Search results for: gradient boosting machines (GBM)

Authors: L. Abukova, O. Abramova, A. Goreva, Y. Yakovlev

Abstract:

Important executive decisions on oil and gas production stimulation in Eastern Siberia have been recently taken. There are unique and large fields of oil, gas, and gas-condensate in Eastern Siberia. The Talakan, Koyumbinskoye, Yurubcheno-Tahomskoye, Kovykta, Chayadinskoye fields are supposed to be developed first. It will result in an abrupt increase in environmental load on the nature of Eastern Siberia. In Eastern Siberia, the introduction of ecological imperatives in hydrocarbon production is still realistic. Underground water movement is the one of the most important factors of the ecosystems condition management. Oil and gas production is associated with the forced displacement of huge water masses, mixing waters of different composition, and origin that determines the extent of anthropogenic impact on water drive systems and their protective reaction. An extensive hydrogeological system of the depression type is identified in the pre-salt deposits here. Pressure relieve here is steady up to the basement. The decrease of the hydrodynamic potential towards the basement with such a gradient resulted in reformation of the fields in process of historical (geological) development of the Nepsko-Botuobinskaya anteclise. The depression hydrodynamic systems are characterized by extremely high isolation and can only exist under such closed conditions. A steady nature of water movement due to a strictly negative gradient of reservoir pressure makes it quite possible to use environmentally-harmful liquid substances instead of water. Disposal of the most hazardous wastes is the most expedient in the deposits of the crystalline basement in certain structures distant from oil and gas fields. The time period for storage of environmentally-harmful liquid substances may be calculated by means of the geological time scales ensuring their complete prevention from releasing into environment or air even during strong earthquakes. Disposal of wastes of chemical and nuclear industries is a matter of special consideration. The existing methods of storage and disposal of wastes are very expensive. The methods applied at the moment for storage of nuclear wastes at the depth of several meters, even in the most durable containers, constitute a potential danger. The enormous size of the depression system of the Nepsko-Botuobinskaya anteclise makes it possible to easily identify such objects at the depth below 1500 m where nuclear wastes will be stored indefinitely without any environmental impact. Thus, the water drive system of the Nepsko-Botuobinskaya anteclise is the ideal object for large-volume injection of environmentally harmful liquid substances even if there are large oil and gas accumulations in the subsurface. Specific geological and hydrodynamic conditions of the system allow the production of hydrocarbons from the subsurface simultaneously with the disposal of industrial wastes of oil and gas, mining, chemical, and nuclear industries without any environmental impact.

Keywords: Eastern Siberia, formation pressure, underground water, waste burial

Procedia PDF Downloads 259

Authors: Farzad Yavari, Hamid Chegini, Saeed Lotfi

Abstract:

This paper reviews the comparison of Resin Rich (RR) and Vacuum Pressure Impregnation (VPI) insulation system qualities for stator bar of rotating electrical machines. Voltage endurance and tangent delta are two diagnostic tests to determine the quality of insulation systems. The paper describes the trend of dissipation factor while performing voltage endurance test for different stator bar samples made with RR and VPI insulation system methods. Some samples were made with the same strands and insulation thickness but with different main wall material to prove the influence of insulation system methods on stator bar quality. Also, some of the samples were subjected to voltage at the temperature of their insulation class, and their dissipation factor changes were measured and studied.

Keywords: VPI, resin rich, insulation, stator bar, dissipation factor, voltage endurance

Procedia PDF Downloads 201

Authors: Sung-Sik Park, Han Chang, Kyung-Hun Chae, Sae-Byeok Lee

Abstract:

In this study, a three-dimensional (3D) Particle method without using grid was developed for analyzing large deformation of soils instead of using ordinary finite element method (FEM) or finite difference method (FDM). In the 3D Particle method, the governing equations were discretized by various particle interaction models corresponding to differential operators such as gradient, divergence, and Laplacian. The Mohr-Coulomb failure criterion was incorporated into the 3D Particle method to determine soil failure. The yielding and hardening behavior of soil before failure was also considered by varying viscosity of soil. First of all, an unconfined compression test was carried out and the large deformation following soil yielding or failure was simulated by the developed 3D Particle method. The results were also compared with those of a commercial FEM software PLAXIS 3D. The developed 3D Particle method was able to simulate the 3D large deformation of soils due to soil yielding and calculate the variation of normal and shear stresses following clay deformation.

