Search results for: Wind prediction
42 Dispersion Rate of Spilled Oil in Water Column under Non-Breaking Water Waves
Authors: Hanifeh Imanian, Morteza Kolahdoozan
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The purpose of this study is to present a mathematical phrase for calculating the dispersion rate of spilled oil in water column under non-breaking waves. In this regard, a multiphase numerical model is applied for which waves and oil phase were computed concurrently, and accuracy of its hydraulic calculations have been proven. More than 200 various scenarios of oil spilling in wave waters were simulated using the multiphase numerical model and its outcome were collected in a database. The recorded results were investigated to identify the major parameters affected vertical oil dispersion and finally 6 parameters were identified as main independent factors. Furthermore, some statistical tests were conducted to identify any relationship between the dependent variable (dispersed oil mass in the water column) and independent variables (water wave specifications containing height, length and wave period and spilled oil characteristics including density, viscosity and spilled oil mass). Finally, a mathematical-statistical relationship is proposed to predict dispersed oil in marine waters. To verify the proposed relationship, a laboratory example available in the literature was selected. Oil mass rate penetrated in water body computed by statistical regression was in accordance with experimental data was predicted. On this occasion, it was necessary to verify the proposed mathematical phrase. In a selected laboratory case available in the literature, mass oil rate penetrated in water body computed by suggested regression. Results showed good agreement with experimental data. The validated mathematical-statistical phrase is a useful tool for oil dispersion prediction in oil spill events in marine areas.Keywords: Dispersion, marine environment, mathematical-statistical relationship, oil spill.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 113541 Concentrated Solar Power Utilization in Space Vehicles Propulsion and Power Generation
Authors: Maged A. Mossallam
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The objective from this paper is to design a solar thermal engine for space vehicles orbital control and electricity generation. A computational model is developed for the prediction of the solar thermal engine performance for different design parameters and conditions in order to enhance the engine efficiency. The engine is divided into two main subsystems. First, the concentrator dish which receives solar energy from the sun and reflects them to the cavity receiver. The second one is the cavity receiver which receives the heat flux reflected from the concentrator and transfers heat to the fluid passing over. Other subsystems depend on the application required from the engine. For thrust application, a nozzle is introduced to the system for the fluid to expand and produce thrust. Hydrogen is preferred as a working fluid in the thruster application. Results model developed is used to determine the thrust for a concentrator dish 4 meters in diameter (provides 10 kW of energy), focusing solar energy to a 10 cm aperture diameter cavity receiver. The cavity receiver outer length is 50 cm and the internal cavity is 47 cm in length. The suggested design material of the internal cavity is tungsten to withstand high temperature. The thermal model and analysis shows that the hydrogen temperature at the plenum reaches 2000oK after about 250 seconds for hot start operation for a flow rate of 0.1 g/sec.Using solar thermal engine as an electricity generation device on earth is also discussed. In this case a compressor and turbine are used to convert the heat gained by the working fluid (air) into mechanical power. This mechanical power can be converted into electrical power by using a generator.Keywords: Concentrated Solar Energy, Orbital Control, Power Generation, Solar Thermal Engine, Space Vehicles Propulsion
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 205740 Using Artificial Neural Network and Leudeking-Piret Model in the Kinetic Modeling of Microbial Production of Poly-β- Hydroxybutyrate
Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand
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Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 479939 Artificial Intelligent in Optimization of Steel Moment Frame Structures: A Review
Authors: Mohsen Soori, Fooad Karimi Ghaleh Jough
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The integration of Artificial Intelligence (AI) techniques in the optimization of steel moment frame structures represents a transformative approach to enhance the design, analysis, and performance of these critical engineering systems. The review encompasses a wide spectrum of AI methods, including machine learning algorithms, evolutionary algorithms, neural networks, and optimization techniques, applied to address various challenges in the field. The synthesis of research findings highlights the interdisciplinary nature of AI applications in structural engineering, emphasizing the synergy between domain expertise and advanced computational methodologies. This synthesis aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking a comprehensive understanding of the state-of-the-art in AI-driven optimization for steel moment frame structures. The paper commences with an overview of the fundamental principles governing steel moment frame structures and identifies the key optimization objectives, such as efficiency of structures. Subsequently, it delves into the application of AI in the conceptual design phase, where algorithms aid in generating innovative structural configurations and optimizing material utilization. The review also explores the use of AI for real-time structural health monitoring and predictive maintenance, contributing to the long-term sustainability and reliability of steel moment frame structures. Furthermore, the paper investigates how AI-driven algorithms facilitate the calibration of structural models, enabling accurate prediction of dynamic responses and seismic performance. Thus, by reviewing and analyzing the recent achievements in applications artificial intelligent in optimization of steel moment frame structures, the process of designing, analysis, and performance of the structures can be analyzed and modified.
