Search results for: residual life prediction

Authors: Anto Antony Samy, Atefeh Golbang, Edward Archer, Alistair McIlhagger

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Fused deposition modelling (FDM) is one of the additive manufacturing techniques that has become highly attractive in the industrial and academic sectors. However, parts fabricated through FDM are highly susceptible to geometrical defects such as warpage, shrinkage, and delamination that can severely affect their function. Among the thermoplastic polymer feedstock for FDM, semi-crystalline polymers are highly prone to part distortion due to polymer crystallization. In this study, the influence of FDM processing conditions such as chamber temperature and print bed temperature on the induced thermal residual stress and resulting warpage are investigated using the 3D transient thermal model for a semi-crystalline polymer. The thermo-mechanical properties and the viscoelasticity of the polymer, as well as the crystallization physics, which considers the crystallinity of the polymer, are coupled with the evolving temperature gradient of the print model. From the results, it was observed that increasing the chamber temperature from 25°C to 75°C lead to a decrease of 1.5% residual stress, while decreasing bed temperature from 100°C to 60°C, resulted in a 33% increase in residual stress and a significant rise of 138% in warpage. The simulated warpage data is validated by comparing it with the measured warpage values of the samples using 3D scanning.

Keywords: finite element analysis, fused deposition modelling, residual stress, warpage

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Authors: Po-Jen Su, Huann-Ming Chou

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In this article we uses the residual correction method to deal with transient thermoelastic problems with a hollow spherical region when the continuum medium possesses spherically isotropic thermoelastic properties. Based on linear thermoelastic theory, the equations of hyperbolic heat conduction and thermoelastic motion were combined to establish the thermoelastic dynamic model with consideration of the deformation acceleration effect and non-Fourier effect under the condition of transient thermal shock. The approximate solutions of temperature and displacement distributions are obtained using the residual correction method based on the maximum principle in combination with the finite difference method, making it easier and faster to obtain upper and lower approximations of exact solutions. The proposed method is found to be an effective numerical method with satisfactory accuracy. Moreover, the result shows that the effect of transient thermal shock induced by deformation acceleration is enhanced by non-Fourier heat conduction with increased peak stress. The influence on the stress increases with the thermal relaxation time.

Keywords: maximum principle, non-Fourier heat conduction, residual correction method, thermo-elastic response

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: A. S. Ibrahim Mamane, S. Giljean, M.-J. Pac, G. L’Hostis

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Fiber reinforced composite laminates are particularly subject to residual stresses due to their heterogeneity and the complex chemical, mechanical and thermal mechanisms that occur during their processing. Residual stresses are now well known to cause damage accumulation, shape instability, and behavior disturbance in composite parts. Many works exist in the literature on techniques for minimizing residual stresses in thermosetting and thermoplastic composites mainly. To study in-depth the influence of processing mechanisms on the formation of residual stresses and to minimize them by establishing a reliable correlation, it is essential to be able to measure very precisely the profile of residual stresses in the composite. Residual stresses are important data to consider when sizing composite parts and predicting their behavior. The incremental hole drilling is very effective in measuring the gradient of residual stresses in composite laminates. This method is semi-destructive and consists of drilling incrementally a hole through the thickness of the material and measuring relaxation strains around the hole for each increment using three strain gauges. These strains are then converted into residual stresses using a matrix of coefficients. These coefficients, called calibration coefficients, depending on the diameter of the hole and the dimensions of the gauges used. The reliability of the incremental hole drilling depends on the accuracy with which the calibration coefficients are determined. These coefficients are calculated using a finite element model. The samples’ features and the experimental conditions must be considered in the simulation. Any mismatch can lead to inadequate calibration coefficients, thus introducing errors on residual stresses. Several calibration coefficient correction methods exist for isotropic material, but there is a lack of information on this subject concerning composite laminates. In this work, a Python program was developed to automatically generate the adequate finite element model. This model allowed us to perform a parametric study to assess the influence of experimental errors on the calibration coefficients. The results highlighted the sensitivity of the calibration coefficients to the considered errors and gave an order of magnitude of the precisions required on the experimental device to have reliable measurements. On the basis of these results, improvements were proposed on the experimental device. Furthermore, a numerical method was proposed to correct the calibration coefficients for different types of materials, including thick composite parts for which the analytical approach is too complex. This method consists of taking into account the experimental errors in the simulation. Accurate measurement of the experimental errors (such as eccentricity of the hole, angular deviation of the gauges from their theoretical position, or errors on increment depth) is therefore necessary. The aim is to determine more precisely the residual stresses and to expand the validity domain of the incremental hole drilling technique.