Keywords: particle method, large deformation, soil column, confined compressive stress

Procedia PDF Downloads 573

Authors: Alexandru-Ion Marinescu

Abstract:

There exist a plethora of methods in the scientific literature which tackle the well-established task of credit score evaluation. In its most abstract form, a credit scoring algorithm takes as input several credit applicant properties, such as age, marital status, employment status, loan duration, etc. and must output a binary response variable (i.e. “GOOD” or “BAD”) stating whether the client is susceptible to payment return delays. Data imbalance is a common occurrence among financial institution databases, with the majority being classified as “GOOD” clients (clients that respect the loan return calendar) alongside a small percentage of “BAD” clients. But it is the “BAD” clients we are interested in since accurately predicting their behavior is crucial in preventing unwanted loss for loan providers. We add to this whole context the constraint that the algorithm must yield an actual, tractable mathematical formula, which is friendlier towards financial analysts. To this end, we have turned to genetic algorithms and genetic programming, aiming to evolve actual mathematical expressions using specially tailored mutation and crossover operators. As far as data representation is concerned, we employ a very flexible mechanism – LINQ expression trees, readily available in the C# programming language, enabling us to construct executable pieces of code at runtime. As the title implies, they model trees, with intermediate nodes being operators (addition, subtraction, multiplication, division) or mathematical functions (sin, cos, abs, round, etc.) and leaf nodes storing either constants or variables. There is a one-to-one correspondence between the client properties and the formula variables. The mutation and crossover operators work on a flattened version of the tree, obtained via a pre-order traversal. A consequence of our chosen technique is that we can identify and discard client properties which do not take part in the final score evaluation, effectively acting as a dimensionality reduction scheme. We compare ourselves with state of the art approaches, such as support vector machines, Bayesian networks, and extreme learning machines, to name a few. The data sets we benchmark against amount to a total of 8, of which we mention the well-known Australian credit and German credit data sets, and the performance indicators are the following: percentage correctly classified, area under curve, partial Gini index, H-measure, Brier score and Kolmogorov-Smirnov statistic, respectively. Finally, we obtain encouraging results, which, although placing us in the lower half of the hierarchy, drive us to further refine the algorithm.

Keywords: expression trees, financial credit scoring, genetic algorithm, genetic programming, symbolic evolution

Procedia PDF Downloads 120

Authors: Atish Bagchi, Siva Chandrasekaran

Abstract:

Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

Procedia PDF Downloads 150

Authors: Leena Korpinen, Rauno Pääkkönen, Fabriziomaria Gobba, Vesa Virtanen

Abstract:

The number of persons with implanted cardiac pacemakers (PM) has increased in Western countries. The aim of this paper is to investigate the possible situations where persons with a PM may be exposed to extremely low frequency (ELF) electric (EF) and magnetic fields (MF) that may disturb their PM. Based on our earlier studies, it is possible to find such high public exposure to EFs only in some places near 400 kV power lines, where an EF may disturb a PM in unipolar mode. Such EFs cannot be found near 110 kV power lines. Disturbing MFs can be found near welding machines. However, we do not have measurement data from welding. Based on literature and earlier studies at Tampere University of Technology, it is difficult to find public EF or MF exposure that is high enough to interfere with PMs.

Keywords: cardiac pacemaker, electric field, magnetic field, electrical engineering

Procedia PDF Downloads 434

Authors: B. Merdas

Abstract:

The Guelma region constitutes a vast geothermal field whose local geothermal gradient is very high. Indeed, various thermal and thermo sources emerging in the region, including some at relatively high temperatures. In the mio Pliocene Hammam N'bails, basin emerges a hot spring that leaves develop a thick series of thermal travertine linked to it. Near the thermal emergences has settled a very special mineralization antimony and zinc and lead. The results of analyses of the thermal waters of the source of Hammam N'bails and the associated travertine, show abnormal values in Pb, Sb, Zn, As, and other metals, demonstrating the genetic link between those waters and mineralization. Hammam N'bails mineralizations by their mineral assembling represented and their association with the hot springs, are very similar to epithermal deposits with precious metals (gold and silver) like Senator mine in Turkey or ‘Carlin-type’ in Nevada (USA).