Keywords: Artificial Intelligent, optimization process, steel moment frame, structural engineering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17538 Economic Evaluation of Degradation by Corrosion of an on-Grid Battery Energy Storage System: A Case Study in Algeria Territory
Authors: Fouzia Brihmat
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Economic planning models, which are used to build microgrids and Distributed Energy Resources (DER), are the current norm for expressing such confidence. These models often decide both short-term DER dispatch and long-term DER investments. This research investigates the most cost-effective hybrid (photovoltaic-diesel) renewable energy system (HRES) based on Total Net Present Cost (TNPC) in an Algerian Saharan area, which has a high potential for solar irradiation and has a production capacity of 1 GW/h. Lead-acid batteries have been around much longer and are easier to understand, but have limited storage capacity. Lithium-ion batteries last longer, are lighter, but generally more expensive. By combining the advantages of each chemistry, we produce cost-effective high-capacity battery banks that operate solely on AC coupling. The financial implications of this research describe the corrosion process that occurs at the interface between the active material and grid material of the positive plate of a lead-acid battery. The best cost study for the HRES is completed with the assistance of the HOMER Pro MATLAB Link. Additionally, during the course of the project's 20 years, the system is simulated for each time step. In this model, which takes into consideration decline in solar efficiency, changes in battery storage levels over time, and rises in fuel prices above the rate of inflation, the trade-off is that the model is more accurate, but the computation takes longer. We initially utilized the optimizer to run the model without multi-year in order to discover the best system architecture. The optimal system for the single-year scenario is the Danvest generator, which has 760 kW, 200 kWh of the necessary quantity of lead-acid storage, and a somewhat lower Cost Of Energy (COE) of $0.309/kWh. Different scenarios that account for fluctuations in the gasified biomass generator's production of electricity have been simulated, and various strategies to guarantee the balance between generation and consumption have been investigated.
Keywords: Battery, Corrosion, Diesel, Economic planning optimization, Hybrid energy system, HES, Lead-acid battery, Li-ion battery, multi-year planning, microgrid, price forecast, total net present cost, wind.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14937 Geostatistical Analysis and Mapping of Groundlevel Ozone in a Medium Sized Urban Area
Authors: F. J. Moral García, P. Valiente González, F. López Rodríguez
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Ground-level tropospheric ozone is one of the air pollutants of most concern. It is mainly produced by photochemical processes involving nitrogen oxides and volatile organic compounds in the lower parts of the atmosphere. Ozone levels become particularly high in regions close to high ozone precursor emissions and during summer, when stagnant meteorological conditions with high insolation and high temperatures are common. In this work, some results of a study about urban ozone distribution patterns in the city of Badajoz, which is the largest and most industrialized city in Extremadura region (southwest Spain) are shown. Fourteen sampling campaigns, at least one per month, were carried out to measure ambient air ozone concentrations, during periods that were selected according to favourable conditions to ozone production, using an automatic portable analyzer. Later, to evaluate the ozone distribution