.

Keywords: It\^{o} process, GQARCH, leverage effects, threshold, realized range-based volatility estimator, quasi-maximum likelihood estimate

Procedia PDF Downloads 154

Authors: Tobias Beyer, Christoph Friedrich

Abstract:

Modern simulation methodology using finite element models is nowadays a recognized tool for product design/optimization. Likewise, manufacturing process design is increasingly becoming the focus of simulation methodology in order to enable sustainable results based on reduced real-life tests here as well. In this article, two process simulations -radial riveting and roller burnishing- used for mechanical joining of components are explained. In the first step, the required boundary conditions are developed and implemented in the respective simulation models. This is followed by process space validation. With the help of the validated models, the interdependencies of the input parameters are investigated and evaluated by means of sensitivity analyses. Limit case investigations are carried out and evaluated with the aid of the process simulations. Likewise, a comparison of the two joining methods to each other becomes possible.

Keywords: FEM, model-based process development, process simulation, radial riveting, roller burnishing, sensitivity analysis

Procedia PDF Downloads 106

Authors: H. C. Chinwenyi, H. D. Ibrahim, J. O. Adekunle

Abstract:

The incubation period is defined as the time from infection with a microorganism to development of symptoms. In this research, two disease models: one with incubation period and another without incubation period were studied. The study involves the use of a  mathematical model with a single incubation period. The test for the existence and stability of the disease free and the endemic equilibrium states for both models were carried out. The fourth order Runge-Kutta method was used to solve both models numerically. Finally, a computer program in MATLAB was developed to run the numerical experiments. From the results, we are able to show that the endemic equilibrium state of the model with incubation period is locally asymptotically stable whereas the endemic equilibrium state of the model without incubation period is unstable under certain conditions on the given model parameters. It was also established that the disease free equilibrium states of the model with and without incubation period are locally asymptotically stable. Furthermore, results from numerical experiments using empirical data obtained from Nigeria Centre for Disease Control (NCDC) showed that the overall population of the infected people for the model with incubation period is higher than that without incubation period. We also established from the results obtained that as the transmission rate from susceptible to infected population increases, the peak values of the infected population for the model with incubation period decrease and are always less than those for the model without incubation period.

Keywords: asymptotic stability, Hartman-Grobman stability criterion, incubation period, Routh-Hurwitz criterion, Runge-Kutta method

Procedia PDF Downloads 173

Authors: Azade Ghorbani-HasanSaraei, Seyed-Ahmad Shahidi, Sakineh Alizadeh, Adeleh Maghsoudlou

Abstract:

Improvement of color, as a quality attribute of Plum Concentrate, has been made possible by the increase in knowledge of kinetic of color change. Three different heating/evaporation processes were employed for the production of pPlum juice concentrate. The Plum juice was concentrated to a final 55 °Bx from an initial °Bx of 15 by microwave heating, rotary vacuum evaporator and evaporating at atmospheric pressure. The final Plum juice concentration of 55 °Bx was achieved in 17, 24 and 57 min by using the microwave, rotary vacuum and atmospheric heating processes, respectively. The colour change during concentration processes was investigated. Total colour differences, Hunter L, a and b parameters were used to estimate the extent of colour loss. All Hunter colour parameters decreased with time. The zero-order, first-order and a combined kinetics model were applied to the changes in colour parameters. Results indicated that variation in TCD followed both first-order and combined kinetics models, and parameters L, a and b followed only combined model. This model implied that the colour formation and pigment destruction occurred during concentration processes of plum juice.

Keywords: colour, kinetics, concentration, plum juice

Procedia PDF Downloads 518

Authors: John Deely, Stephen Hynes, Mary Cawley, Sarah Hogan

Abstract:

Domestic marine and coastal tourism have increased in importance over the last number of years due to the impacts of international travel, environmental concerns, associated health benefits and COVID-19 related travel restrictions. Consequently, this paper conceptualizes domestic marine and coastal tourism within an economic framework. Two logit models examine the factors that influence participation in the coastal day trips and overnight stays markets, respectively. Two truncated travel cost models are employed to explore trip duration, one analyzing the number of day trips taken and the other examining the number of nights spent in marine and coastal areas. Although a range of variables predicts participation, no one variable had a significant and consistent effect on every model. A division in access to domestic marine and coastal tourism is also observed based on variation in household income. The results also indicate a vibrant day trip market and large consumer surpluses.