Keywords: hot springs, mineralization; basement faults, Guelma, NE Algeria

Procedia PDF Downloads 431

Authors: Murat Toren, Mehmet Çelebi

Abstract:

Reducing the weight of transformers while providing good performance, cost reduction and increased efficiency is important. Weight is one of the most significant factors in all electrical machines, and as such, many transformer design parameters are related to weight calculations. This study presents a comparison of the weight of oil type transformers and dry type transformer weight. Oil type transformers are mainly used in industry; however, dry type transformers are becoming more widespread in recent years. MATLAB is typically used for designing transformers and design parameters (rated voltages, core loss, etc.) along with design in ANSYS Maxwell. Similar to other studies, this study presented that the dry type transformer option is limited. Moreover, the commonly-used 50 kVA distribution transformers in the industry are oil type and dry type transformers are designed and considered in terms of weight. Currently, the preference for low-cost oil-type transformers would change if costs for dry-type transformer were more competitive. The aim of this study was to compare the weight of transformers, which is a substantial cost factor, and to provide an evaluation about increasing the use of dry type transformers.

Keywords: weight, optimization, oil-type transformers, dry-type transformers

Procedia PDF Downloads 354

Authors: Rohana Abdullah, Md Nizam Abd Rahman, Seri Rahayu Kamat

Abstract:

Operator efficiency aspect is gaining importance in ensuring optimized usage of resources especially in the semi-automated manufacturing environment. This paper addresses a case study done to solve operator efficiency and line balancing issue at a semiconductor assembly and test manufacturing. A Man-to-Machine (M2M) work study technique is used to study operator current utilization and determine the optimum allocation of the operators to the machines. Critical factors such as operator activity, activity frequency and operator competency level are considered to gain insight on the parameters that affects the operator utilization. Equipment standard time and overall equipment efficiency (OEE) information are also gathered and analyzed to achieve a balanced and optimized production.

Keywords: operator efficiency, optimized production, line balancing, industrial and manufacturing engineering

Procedia PDF Downloads 730

Authors: Naser B. Almari, Salem S. Alghamdi, Muhammad Afzal, Mohamed Helmy El Shal

Abstract:

Wheat landraces are a rich genetic resource for boosting agronomic qualities in breeding programs while also providing diversity and unique adaptation to local environmental conditions. These genotypes have grown increasingly important in the face of recent climate change challenges. This research aimed to look at the genetic diversity of Saudi Durum wheat landraces using morpho-phenological and molecular data. The principal components analysis (PCA) analysis recorded 78.47 % variance and 1.064 eigenvalues for the first six PCs of the total, respectively. The significant characters contributed more to the diversity are the length of owns at the tip relative to the length of the ear, culm: glaucosity of the neck, flag leaf: glaucosity of the sheath, flag leaf: anthocyanin coloration of auricles, plant: frequency of plants with recurved flag leaves, ear: length, and ear: shape in profile in the PC1. The significant wheat genotypes contributed more in the PC1 (8, 14, 497, 650, 569, 590, 594, 598, 600, 601, and 604). The cluster analysis recorded an 85.42 cophenetic correlation among the 22 wheat genotypes and grouped the genotypes into two main groups. Group, I contain 8 genotypes, however, the 2nd group contains 12 wheat genotypes, while two genotypes (13 and 497) are standing alone in the dendrogram and unable to make a group with any one of the genotypes. The second group was subdivided into two subgroups. The genotypes (14, 602, and 600) were present in the second sub-group. The genotypes were grouped into two main groups. The first group contains 17 genotypes, while the second group contains 3 (8, 977, and 594) wheat genotypes. The genotype (602) was standing alone and unable to make a group with any wheat genotype. The genotypes 650 and 13 also stand alone in the first group. Using the Mantel test, the data recorded a significant (R2 = 0.0006) correlation (phenotypic and genetic) among 22 wheat durum genotypes.