at the city, the measured ozone data were analyzed using geostatistical techniques. Thus, first, during the exploratory analysis of data, it was revealed that they were distributed normally, which is a desirable property for the subsequent stages of the geostatistical study. Secondly, during the structural analysis of data, theoretical spherical models provided the best fit for all monthly experimental variograms. The parameters of these variograms (sill, range and nugget) revealed that the maximum distance of spatial dependence is between 302-790 m and the variable, air ozone concentration, is not evenly distributed in reduced distances. Finally, predictive ozone maps were derived for all points of the experimental study area, by use of geostatistical algorithms (kriging). High prediction accuracy was obtained in all cases as cross-validation showed. Useful information for hazard assessment was also provided when probability maps, based on kriging interpolation and kriging standard deviation, were produced.Keywords: Kriging, map, tropospheric ozone, variogram.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 184936 Influence of Improved Roughage Quality and Period of Meal Termination on Digesta Load in the Digestive Organs of Goats
Authors: Rasheed A. Adebayo, Mehluli M. Moyo, Ignatius V. Nsahlai
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Ruminants are known to relish roughage for productivity but the effect of its quality on digesta load in rumen, omasum, abomasum and other distal organs of the digestive tract is yet unknown. Reticulorumen fill is a strong indicator for long-term control of intake in ruminants. As such, the measurement and prediction of digesta load in these compartments may be crucial to productivity in the ruminant industry. The current study aimed at determining the effect of (a) diet quality on digesta load in digestive organs of goats, and (b) period of meal termination on the reticulorumen fill and digesta load in other distal compartments of the digestive tract of goats. Goats were fed with urea-treated hay (UTH), urea-sprayed hay (USH) and non-treated hay (NTH). At the end of eight weeks of a feeding trial period, upon termination of a meal in the morning, afternoon or evening, all goats were slaughtered in random groups of three per day to measure reticulorumen fill and digesta loads in other distal compartments of the digestive tract. Both diet quality and period affected (P < 0.05) the measure of reticulorumen fill. However, reticulorumen fill in the evening was larger (P < 0.05) than afternoon, while afternoon was similar (P > 0.05) to morning. Also, diet quality affected (P < 0.05) the wet omasal digesta load, wet abomasum, dry abomasum and dry caecum digesta loads but did not affect (P > 0.05) both wet and dry digesta loads in other compartments of the digestive tract. Period of measurement did not affect (P > 0.05) the wet omasal digesta load, and both wet and dry digesta loads in other compartments of the digestive tract except wet abomasum digesta load (P < 0.05) and dry caecum digesta load (P < 0.05). Both wet and dry reticulorumen fill were correlated (P < 0.05) with omasum (r = 0.623) and (r = 0.723), respectively. In conclusion, reticulorumen fill of goats decreased by improving the roughage quality; and the period of meal termination and measurement of the fill is a key factor to the quantity of digesta load.
Keywords: Digesta, goats, meal termination, reticulorumen fill.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 80435 Lung Cancer Detection and Multi Level Classification Using Discrete Wavelet Transform Approach
Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar
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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.