Keywords: domestic marine and coastal tourism, day tripper, participation models, truncated travel cost model

Procedia PDF Downloads 131

Authors: Gaurav Kumar Sinha

Abstract:

The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

Procedia PDF Downloads 33

Authors: A. Arshad, M. Lehtonen. M. Humayun

Abstract:

With the increasing Distributed Generation (DG) penetration, distribution systems are advancing towards the smart grid technology for least latency in tackling voltage control problem in a distributed manner. This paper proposes a Multi-agent based distributed voltage level control. In this method a flat architecture of agents is used and agents involved in the whole controlling procedure are On Load Tap Changer Agent (OLTCA), Static VAR Compensator Agent (SVCA), and the agents associated with DGs and loads at their locations. The objectives of the proposed voltage control model are to minimize network losses and DG curtailments while maintaining voltage value within statutory limits as close as possible to the nominal. The total loss cost is the sum of network losses cost, DG curtailment costs, and voltage damage cost (which is based on penalty function implementation). The total cost is iteratively calculated for various stricter limits by plotting voltage damage cost and losses cost against varying voltage limit band. The method provides the optimal limits closer to nominal value with minimum total loss cost. In order to achieve the objective of voltage control, the whole network is divided into multiple control regions; downstream from the controlling device. The OLTCA behaves as a supervisory agent and performs all the optimizations. At first, a token is generated by OLTCA on each time step and it transfers from node to node until the node with voltage violation is detected. Upon detection of such a node, the token grants permission to Load Agent (LA) for initiation of possible remedial actions. LA will contact the respective controlling devices dependent on the vicinity of the violated node. If the violated node does not lie in the vicinity of the controller or the controlling capabilities of all the downstream control devices are at their limits then OLTC is considered as a last resort. For a realistic study, simulations are performed for a typical Finnish residential medium-voltage distribution system using Matlab ®. These simulations are executed for two cases; simple Distributed Voltage Control (DVC) and DVC with optimized loss cost (DVC + Penalty Function). A sensitivity analysis is performed based on DG penetration. The results indicate that costs of losses and DG curtailments are directly proportional to the DG penetration, while in case 2 there is a significant reduction in total loss. For lower DG penetration, losses are reduced more or less 50%, while for higher DG penetration, loss reduction is not very significant. Another observation is that the newer stricter limits calculated by cost optimization moves towards the statutory limits of ±10% of the nominal with the increasing DG penetration as for 25, 45 and 65% limits calculated are ±5, ±6.25 and 8.75% respectively. Observed results conclude that the novel voltage control algorithm proposed in case 1 is able to deal with the voltage control problem instantly but with higher losses. In contrast, case 2 make sure to reduce the network losses through proposed iterative method of loss cost optimization by OLTCA, slowly with time.

Keywords: distributed voltage control, distribution system, multi-agent systems, smart grids

Procedia PDF Downloads 310

Authors: Hafizuddin W. Yussof, Syamsutajri S. Bahri, Adam P. Harvey

Abstract:

Strong anion exchange resins with QN+OH-, have the potential to be developed and employed as heterogeneous catalyst for transesterification, as they are chemically stable to leaching of the functional group. Nine different SIERs (SIER1-9) with QN+OH- were prepared by suspension polymerization of vinylbenzyl chloride-divinylbenzene (VBC-DVB) copolymers in the presence of n-heptane (pore-forming agent). The amine group was successfully grafted into the polymeric resin beads through functionalization with trimethylamine. These SIERs are then used as a catalyst for the transesterification of triacetin with methanol. A set of differential equations that represents the Langmuir-Hinshelwood-Hougen-Watson (LHHW) and Eley-Rideal (ER) models for the transesterification reaction were developed. These kinetic models of LHHW and ER were fitted to the experimental data. Overall, the synthesized ion exchange resin-catalyzed reaction were well-described by the Eley-Rideal model compared to LHHW models, with sum of square error (SSE) of 0.742 and 0.996, respectively.

Keywords: anion exchange resin, Eley-Rideal, Langmuir-Hinshelwood-Hougen-Watson, transesterification

Procedia PDF Downloads 359

Authors: A. Chowdhury, P. Egodawatta, J. M. McGree, A. Goonetilleke

Abstract:

Hydrologic models are increasingly used as tools to predict stormwater quantity and quality from urban catchments. However, due to a range of practical issues, most models produce gross errors in simulating complex hydraulic and hydrologic systems. Difficulty in finding a robust approach for model calibration is one of the main issues. Though automatic calibration techniques are available, they are rarely used in common commercial hydraulic and hydrologic modelling software e.g. MIKE URBAN. This is partly due to the need for a large number of parameters and large datasets in the calibration process. To overcome this practical issue, a framework for automatic calibration of a hydrologic model was developed in R platform and presented in this paper. The model was developed based on the time-area conceptualization. Four calibration parameters, including initial loss, reduction factor, time of concentration and time-lag were considered as the primary set of parameters. Using these parameters, automatic calibration was performed using Approximate Bayesian Computation (ABC). ABC is a simulation-based technique for performing Bayesian inference when the likelihood is intractable or computationally expensive to compute. To test the performance and usefulness, the technique was used to simulate three small catchments in Gold Coast. For comparison, simulation outcomes from the same three catchments using commercial modelling software, MIKE URBAN were used. The graphical comparison shows strong agreement of MIKE URBAN result within the upper and lower 95% credible intervals of posterior predictions as obtained via ABC. Statistical validation for posterior predictions of runoff result using coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME) was found reasonable for three study catchments. The main benefit of using ABC over MIKE URBAN is that ABC provides a posterior distribution for runoff flow prediction, and therefore associated uncertainty in predictions can be obtained. In contrast, MIKE URBAN just provides a point estimate. Based on the results of the analysis, it appears as though ABC the developed framework performs well for automatic calibration.