Keywords: durum wheat, PCA, cluster analysis, SRAP, genetic diversity

Procedia PDF Downloads 116

Authors: Landry Tchambou Tchouongsi, Appolinaire Derbetini Vondou

Abstract:

This study describes the local Hadley circulation (HC) during the December-February (DJF) and June-August (JJA) seasons, respectively, in Central Africa (CA) from the divergent component of the mean meridional wind and also from a new method called the variation of the ψ vector. Historical data from the ERA5 reanalysis for the period 1983 to 2013 were used. The results show that the maximum of the upward branch of the local Hadley circulation in the DJF and JJA seasons is located under the Congo Basin (CB). However, seasonal and horizontal variations in the mean temperature gradient and thermodynamic properties are largely associated with the distribution of convection and large-scale upward motion. Thus, temperatures beneath the CB show a slight variation between the DJF and JJA seasons. Moreover, energy transport of the moist static energy (MSE) adequately captures the mean flow component of the HC over the tropics. By the way, the divergence under the CB is enhanced by the presence of the low pressure of western Cameroon and the contribution of the warm and dry air currents coming from the Sahara.

Keywords: Circulation, reanalysis, thermodynamic, local Hadley.

Procedia PDF Downloads 89

Authors: Hansoo Kim, Seunghak Shin

Abstract:

The differential column shortening in tall buildings can be reduced by improving material and structural characteristics of the structural systems. This paper proposes structural methods to reduce differential column shortening in reinforced concrete tall buildings; connecting columns with rigidly jointed horizontal members, using outriggers, and placing additional reinforcement at the columns. The rigidly connected horizontal members including outriggers reduce the differential shortening between adjacent vertical members. The axial stiffness of columns with greater shortening can be effectively increased by placing additional reinforcement at the columns, thus the differential column shortening can be reduced in the design stage. The optimum distribution of additional reinforcement can be determined by applying a gradient based optimization technique.

Keywords: column shortening, long-term behavior, optimization, tall building

Procedia PDF Downloads 251

Authors: Vinay Kumar Vanjakula, Frank Adam, Nils Goseberg

Abstract:

Wind power is one of the leading renewable energy generation methods. Due to abundant higher wind speeds far away from shore, the construction of offshore wind turbines began in the last decades. However, installation of offshore foundation-based (monopiles) wind turbines in deep waters are often associated with technical and financial challenges. To overcome such challenges, the concept of floating wind turbines is expanded as the basis from the oil and gas industry. The unfolding of Universal heavyweight gravity anchor (UGA) for floating based foundation for floating Tension Leg Platform (TLP) sub-structures is developed in this research work. It is funded by the German Federal Ministry of Education and Research) for a three-year (2019-2022) research program called “Offshore Wind Solutions Plus (OWSplus) - Floating Offshore Wind Solutions Mecklenburg-Vorpommern.” It’s a group consists of German institutions (Universities, laboratories, and consulting companies). The part of the project is focused on the numerical modeling of gravity anchor that involves to analyze and solve fluid flow problems. Compared to gravity-based torpedo anchors, these UGA will be towed and lowered via controlled machines (tug boats) at lower speeds. This kind of installation of UGA are new to the offshore wind industry, particularly for TLP, and very few research works have been carried out in recent years. Conventional methods for transporting the anchor requires a large transportation crane vessel which involves a greater cost. This conceptual UGA anchors consists of ballasting chambers which utilizes the concept of buoyancy forces; the inside chambers are filled with the required amount of water in a way that they can float on the water for towing. After reaching the installation site, those chambers are ballasted with water for lowering. After it’s lifetime, these UGA can be unballasted (for erection or replacement) results in self-rising to the sea surface; buoyancy chambers give an advantage for using an UGA without the need of heavy machinery. However, while lowering/rising the UGA towards/away from the seabed, it experiences difficult, harsh marine environments due to the interaction of waves and currents. This leads to drifting of the anchor from the desired installation position and damage to the lowering machines. To overcome such harsh environments problems, a numerical model is built to investigate the influences of different outer contours and other fluid governing shapes that can be installed on the UGA to overcome the turbulence and drifting. The presentation will highlight the importance of the Computational Fluid Dynamics (CFD) numerical model in OpenFOAM, which is open-source programming software.