Keywords: ANN, DWT, GLCM, KNN, ROI, artificial neural networks, discrete wavelet transform, gray-level co-occurrence matrix, k-nearest neighbor, region of interest.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 94634 Predicting Foreign Direct Investment of IC Design Firms from Taiwan to East and South China Using Lotka-Volterra Model
Authors: Bi-Huei Tsai
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This work explores the inter-region investment behaviors of Integrated Circuit (IC) design industry from Taiwan to China using the amount of foreign direct investment (FDI). According to the mutual dependence among different IC design industrial locations, Lotka-Volterra model is utilized to explore the FDI interactions between South and East China. Effects of inter-regional collaborations on FDI flows into China are considered. The analysis results show that FDIs into South China for IC design industry significantly inspire the subsequent FDIs into East China, while FDIs into East China for Taiwan’s IC design industry significantly hinder the subsequent FDIs into South China. Because the supply chain along IC industry includes upstream IC design, midstream manufacturing, as well as downstream packing and testing enterprises, IC design industry has to cooperate with IC manufacturing, packaging and testing industries in the same area to form a strong IC industrial cluster. Taiwan’s IC design industry implement the largest FDI amount into East China and the second largest FDI amount into South China among the four regions: North, East, Mid-West and South China. If IC design houses undertake more FDIs in South China, those in East China are urged to incrementally implement more FDIs into East China to maintain the competitive advantages of the IC supply chain in East China. On the other hand, as the FDIs in East China rise, the FDIs in South China will successively decline since capitals have concentrated in East China. In addition, this investigation proves that the prediction of Lotka-Volterra model in FDI trends is accurate because the industrial interactions between the two regions are included. Finally, this work confirms that the FDI flows cannot reach a stable equilibrium point, so the FDI inflows into East and South China will expand in the future.Keywords: Lotka-Volterra model, Foreign direct investment, Competitive, Equilibrium analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 146333 Classification of Extreme Ground-Level Ozone Based on Generalized Extreme Value Model for Air Monitoring Station
Authors: Siti Aisyah Zakaria, Nor Azrita Mohd Amin, Noor Fadhilah Ahmad Radi, Nasrul Hamidin
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Higher ground-level ozone (GLO) concentration adversely affects human health, vegetations as well as activities in the ecosystem. In Malaysia, most of the analysis on GLO concentration are carried out using the average value of GLO concentration, which refers to the centre of distribution to make a prediction or estimation. However, analysis which focuses on the higher value or extreme value in GLO concentration is rarely explored. Hence, the objective of this study is to classify the tail behaviour of GLO using generalized extreme value (GEV) distribution estimation the return level using the corresponding modelling (Gumbel, Weibull, and Frechet) of GEV distribution. The results show that Weibull distribution which is also known as short tail distribution and considered as having less extreme behaviour is the best-fitted distribution for four selected air monitoring stations in Peninsular Malaysia, namely Larkin, Pelabuhan Kelang, Shah Alam, and Tanjung Malim; while Gumbel distribution which is considered as a medium tail distribution is the best-fitted distribution for Nilai station. The return level of GLO concentration in Shah Alam station is comparatively higher than other stations. Overall, return levels increase with increasing return periods but the increment depends on the type of the tail of GEV distribution’s tail. We conduct this study by using maximum likelihood estimation (MLE) method to estimate the parameters at four selected stations in Peninsular Malaysia. Next, the validation for the fitted block maxima series to GEV distribution is performed using probability plot, quantile plot and likelihood ratio test. Profile likelihood confidence interval is tested to verify the type of GEV distribution. These results are important as a guide for early notification on future extreme ozone events.
Keywords: Extreme value theory, generalized extreme value distribution, ground-level ozone, return level.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 50432 Variational Explanation Generator: Generating Explanation for Natural Language Inference Using Variational Auto-Encoder
Authors: Zhen Cheng, Xinyu Dai, Shujian Huang, Jiajun Chen
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Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable.Keywords: Natural Language Inference, explanation generation, variational auto-encoder, generative model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 67331 Classifying Turbomachinery Blade Mode Shapes Using Artificial Neural Networks
Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton
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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.