Keywords: automatic calibration framework, approximate bayesian computation, hydrologic and hydraulic modelling, MIKE URBAN software, R platform

Procedia PDF Downloads 303

Authors: E. Al Daoud

Abstract:

In many cases, theoretical treatments are available for models for which there is no perfect physical realization. In this situation, the only possible test for an approximate theoretical solution is to compare with data generated from a computer simulation. In this paper, Monte Carlo tools are used to study and compare the elementary particles models. All the experiments are implemented using 10000 events, and the simulated energy is 13 TeV. The mean and the curves of several variables are calculated for each model using MadAnalysis 5. Anomalies in the results can be seen in the muons masses of the minimal supersymmetric standard model and the two Higgs doublet model.

Keywords: Feynman rules, hadrons, Lagrangian, Monte Carlo, simulation

Procedia PDF Downloads 315

Authors: K. Nasir, S. Ahmad, A. Khan, H. Benkreira

Abstract:

The topic of the research is to develop sustainable fire-resistant materials for vibration and acoustic damping of structure and airborne noises from sustainable recycled materials and biodegradable binders. The paper reports, methods and techniques of enhancing fire resistive, vibration and acoustic properties of building insulation materials made from natural resources like wood and recycled materials like rubber and textile waste. The structures are designed to optimize the number, size and stratification of closed (heat insulating) and open (noise insulating) pores. The samples produced are tested for their heat and noise insulating properties, including vibration damping and their structural properties (airflow resistivity, porosity, tortuosity and elastic modulus). The structural properties are then used in theoretical models to check the acoustic insulation measurements. Initial data indicate that one layer of such material can yield as much as 18 times more damping, increasing the loss factor by 18%.

Keywords: fire resistant, vibration damping, acoustic material, vibro-acoustic, thermal insulation, sustainable material, low cost materials, recycled materials, construction material

Procedia PDF Downloads 131

Authors: Aadityaa Singla

Abstract:

The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

Procedia PDF Downloads 144

Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

Abstract:

Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

Procedia PDF Downloads 392

Authors: Moulana Mohammed

Abstract:

Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

Procedia PDF Downloads 133

Authors: Georgi Vendramin, Aurea Lúcia, Yamamoto, Carlos Itsuo, Camargo Nogueira, Carlos Eduardo, Lenz, Anderson Miguel, Souza Melegari, Samuel N.

Abstract:

The thermal performance of a solar water heating with 1.00 m2 flat plate collectors in Cascavel-PR, is which presented in this article, paper presents the solution to leverage the marketing of solar heating systems through detailed constituent materials of the solar collector studies, these abundant materials in construction, such as expanded polyethylene, PVC, aluminum and glass tubes, mixing them with new materials to minimize loss of efficiency while decreasing its cost. The system was tested during months and the collector obtained maximum recorded temperature of outlet fluid of 55 °C, while the maximum temperature of the water at the bottom of the hot water tank was 35 °C. The average daily energy collected was 19 6 MJ/d; the energy supplied by the solar plate was 16.2 MJ/d; the loss in the feed pipe was 3.2 MJ/d; the solar fraction was 32.2%, the efficiency of the collector was 45.6% and the efficiency of the system was 37.8%.

Keywords: recycling materials, energy efficiency, solar collector, solar water heating system

Procedia PDF Downloads 595

Authors: Laura Kocsor, Laszlo Peter, Laszlo Kovacs, Zsolt Kis

Abstract:

The aim of our research is to prepare rare-earth-doped lithium niobate (LiNbO3) nanocrystals, having only a few dopant ions in the focal point of an exciting laser beam. These samples will be used to achieve individual addressing of the dopant ions by light beams in a confocal microscope setup. One method for the preparation of nanocrystalline materials is to reduce the particle size by mechanical grinding. High-energy ball-milling was used in several works to produce nano lithium niobate. Previously, it was reported that dry high-energy ball-milling of lithium niobate in a shaker mill results in the partial reduction of the material, which leads to a balanced formation of bipolarons and polarons yielding gray color together with oxygen release and Li2O segregation on the open surfaces. In the present work we focus on preparing LiNbO3 nanocrystals by high-energy ball-milling using a Fritsch Pulverisette 7 planetary mill. Every ball-milling process was carried out in zirconia vial with zirconia balls of different sizes (from 3 mm to 0.1 mm), wet grinding with water, and the grinding time being less than an hour. Gradually decreasing the ball size to 0.1 mm, an average particle size of about 10 nm could be obtained determined by dynamic light scattering and verified by scanning electron microscopy. High-energy ball-milling resulted in sample darkening evidenced by optical absorption spectroscopy measurements indicating that the material underwent partial reduction. The unwanted lithium oxide loss decreases the Li/Nb ratio in the crystal, strongly influencing the spectroscopic properties of lithium niobate. Zirconia contamination was found in ground samples proved by energy-dispersive X-ray spectroscopy measurements; however, it cannot be explained based on the hardness properties of the materials involved in the ball-milling process. It can be understood taking into account the presence of lithium hydroxide formed the segregated lithium oxide and water during the ball-milling process, through chemically induced abrasion. The quantity of the segregated Li2O was measured by coulometric titration. During the wet milling process in the planetary mill, it was found that the lithium oxide loss increases linearly in the early phase of the milling process, then a saturation of the Li2O loss can be seen. This change goes along with the disappearance of the relatively large particles until a relatively narrow size distribution is achieved in accord with the dynamic light scattering measurements. With the 3 mm ball size and 1100 rpm rotation rate, the mean particle size achieved is 100 nm, and the total Li2O loss is about 1.2 wt.% of the original LiNbO3. Further investigations have been done to minimize the Li2O segregation during the ball-milling process. Since the Li2O loss was observed to increase with the growing total surface of the particles, the influence of ball-milling parameters on its quantity has also been studied.

Keywords: high-energy ball-milling, lithium niobate, mechanochemical reaction, nanocrystals

Procedia PDF Downloads 133

Authors: M. Pepe, S. Ackermann, L. Fregonese, C. Achille

Abstract:

3D models obtained with advanced survey techniques such as close-range photogrammetry and laser scanner are nowadays particularly appreciated in Cultural Heritage and Archaeology fields. In order to produce high quality models representing archaeological evidences and anthropological artifacts, the appearance of the model (i.e. color) beyond the geometric accuracy, is not a negligible aspect. The integration of the close-range photogrammetry survey techniques with the laser scanner is still a topic of study and research. By combining point cloud data sets of the same object generated with both technologies, or with the same technology but registered in different moment and/or natural light condition, could construct a final point cloud with accentuated color dissimilarities. In this paper, a methodology to uniform the different data sets, to improve the chromatic quality and to highlight further details by balancing the point color will be presented.

Keywords: color models, cultural heritage, laser scanner, photogrammetry

Procedia PDF Downloads 279

Authors: R. K. Patel

Abstract:

The experiment entitled “Bionomics of Cryptophlebia ombrodelta Lower (Lepidoptera: Tortricidae), a Major Pest of Tamarind, Tamarindus indica in Bastar tribal belt of Chhattisgarh” was conducted at S. G. College of Agriculture and Research Station, Jagdalpur (Chhattisgarh) during 2014-15. The moth, Cryptophlebia ombrodelta (Lower) is very destructive pest to tamarind, Tamarindus indica. The mature larva is pinkish in colour whereas, the moth is generally grayish in colour and it lays pale yellowish - white, flat and round eggs near the peduncle joint of pod (fruit) or on the pod surface. The newly hatched larva enters into the fruit by making hole packed with excreta. It completes three to four generation in a year and can cause fourty two per cent loss to tamarind fruits. The morphological details of this pest were studied.

Keywords: bionomics, Cryptophlebia ombrodelta, loss, pest, Tamarind, Tamarindus indica

Procedia PDF Downloads 297

Authors: Bhavita S. Dave, Jaimin Vaidya, Chandresh H. Solanki, Atul K.

Abstract:

To measure the permeability of a soil specimen, one of the basic assumptions of Darcy's law is that the soil sample should be saturated. Unlike saturated soils, the permeability of unsaturated soils cannot be found using conventional methods as it does not follow Darcy's law. Many empirical models, such as the Van Genuchten Model and Fredlund-Xing Model were suggested to predict permeability value for unsaturated soil. Such models use data from the soil-freezing characteristic curve to find fitting parameters for frozen unsaturated soils. In this study, soil specimens were subjected to 0, 1, 3, and 5 freezing-thawing (F-T) cycles for different degrees of saturation to have a wide range of suction, and its soil freezing characteristic curves were formulated for all F-T cycles. Changes in fitting parameters and relative permeability with subsequent F-T cycles are presented in this paper for both models.

Keywords: frozen unsaturated soil, Fredlund Xing model, soil-freezing characteristic curve, Van Genuchten model

Procedia PDF Downloads 187