Keywords: anchor lowering, towing, waves, currrents, computational fluid dynamics

Procedia PDF Downloads 167

Authors: Mohammad Abdullah-Al-Wadud

Abstract:

In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

Procedia PDF Downloads 426

Authors: M. Asato, C. Liu, N. Fujima, T. Hoshino, Y. Chen, T. Mohri

Abstract:

We study the temperature dependence of the interaction energies (IEs) of X (=Ru, Rh) impurities in Pd, due to the Fermi-Dirac (FD) distribution and the thermal vibration effect by the Debye-Grüneisen model. The n-body (n=2~4) IEs among X impurities in Pd, being used to calculate the internal energies in the free energies of the Pd-rich PdX alloys, are determined uniquely and successively from the lower-order to higher-order, by the full-potential Korringa-Kohn-Rostoker Green’s function method (FPKKR), combined with the generalized gradient approximation in the density functional theory. We found that the temperature dependence of IEs due to the FD distribution, being usually neglected, is very important to reproduce the X-concentration dependence of the observed solvus temperatures of the Pd-rich PdX (X=Ru, Rh) alloys.

Keywords: full-potential KKR-green’s function method, Fermi-Dirac distribution, GGA, phase diagram of Pd-rich PdX (X=Ru, Rh) alloys, thermal vibration effect

Procedia PDF Downloads 275

Authors: Safia Rabaaoui, Héla Hachicha, Ezzeddine Zagrouba

Abstract:

Nowadays, cloud computing is becoming the more popular technology to various companies and consumers, which benefit from its increased efficiency, cost optimization, data security, unlimited storage capacity, etc. One of the biggest challenges of cloud computing is resource allocation. Its efficiency directly influences the performance of the whole cloud environment. Finding an effective method to address these critical issues and increase cloud performance was necessary. This paper proposes a mobile agents-based framework for dynamic resource allocation in cloud computing to minimize both the cost of using virtual machines and the makespan. Furthermore, its impact on the best response time and power consumption has been studied. The simulation showed that our method gave better results than here.

Keywords: cloud computing, multi-agent system, mobile agent, dynamic resource allocation, cost, makespan

Procedia PDF Downloads 105

Authors: Mansour Al Qubeissi, Sergei S. Sazhin, Cyril Crua, Morgan R. Heikal

Abstract:

This paper presents the application of the Discrete Component Model for heating and evaporation to multi-component biodiesel fuel droplets in direct injection internal combustion engines. This model takes into account the effects of temperature gradient, recirculation and species diffusion inside droplets. A distinctive feature of the model used in the analysis is that it is based on the analytical solutions to the temperature and species diffusion equations inside the droplets. Nineteen types of biodiesel fuels are considered. It is shown that a simplistic model, based on the approximation of biodiesel fuel by a single component or ignoring the diffusion of components of biodiesel fuel, leads to noticeable errors in predicted droplet evaporation time and time evolution of droplet surface temperature and radius.

Keywords: heat/mass transfer, biodiesel, multi-component fuel, droplet

Procedia PDF Downloads 568

Authors: Baeza S. Roberto

Abstract:

The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

Procedia PDF Downloads 585

Authors: Ammar Benamrani

Abstract:

This work investigates the equation of state parameters, elastic constants, and several other physical properties of (B2-type) Yttrium-Copper (YCu) rare earth intermetallic compound using the projected augmented wave (PAW) pseudopotentials method as implemented in the Quantum Espresso code. Using both the local density approximation (LDA) and the generalized gradient approximation (GGA), the finding of this research on the lattice parameter of YCu intermetallic compound agree very well with the experimental ones. The obtained results of the elastic constants and the Debye temperature are also in general in good agreement compared to the theoretical ones reported previously in literature. Furthermore, several thermodynamic properties of YCu intermetallic compound have been studied using quasi-harmonic approximations (QHA). The calculated data on the thermodynamic properties shows that the free energy and both isothermal and adiabatic bulk moduli decrease gradually with increasing of the temperature, while all other thermodynamic quantities increase with the temperature.