Keywords: Modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 89430 Deorbiting Performance of Electrodynamic Tethers to Mitigate Space Debris
Authors: Giulia Sarego, Lorenzo Olivieri, Andrea Valmorbida, Carlo Bettanini, Giacomo Colombatti, Marco Pertile, Enrico C. Lorenzini
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International guidelines recommend removing any artificial body in Low Earth Orbit (LEO) within 25 years from mission completion. Among disposal strategies, electrodynamic tethers appear to be a promising option for LEO, thanks to the limited storage mass and the minimum interface requirements to the host spacecraft. In particular, recent technological advances make it feasible to deorbit large objects with tether lengths of a few kilometers or less. To further investigate such an innovative passive system, the European Union is currently funding the project E.T.PACK – Electrodynamic Tether Technology for Passive Consumable-less Deorbit Kit in the framework of the H2020 Future Emerging Technologies (FET) Open program. The project focuses on the design of an end of life disposal kit for LEO satellites. This kit aims to deploy a taped tether that can be activated at the spacecraft end of life to perform autonomous deorbit within the international guidelines. In this paper, the orbital performance of the E.T.PACK deorbiting kit is compared to other disposal methods. Besides, the orbital decay prediction is parametrized as a function of spacecraft mass and tether system performance. Different values of length, width, and thickness of the tether will be evaluated for various scenarios (i.e., different initial orbital parameters). The results will be compared to other end-of-life disposal methods with similar allocated resources. The analysis of the more innovative system’s performance with the tape coated with a thermionic material, which has a low work-function (LWT), for which no active component for the cathode is required, will also be briefly discussed. The results show that the electrodynamic tether option can be a competitive and performant solution for satellite disposal compared to other deorbit technologies.Keywords: Deorbiting performance, H2020, spacecraft disposal, space electrodynamic tethers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 60929 A Continuous Real-Time Analytic for Predicting Instability in Acute Care Rapid Response Team Activations
Authors: Ashwin Belle, Bryce Benson, Mark Salamango, Fadi Islim, Rodney Daniels, Kevin Ward
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A reliable, real-time, and non-invasive system that can identify patients at risk for hemodynamic instability is needed to aid clinicians in their efforts to anticipate patient deterioration and initiate early interventions. The purpose of this pilot study was to explore the clinical capabilities of a real-time analytic from a single lead of an electrocardiograph to correctly distinguish between rapid response team (RRT) activations due to hemodynamic (H-RRT) and non-hemodynamic (NH-RRT) causes, as well as predict H-RRT cases with actionable lead times. The study consisted of a single center, retrospective cohort of 21 patients with RRT activations from step-down and telemetry units. Through electronic health record review and blinded to the analytic’s output, each patient was categorized by clinicians into H-RRT and NH-RRT cases. The analytic output and the categorization were compared. The prediction lead time prior to the RRT call was calculated. The analytic correctly distinguished between H-RRT and NH-RRT cases with 100% accuracy, demonstrating 100% positive and negative predictive values, and 100% sensitivity and specificity. In H-RRT cases, the analytic detected hemodynamic deterioration with a median lead time of 9.5 hours prior to the RRT call (range 14 minutes to 52 hours). The study demonstrates that an electrocardiogram (ECG) based analytic has the potential for providing clinical decision and monitoring support for caregivers to identify at risk patients within a clinically relevant timeframe allowing for increased vigilance and early interventional support to reduce the chances of continued patient deterioration.
Keywords: Critical care, early warning systems, emergency medicine, heart rate variability, hemodynamic instability, rapid response team.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 158228 Temperature-Based Detection of Initial Yielding Point in Loading of Tensile Specimens Made of Structural Steel
Authors: Aqsa Jamil, Hiroshi Tamura, Hiroshi Katsuchi, Jiaqi Wang
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Yield point represents the upper limit of forces which can be applied on a specimen without causing any permanent deformation. After yielding, the behavior of specimen suddenly changes including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of thermography camera. The yield point of specimens was estimated by the help of temperature dip which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing repeatability analysis. The effect of temperature imperfection and light source has been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of thermographic technique.