Keywords: fiber reinforced composites, finite element simulation, incremental hole drilling method, numerical correction of the calibration coefficients, residual stresses

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Authors: Gholamreza Ghodrati Amiri, Mojtaba Jafarian Abyaneh, Ali Zare Hosseinzadeh

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This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.

Keywords: frame, grey wolf optimization algorithm, modal residual force, structural damage detection

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Sachin Pawar, Suman Patra, Goutam Mukhopadhyay

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The primary function of a shaft is to transfer power. The shaft can be cast or forged and then machined to the final shape. Manufacturing of ~5 m length and 0.6 m diameter shaft is very critical. More difficult is to maintain its straightness during heat treatment and machining operations, which involve thermal and mechanical loads, respectively. During the machining operation of a such forged mandrel shaft, a deflection of 3-4mm was observed. To remove this deflection shaft was pressed at both ends which led to the development of cracks in it. To investigate the root cause of the deflection and cracking, the sample was cut from the failed shaft. Possible causes were identified with the help of a cause and effect diagram. Chemical composition analysis, microstructural analysis, and hardness measurement were done to confirm whether the shaft meets the required specifications or not. Chemical composition analysis confirmed that the material grade was 42CrMo4. Microstructural analysis revealed the presence of untempered martensite, indicating improper heat treatment. Due to this, ductility and impact toughness values were considerably lower than the specification of the mentioned grade. Residual stress measurement of one more bent shaft manufactured by a similar route was done by portable X-ray diffraction(XRD) technique. For better understanding, measurements were done at twelve different locations along the length of the shaft. The occurrence of a high amount of undesirable tensile residual stresses close to the Ultimate Tensile Strength(UTS) of the material was observed. Untempered martensitic structure, lower ductility, lower impact strength, and presence of a high amount of residual stresses all confirmed the improper tempering heat treatment of the shaft. Tempering relieves the residual stresses. Based on the findings of this study, stress-relieving heat treatment was done to remove the residual stresses and deflection in the shaft successfully.

Keywords: residual stress, mandrel shaft, untempered martensite, portable XRD

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Merzak Laribi, Abdelmadjid Kasser

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Thermal spraying processes are widely used to produce coatings on original constructions as well as in repair and maintenance of long standing structures. A lot of efforts forwarding to develop thermal spray coatings technology have been focused on improving mechanical characteristics, minimizing residual stress level and reducing porosity of the coatings. The specific aim of this paper is to determine either residual stresses distribution or wear resistance of stainless steel coating thermally sprayed on a carbon steel substrate. Internal stresses determination was performed using an extensometric method in combination with a simultaneous progressive electrolytic polishing. The procedure consists of measuring micro-deformations using a bi-directional extensometric gauges glued on the substrate side of the materials. Very thin layers of the deposits are removed by electrochemical polishing across the sample surface. Micro-deformations are instantaneously measured, leading to residual stresses calculation after each removal. Wear resistance of the coating has been determined using a ball-on-plate tribometer. Friction coefficient is instantaneously measured during the tribological test. Attention was particularly focused on the influence of a post-annealing at 850 °C for one hour in vacuum either on the residual stresses distribution or on the wear resistance behavior under specific wear and lubrication conditions. The obtained results showed that the microstructure of the obtained arc sprayed stainless steel coating is classical. It is homogeneous and contains un-melted particles, metallic oxides and also pores and micro-cracks. The internal stresses are in compression in the coating. They are more or less scattered between -50 and -270 MPa on the surface and decreased more at the interface. The value at the surface of the substrate is about –700 MPa, partially due to the molten particles impact with the substrate. The post annealing has reduced the residual stresses in both coating and surface of the steel substrate so that the hole material becomes more relaxed. Friction coefficient has an average value of 0.3 and 0.4 respectively for non annealed and annealed specimen. It is rather oil lubrication which is really benefit so that friction coefficient is decreased to about 0.06.

Keywords: residual stresses, wear resistance, stainless steel, coating, thermal spraying, annealing, lubrication

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Ana Paula S. A. Correa, Allan K. R. Galardo, Luana A. Lima, Talita F. Sobral, Josiane N. Muller, Jessica F. S. Barroso, Nercy V. R. Furtado, Ednaldo C. Rêgo., Jose B. P. Lima