Keywords: Yttrium-Copper intermetallic compound, thermo_pw package, elastic constants, thermodynamic properties

Procedia PDF Downloads 149

Authors: Yi F. Wu, Ai Q. Li, Hao Wang

Abstract:

Taking a novel type of bridge bearings with the diameter being 100mm as an example, the theoretical analysis, the experimental research as well as the numerical simulation of the bearing were conducted. Since the normal compression-shear machines cannot be applied to the small-size bearing, an improved device to test the properties of the bearing was proposed and fabricated. Besides, the simulation of the bearing was conducted on the basis of the explicit finite element software ANSYS/LS-DYNA, and some parameters of the bearing are modified in the finite element model to effectively reduce the computation cost. Results show that all the research methods are capable of revealing the fundamental properties of the small-size bearings, and a combined use of these methods can better catch both the integral properties and the inner detailed mechanical behaviors of the bearing.

Keywords: ANSYS/LS-DYNA, compression shear, contact analysis, explicit algorithm, small-size

Procedia PDF Downloads 181

Authors: A.Zitouni, S.Bentata, B.Bouadjemi, T.Lantri, W. Benstaali, Z.Aziz, S.Cherid

Abstract:

We investigate the structural, electronic and magnetic properties of the diluted magnetic semiconductors (DMSs) CdCoTe and CdMnTe in the zinc blende phase with 25% of Co and Mn. The calculations are performed by the recent ab initio full potential augmented plane waves (FP_L/APW) method within the spin polarized density-functional theory (DFT) and the generalized gradient approximation GGA. Structural properties are determined from the total energy calculations and we found that these compounds are stable in the ferromagnetic phase. We discuss the electronic structures, total and partial densities of states and total magnetic moments. The calculated densities of states presented in this study identify the half-metallic of CdCoTe and CdMnTe.

Keywords: electronic structure, half-metallic, magnetic moment, total and partial densities of states

Procedia PDF Downloads 497

Authors: Md Imtiaz, Sanchita Dei, Shubham Damke

Abstract:

In recent years, there has been a rapidly accelerating shift from traditional surface processing operations to subsea processing operation. This shift has been driven by a number of factors including the depletion of shallow fields around the world, technological advances in subsea processing equipment, the need for production from marginal fields, and lower initial upfront investment costs compared to traditional production facilities. Moving production facilities to the seafloor offers a number of advantage, including a reduction in field development costs, increased production rates from subsea wells, reduction in the need for chemical injection, minimization of risks to worker ,reduction in spills due to hurricane damage, and increased in oil production by enabling production from marginal fields. Subsea processing consists of a range of technologies for separation, pumping, compression that enables production from offshore well without the need for surface facilities. At present, there are two primary technologies being used for subsea processing: subsea multiphase pumping and subsea separation. Multiphase pumping is the most basic subsea processing technology. Multiphase pumping involves the use of boosting system to transport the multiphase mixture through pipelines to floating production vessels. The separation system is combined with single phase pumps or water would be removed and either pumped to the surface, re-injected, or discharged to the sea. Subsea processing can allow for an entire topside facility to be decommissioned and the processed fluids to be tied back to a new, more distant, host. This type of application reduces costs and increased both overall facility and integrity and recoverable reserve. In future, full subsea processing could be possible, thereby eliminating the need for surface facilities.

Keywords: FPSO, marginal field, Subsea processing, SWAG

Procedia PDF Downloads 414

Authors: Ali Reza Vaezi, Ouldouz Bakhshi Rad

Abstract:

Gully erosion is a widespread and often dramatic form of soil erosion caused by water during and immediately after heavy rainfall. It occurs when flowing surface water is channelled across unprotected land and washes away the soil along the drainage lines. The formation of gully is influenced by various factors, including climate, drainage surface area, slope gradient, vegetation cover, land use, and soil properties. It is a very important problem in semi-arid regions, where soils have lower organic matter and are weakly aggregated. Intensive agriculture and tillage along the slope can accelerate soil erosion by water in the region. There is little information on the development of gully erosion in agricultural rainfed areas. Therefore, this study was carried out to investigate the relationship between gully erosion and morphometric characteristics of the drainage area and the effects of soil properties and soil management factors (land use and tillage method) on gully development. A field study was done in a 900 km2 agricultural area in Hshtroud township located in the south of East Azarbijan province, NW Iran. Toward this, two hundred twenty-two gullies created in rainfed lands were found in the area. Some properties of gullies, consisting of length, width, depth, height difference, cross section area, and volume, were determined. Drainage areas for each or some gullies were determined, and their boundaries were drawn. Additionally, the surface area of each drainage, land use, tillage direction, and soil properties that may affect gully formation were determined. The soil erodibility factor (K) defined in the Universal Soil Loss Equation (USLE) was estimated based on five soil properties (silt and very fine sand, coarse sand, organic matter, soil structure code, and soil permeability). Gully development in each drainage area was quantified using its volume and soil loss. The dependency of gully development on drainage area characteristics (surface area, land use, tillage direction, and soil properties) was determined using correlation matrix analysis. Based on the results, gully length was the most important morphometric characteristic indicating the development of gully erosion in the lands. Gully development in the area was related to slope gradient (r= -0.26), surface area (r= 0.71), the area of rainfed lands (r= 0.23), and the area of rainfed tilled along the slope (r= 0.24). Nevertheless, its correlation with the area of pasture and soil erodibility factor (K) was not significant. Among the characteristics of drainage area, surface area is the major factor controlling gully volume in the agricultural land. No significant correlation was found between gully erosion and soil erodibility factor (K) estimated by the Universal Soil Loss Equation (USLE). It seems the estimated soil erodibility can’t describe the susceptibility of the study soils to the gully erosion process. In these soils, aggregate stability and soil permeability are the two soil physical properties that affect the actual soil erodibility and in consequence, these soil properties can control gully erosion in the rainfed lands.

Keywords: agricultural area, gully properties, soil structure, USLE

Procedia PDF Downloads 78

Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

Abstract:

The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

Procedia PDF Downloads 92

Authors: Mamadou Dione, Eric Matzner-lober, Philippe Alexandre

Abstract:

The Energy Transition Act defined by the French State has precise implications on Renewable Energies, in particular on its remuneration mechanism. Until then, a purchase obligation contract permitted the sale of wind-generated electricity at a fixed rate. Tomorrow, it will be necessary to sell this electricity on the Market (at variable rates) before obtaining additional compensation intended to reduce the risk. This sale on the market requires to announce in advance (about 48 hours before) the production that will be delivered on the network, so to be able to predict (in the short term) this production. The fundamental problem remains the variability of the Wind accentuated by the geographical situation. The objective of the project is to provide, every day, short-term forecasts (48-hour horizon) of wind production using weather data. The predictions of the GFS model and those of the ECMWF model are used as explanatory variables. The variable to be predicted is the production of a wind farm. We do two approaches: a direct approach that predicts wind generation directly from weather data, and an integrated approach that estimâtes wind from weather data and converts it into wind power by power curves. We used machine learning techniques to predict this production. The models tested are random forests, CART + Bagging, CART + Boosting, SVM (Support Vector Machine). The application is made on a wind farm of 22MW (11 wind turbines) of the Compagnie du Vent (that became Engie Green France). Our results are very conclusive compared to the literature.

Keywords: forecast aggregation, machine learning, spatio-temporal dynamics modeling, wind power forcast

Procedia PDF Downloads 217

Authors: I. Kruashvili, I. Inashvili, K. Bziava, M. Lomishvili

Abstract:

Seepage capillary anomalies in the active layer of soil, related to the soil water movement, often cause variation of soil hydrophysical properties and become one of the main objectives of the hydroecology. It is necessary to mention that all existing equations for computing the seepage flow particularly from soil channels, through dams, bulkheads, and foundations of hydraulic engineering structures are preferable based on the linear seepage law. Regarding the existing beliefs, anomalous seepage is based on postulates according to which the fluid in free volume is characterized by resistance against shear deformation and is presented in the form of initial gradient. According to the above-mentioned information, we have determined: Equation to calculate seepage coefficient when the velocity of transition flow is equal to seepage flow velocity; by means of power function, equations for the calculation of average and maximum velocities of seepage flow have been derived; taking into consideration the fluid continuity condition, average velocity for calculation of average velocity in capillary tube has been received.