Keywords: Signal to noise ratio, thermoelastic effect, thermography, yield point.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34527 A Novel GNSS Integrity Augmentation System for Civil and Military Aircraft
Authors: Roberto Sabatini, Terry Moore, Chris Hill
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This paper presents a novel Global Navigation Satellite System (GNSS) Avionics Based Integrity Augmentation (ABIA) system architecture suitable for civil and military air platforms, including Unmanned Aircraft Systems (UAS). Taking the move from previous research on high-accuracy Differential GNSS (DGNSS) systems design, integration and experimental flight test activities conducted at the Italian Air Force Flight Test Centre (CSV-RSV), our research focused on the development of a novel approach to the problem of GNSS ABIA for mission- and safety-critical air vehicle applications and for multi-sensor avionics architectures based on GNSS. Detailed mathematical models were developed to describe the main causes of GNSS signal outages and degradation in flight, namely: antenna obscuration, multipath, fading due to adverse geometry and Doppler shift. Adopting these models in association with suitable integrity thresholds and guidance algorithms, the ABIA system is able to generate integrity cautions (predictive flags) and warnings (reactive flags), as well as providing steering information to the pilot and electronic commands to the aircraft/UAS flight control systems. These features allow real-time avoidance of safety-critical flight conditions and fast recovery of the required navigation performance in case of GNSS data losses. In other words, this novel ABIA system addresses all three cornerstones of GNSS integrity augmentation in mission- and safety-critical applications: prediction (caution flags), reaction (warning flags) and correction (alternate flight path computation).
Keywords: Global Navigation Satellite Systems (GNSS), Integrity Augmentation, Unmanned Aircraft Systems, Aircraft Based Augmentation, Avionics Based Integrity Augmentation, Safety-Critical Applications.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 322426 TNFRSF11B Gene Polymorphisms A163G and G11811C in Prediction of Osteoporosis Risk
Authors: Boroňová I., Bernasovská J., Kľoc J., Tomková Z., Petrejčíková E., Gabriková D., Mačeková S.
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Osteoporosis is a complex health disease characterized by low bone mineral density, which is determined by an interaction of genetics with metabolic and environmental factors. Current research in genetics of osteoporosis is focused on identification of responsible genes and polymorphisms. TNFRSF11B gene plays a key role in bone remodeling. The aim of this study was to investigate the genotype and allele distribution of A163G (rs3102735) osteoprotegerin gene promoter and G1181C (rs2073618) osteoprotegerin first exon polymorphisms in the group of 180 unrelated postmenopausal women with diagnosed osteoporosis and 180 normal controls. Genomic DNA was isolated from peripheral blood leukocytes using standard methodology. Genotyping for presence of different polymorphisms was performed using the Custom Taqman®SNP Genotyping assays. Hardy-Weinberg equilibrium was tested for each SNP in the groups of participants using the chi-square (χ2) test. The distribution of investigated genotypes in the group of patients with osteoporosis were as follows: AA (66.7%), AG (32.2%), GG (1.1%) for A163G polymorphism; GG (19.4%), CG (44.4%), CC (36.1%) for G1181C polymorphism. The distribution of genotypes in normal controls were follows: AA (71.1%), AG (26.1%), GG (2.8%) for A163G polymorphism; GG (22.2%), CG (48.9%), CC (28.9%) for G1181C polymorphism. In A163G polymorphism the variant G allele was more common among patients with osteoporosis: 17.2% versus 15.8% in normal controls. Also, in G1181C polymorphism the phenomenon of more frequent occurrence of C allele in the group of patients with osteoporosis was observed (58.3% versus 53.3%). Genotype and allele distributions showed no significant differences (A163G: χ2=0.270, p=0.605; χ2=0.250, p=0.616; G1181C: χ2= 1.730, p=0.188; χ2=1.820, p=0.177). Our results represents an initial study, further studies of more numerous file and associations studies will be carried out. Knowing the distribution of genotypes is important for assessing the impact of these polymorphisms on various parameters associated with osteoporosis. Screening for identification of “at-risk” women likely to develop osteoporosis and initiating subsequent early intervention appears to be most effective strategy to substantially reduce the risks of osteoporosis.