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Malaria is a public health problem in the Brazilian Legal Amazon. Among the integrated approaches for anopheline control, the Indoor Residual Spraying (IRS) remains one of the main tools in the basic strategy applied in the Amazonian States, where the National Malaria Control Program currently uses one of the insecticides from the pyrethroid class, the Etofenprox WP. Understanding the residual efficacy of insecticides on different surfaces is essential to determine the spray cycles, in order to maintain a rational use and to avoid product waste. The aim of this study was to evaluate the residual efficacy of Etofenprox - VECTRON ® 20 WP on surfaces of Unplastered Cement (UC) and Unpainted Wood (UW) on panels, in field, and in semi-field evaluation of Brazil’s Amapa State. The evaluation criteria used was the cone bioassay test, following the World Health Organization (WHO) recommended method, using plastic cones and female mosquitos of Anopheles sp. The tests were carried out in laboratory panels, semi-field evaluation in a “test house” built in the Macapa municipality, and in the field in 20 houses, being ten houses per surface type (UC and UW), in an endemic malaria area in Mazagão’s municipality. The residual efficacy was measured from March to September 2017, starting one day after the spraying, repeated monthly for a period of six months. The UW surface presented higher residual efficacy than the UC. In fact, the UW presented a residual efficacy of the insecticide throughout the period of this study with a mortality rate above 80% in the panels (= 95%), in the "test house" (= 86%) and in field houses ( = 87%). On the UC surface it was observed a mortality decreased in all the tests performed, with a mortality rate of 45, 47 and 29% on panels, semi-field and in field, respectively; however, the residual efficacy ≥ 80% only occurred in the first evaluation after the 24-hour spraying bioassay in the "test house". Thus, only the UW surface meets the specifications of the World Health Organization Pesticide Evaluation Scheme (WHOPES) regarding the duration of effective action (three to six months). To sum up, the insecticide residual efficacy presented variability on the different surfaces where it was sprayed. Although the IRS with Etofenprox WP was efficient on UW surfaces, and it can be used in spraying cycles at 4-month intervals, it is important to consider the diversity of houses in the Brazilian Legal Amazon, in order to implement alternatives for vector control, including the evaluation of new products or different formulations types for insecticides.

Keywords: Anopheles, vector control, insecticide, bioassay

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Authors: Herviyanti, Gusnidar, M. Harianti

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The objective of this research were: a) using low-rank coal (subbituminous) as main humate material sources because this material will not be anthracite, and cannot using to be an energy sources b) to examine residual effects of humic matter from subbituminous which was combined with P fertilizers to adsorp Al and Fe metal, improving soil fertility, and increasing P fertilizing efficiency and Oxisol productivity. Therefore, optimalization crop productivity of upland rice can be achieved. The experiment was designed using a 3 x 4 factorial with 3 replications in randomly groups design. The 1st factor was 3 ways incubating humate material with P-fertilizer, which are: I1 = Incubation of humate material 1 week, then incubation P-fertilizers 1 week; I2 = Incubation of humate materials and P fertilizers directly into the soil for 2 weeks; and I3 = humate material and P fertilizer mixed for 1 week, then incubation to the soil for 1 week. The 2nd factor was residual effects of humate material and P-fertilizer combination which are 4 doses H1 = 400 ppm (0.8 Mg/ha) + 100% R; H2 = 400 ppm + 75% R; H3 = 800 ppm (1.6 Mg/ha) + 100% R,; and H4 = 800 ppm + 75% R. The 2nd year research results showed that the best treatment was founded residue effect of 800 ppm humate material and 100% R P-fertilizer doses in I3 way incubation that is equal to 6.19 t ha-1 upland rice yield. However, this result is almost the same as residual effects of 800 ppm humate material + 75% R P-fertilizer doses and upland rice yield the 1st year. It was concluded that addition of humate material can given the efficiency of P-fertilizer using up to 25% until the 2nd season planted.

Keywords: humate materials, P-fertilizer, subbituminous, upland rice

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: M. R. Daneshgar, S. E. Habibi, E. Daneshgar, A. Daneshgar

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In this paper, a two-dimensional method is developed to simulate the fillet welds in a stiffened cylindrical shell, using finite element method. The stiffener material is aluminum 2519. The thermo-elasto-plastic analysis is used to analyze the thermo-mechanical behavior. Due to the high heat flux rate of the welding process, two uncouple thermal and mechanical analysis are carried out instead of performing a single couple thermo-mechanical simulation. In order to investigate the effects of the welding procedures, two different welding techniques are examined. The resulted residual stresses and distortions due to different welding procedures are obtained. Furthermore, this study employed the technique of element birth and death to simulate the weld filler variation with time in fillet welds. The obtained results are in good agreement with the published experimental and three-dimensional numerical simulation results. Therefore, the proposed 2D modeling technique can effectively give the corresponding results of 3D models. Furthermore, by inspection of the obtained residual hoop and transverse stresses and angular distortions, proper welding procedure is suggested.