Keywords: seepage, soil, velocity, water

Procedia PDF Downloads 462

Authors: Elisa Boatti, Ulisse Stefanelli, Alessandro Reali, Ferdinando Auricchio

Abstract:

Thanks to their unusual properties, shape memory alloys (SMAs) are good candidates for advanced applications in a wide range of engineering fields, such as automotive, robotics, civil, biomedical, aerospace. In the last decades, the ever-growing interest for such materials has boosted several research studies aimed at modeling their complex nonlinear behavior in an effective and robust way. Since the constitutive response of SMAs is strongly thermomechanically coupled, the investigation of the non-isothermal evolution of the material must be taken into consideration. The present study considers an existing three-dimensional phenomenological model for SMAs, able to reproduce the main SMA properties while maintaining a simple user-friendly structure, and proposes a variational reformulation of the full non-isothermal version of the model. While the considered model has been thoroughly assessed in an isothermal setting, the proposed formulation allows to take into account the full nonisothermal problem. In particular, the reformulation is inspired to the GENERIC (General Equations for Non-Equilibrium Reversible-Irreversible Coupling) formalism, and is based on a generalized gradient flow of the total entropy, related to thermal and mechanical variables. Such phrasing of the model is new and allows for a discussion of the model from both a theoretical and a numerical point of view. Moreover, it directly implies the dissipativity of the flow. A semi-implicit time-discrete scheme is also presented for the fully coupled thermomechanical system, and is proven unconditionally stable and convergent. The correspondent algorithm is then implemented, under a space-homogeneous temperature field assumption, and tested under different conditions. The core of the algorithm is composed of a mechanical subproblem and a thermal subproblem. The iterative scheme is solved by a generalized Newton method. Numerous uniaxial and biaxial tests are reported to assess the performance of the model and algorithm, including variable imposed strain, strain rate, heat exchange properties, and external temperature. In particular, the heat exchange with the environment is the only source of rate-dependency in the model. The reported curves clearly display the interdependence between phase transformation strain and material temperature. The full thermomechanical coupling allows to reproduce the exothermic and endothermic effects during respectively forward and backward phase transformation. The numerical tests have thus demonstrated that the model can appropriately reproduce the coupled SMA behavior in different loading conditions and rates. Moreover, the algorithm has proved effective and robust. Further developments are being considered, such as the extension of the formulation to the finite-strain setting and the study of the boundary value problem.

Keywords: generalized gradient flow, GENERIC formalism, shape memory alloys, thermomechanical coupling

Procedia PDF Downloads 222

Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

Abstract:

In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

Procedia PDF Downloads 154

Authors: Mohammad Tahmasebipour, Hosein Salarpour

Abstract:

Micro cantilevers are one of the components used in the manufacture of micro-electromechanical systems. Epoxy microcantilevers have a variety of applications in the manufacture of micro-sensors and micro-actuators. In this paper, the Timoshenko Micro cantilever was statically and dynamically analyzed using the finite element method. First, all boundary conditions and initial conditions governing micro cantilevers were considered. The effect of size on the deflection, angle of rotation, natural frequencies, and mode shapes were then analyzed and evaluated under different frequencies. It was observed that an increased micro cantilever thickness reduces the deflection, rotation, and resonant frequency. A good agreement was observed between our results and those obtained by the couple stress theory, the classical theory, and the strain gradient elasticity theory.

Keywords: microcantilever, microsensor; epoxy, dynamic behavior, static behavior, finite element method

Procedia PDF Downloads 416

Authors: Khald Anisse, Amina Radgui, Mohammed Rziza

Abstract:

Tracking objects on video sequences is a very challenging task in many works in computer vision applications. However, there is no article that treats this topic in catadioptric vision. This paper is an attempt that tries to describe a new approach of omnidirectional images processing based on inverse stereographic projection in the half-sphere. We used the spherical model proposed by Gayer and al. For object tracking, our work is based on snake method, with optimization using the Greedy algorithm, by adapting its different operators. The algorithm will respect the deformed geometries of omnidirectional images such as spherical neighborhood, spherical gradient and reformulation of optimization algorithm on the spherical domain. This tracking method that we call "spherical snake" permitted to know the change of the shape and the size of object in different replacements in the spherical image.

Keywords: computer vision, spherical snake, omnidirectional image, object tracking, inverse stereographic projection

Procedia PDF Downloads 404