Keywords: Osteoporosis, Real-time PCR method, SNP polymorphisms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 223525 Assessment of Predictive Confounders for the Prevalence of Breast Cancer among Iraqi Population: A Retrospective Study from Baghdad, Iraq
Authors: Nadia H. Mohammed, Anmar Al-Taie, Fadia H. Al-Sultany
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Although breast cancer prevalence continues to increase, mortality has been decreasing as a result of early detection and improvement in adjuvant systemic therapy. Nevertheless, this disease required further efforts to understand and identify the associated potential risk factors that could play a role in the prevalence of this malignancy among Iraqi women. The objective of this study was to assess the perception of certain predictive risk factors on the prevalence of breast cancer types among a sample of Iraqi women diagnosed with breast cancer. This was a retrospective observational study carried out at National Cancer Research Center in College of Medicine, Baghdad University from November 2017 to January 2018. Data of 100 patients with breast cancer whose biopsies examined in the National Cancer Research Center were included in this study. Data were collected to structure a detailed assessment regarding the patients’ demographic, medical and cancer records. The majority of study participants (94%) suffered from ductal breast cancer with mean age 49.57 years. Among those women, 48.9% were obese with body mass index (BMI) 35 kg/m2. 68.1% of them had positive family history of breast cancer and 66% had low parity. 40.4% had stage II ductal breast cancer followed by 25.5% with stage III. It was found that 59.6% and 68.1% had positive oestrogen receptor sensitivity and positive human epidermal growth factor (HER2/neu) receptor sensitivity respectively. In regard to the impact of prediction of certain variables on the incidence of ductal breast cancer, positive family history of breast cancer (P < 0.0001), low parity (P< 0.0001), stage I and II breast cancer (P = 0.02) and positive HER2/neu status (P < 0.0001) were significant predictive factors among the study participants. The results from this study provide relevant evidence for a significant positive and potential association between certain risk factors and the prevalence of breast cancer among Iraqi women.
Keywords: Ductal breast cancer, hormone sensitivity, Iraq, risk factors.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 106424 Comparison and Improvement of the Existing Cone Penetration Test Results: Shear Wave Velocity Correlations for Hungarian Soils
Authors: Ákos Wolf, Richard P. Ray
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Due to the introduction of Eurocode 8, the structural design for seismic and dynamic effects has become more significant in Hungary. This has emphasized the need for more effort to describe the behavior of structures under these conditions. Soil conditions have a significant effect on the response of structures by modifying the stiffness and damping of the soil-structural system and by modifying the seismic action as it reaches the ground surface. Shear modulus (G) and shear wave velocity (vs), which are often measured in the field, are the fundamental dynamic soil properties for foundation vibration problems, liquefaction potential and earthquake site response analysis. There are several laboratory and in-situ measurement techniques to evaluate dynamic soil properties, but unfortunately, they are often too expensive for general design practice. However, a significant number of correlations have been proposed to determine shear wave velocity or shear modulus from Cone Penetration Tests (CPT), which are used more and more in geotechnical design practice in Hungary. This allows the designer to analyze and compare CPT and seismic test result in order to select the best correlation equations for Hungarian soils and to improve the recommendations for the Hungarian geologic conditions. Based on a literature review, as well as research experience in Hungary, the influence of various parameters on the accuracy of results will be shown. This study can serve as a basis for selecting and modifying correlation equations for Hungarian soils. Test data are taken from seven locations in Hungary with similar geologic conditions. The shear wave velocity values were measured by seismic CPT. Several factors are analyzed including soil type, behavior index, measurement depth, geologic age etc. for their effect on the accuracy of predictions. The final results show an improved prediction method for Hungarian soils
Keywords: CPT correlation, dynamic soil properties, seismic CPT, shear wave velocity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 115523 Spatial Clustering Model of Vessel Trajectory to Extract Sailing Routes Based on AIS Data
Authors: Lubna Eljabu, Mohammad Etemad, Stan Matwin
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The automatic extraction of shipping routes is advantageous for intelligent traffic management systems to identify events and support decision-making in maritime surveillance. At present, there is a high demand for the extraction of maritime traffic networks that resemble the real traffic of vessels accurately, which is valuable for further analytical processing tasks for vessels trajectories (e.g., naval routing and voyage planning, anomaly detection, destination prediction, time of arrival estimation). With the help of big data and processing huge amounts of vessels’ trajectory data, it is possible to learn these shipping routes from the navigation history of past behaviour of other, similar ships that were travelling in a given area. In this paper, we propose a spatial clustering model of vessels’ trajectories (SPTCLUST) to extract spatial representations of sailing routes from historical Automatic Identification System (AIS) data. The whole model consists of three main parts: data preprocessing, path finding, and route extraction, which consists of clustering and representative trajectory extraction. The proposed clustering method provides techniques to overcome the problems of: (i) optimal input parameters selection; (ii) the high complexity of processing a huge volume of multidimensional data; (iii) and the spatial representation of complete representative trajectory detection in the context of trajectory clustering algorithms. The experimental evaluation showed the effectiveness of the proposed model by using a real-world AIS dataset from the Port of Halifax. The results contribute to further understanding of shipping route patterns. This could aid surveillance authorities in stable and sustainable vessel traffic management.