Keywords: stiffened cylindrical shell, fillet welds, residual stress, angular distortion, finite element method

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: A. Kouadri-Henni, L. Barrallier

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The objective of the study was to characterize the properties of a magnesium alloy welded by friction stir welding (FSW). The results led to a better understanding of the relationship between this process, the microstructure and anisotropic properties of alloy materials. Welding principally leads to a large reduction in grain size in welded zones due to the phenomenon of dynamic recrystallization. The most remarkable observation was that crystallographic textures changed from a base metal with one texture in two zones: the thermo-mechanically affected and stir welded zones. The latter zone has the peculiarity of possessing a marked texture with two components on the basal plane and the pyramidal plane. These characteristics disappeared in the TMAZ, which had only one component following the basal plane. These modifications have been explained by the nature of the plastic deformation in these zones, which occurs at a moderate temperature in the TMAZ and high temperature in the SWZ. In the same time, we compared this evolution with the nature and the level of the residual stresses obtained by X-ray diffraction.

Keywords: texture christallography, residual stresses, FSW process

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Sami Aboujafar

Abstract:

Oil recovery in petroleum reservoirs is greatly affected by fluid-rock and fluid-fluid interactions. These interactions directly control rock wettability, capillary pressure and relative permeability curves. Laboratory core-floods and centrifuge experiments were conducted on sandstone and carbonate cores to study the effect of low and high brine salinity and viscosity and oil viscosity on residual saturations and relative permeability. Drainage and imbibition relative permeability in two phase system were measured, refined lab oils with different viscosities, heavy and light, and several brine salinities were used. Sensitivity analysis with different values for the salinity and viscosity of the fluids,, oil and water, were done to investigate the effect of these properties on water/oil relative permeability, residual oil saturation and oil recovery. Experiments were conducted on core material from viscous/heavy and light oil fields. History matching core flood simulator was used to study how the relative permeability curves and end point saturations were affected by different fluid properties using several correlations. Results were compared with field data and literature data. The results indicate that there is a correlation between the oil viscosity and/or brine salinity and residual oil saturation and water relative permeability end point. Increasing oil viscosity reduces the Krw@Sor and increases Sor. The remaining oil saturation from laboratory measurements might be too high due to experimental procedures, capillary end effect and early termination of the experiment, especially when using heavy/viscous oil. Similarly the Krw@Sor may be too low. The effect of wettability on the observed results is also discussed. A consistent relationship has been drawn between the fluid parameters, water/oil relative permeability and residual saturations, and a descriptor may be derived to define different flow behaviors. The results of this work will have application to producing fields and the methodologies developed could have wider application to sandstone and carbonate reservoirs worldwide.

Keywords: history matching core flood simulator, oil recovery, relative permeability, residual saturations

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Authors: A. Mostafa

Abstract:

This paper presents a numerical study on determination of ballistic limit velocity (V₅₀) of stainless steel 304 (SS 304) used in manufacturing security screens. The simulated ballistic impact tests were conducted on clamped sheets with different thicknesses using ABAQUS/Explicit nonlinear finite element (FE) package. The ballistic limit velocity was determined using three approaches, namely: numerical tests based on material properties, FE calculated residual velocities and FE calculated residual energies. Johnson-Cook plasticity and failure criterion were utilized to simulate the dynamic behaviour of the SS 304 under various strain rates, while the well-known Lambert-Jonas equation was used for the data regression for the residual velocity and energy model. Good agreement between the investigated numerical methods was achieved. Additionally, the dependence of the ballistic limit velocity on the sheet thickness was observed. The proposed approaches present viable and cost-effective assessment methods of the ballistic performance of SS 304, which will support the development of robust security screen systems.

Keywords: ballistic velocity, stainless steel, numerical approaches, security screen

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

Abstract:

Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

Abstract:

Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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Authors: Meihua Fang, Tao Fei

Abstract:

Post-processing can effectively improve the resistance to laser damage in multilayer films used in a high power laser system. In this work, HfO₂/SiO₂ multilayer films are prepared by e-beam evaporation and then β-ray irradiation is employed as the post-processing method. The particle irradiation affects the laser induced damage threshold (LIDT), which includes defects, surface roughness, packing density, and residual stress. The residual stress that is relaxed during irradiation changes from compressive stress into tensile stress. Our results indicate that appropriate tensile stress can improve LIDT remarkably. In view of the fact that LIDT rises from 8 J/cm² to 12 J/cm², i.e., 50% increase, after the film has been irradiated by 2.2×10¹³/cm² β-ray, the particle irradiation can be used as a controllable and desirable post-processing method to improve the resistance to laser induced damage.

Keywords: β-ray irradiation, multilayer film, residual stress, laser-induced damage threshold

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Authors: Chen-Yin Kuo, Yung-Hsin Lee

Abstract:

Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

Procedia PDF Downloads 286