Keywords: Vessel trajectory clustering, trajectory mining, Spatial Clustering, marine intelligent navigation, maritime traffic network extraction, sdailing routes extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 42222 Bidirectional Pendulum Vibration Absorbers with Homogeneous Variable Tangential Friction: Modelling and Design
Authors: Emiliano Matta
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Passive resonant vibration absorbers are among the most widely used dynamic control systems in civil engineering. They typically consist in a single-degree-of-freedom mechanical appendage of the main structure, tuned to one structural target mode through frequency and damping optimization. One classical scheme is the pendulum absorber, whose mass is constrained to move along a curved trajectory and is damped by viscous dashpots. Even though the principle is well known, the search for improved arrangements is still under way. In recent years this investigation inspired a type of bidirectional pendulum absorber (BPA), consisting of a mass constrained to move along an optimal three-dimensional (3D) concave surface. For such a BPA, the surface principal curvatures are designed to ensure a bidirectional tuning of the absorber to both principal modes of the main structure, while damping is produced either by horizontal viscous dashpots or by vertical friction dashpots, connecting the BPA to the main structure. In this paper, a variant of BPA is proposed, where damping originates from the variable tangential friction force which develops between the pendulum mass and the 3D surface as a result of a spatially-varying friction coefficient pattern. Namely, a friction coefficient is proposed that varies along the pendulum surface in proportion to the modulus of the 3D surface gradient. With such an assumption, the dissipative model of the absorber can be proven to be nonlinear homogeneous in the small displacement domain. The resulting homogeneous BPA (HBPA) has a fundamental advantage over conventional friction-type absorbers, because its equivalent damping ratio results independent on the amplitude of oscillations, and therefore its optimal performance does not depend on the excitation level. On the other hand, the HBPA is more compact than viscously damped BPAs because it does not need the installation of dampers. This paper presents the analytical model of the HBPA and an optimal methodology for its design. Numerical simulations of single- and multi-story building structures under wind and earthquake loads are presented to compare the HBPA with classical viscously damped BPAs. It is shown that the HBPA is a promising alternative to existing BPA types and that homogeneous tangential friction is an effective means to realize systems provided with amplitude-independent damping.
Keywords: Amplitude-independent damping, Homogeneous friction, Pendulum nonlinear dynamics, Structural control, Vibration resonant absorbers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 72021 The Extent of Land Use Externalities in the Fringe of Jakarta Metropolitan: An Application of Spatial Panel Dynamic Land Value Model
Authors: Rahma Fitriani, Eni Sumarminingsih, Suci Astutik
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In a fast growing region, conversion of agricultural lands which are surrounded by some new development sites will occur sooner than expected. This phenomenon has been experienced by many regions in Indonesia, especially the fringe of Jakarta (BoDeTaBek). Being Indonesia’s capital city, rapid conversion of land in this area is an unavoidable process. The land conversion expands spatially into the fringe regions, which were initially dominated by agricultural land or conservation sites. Without proper control or growth management, this activity will invite greater costs than benefits. The current land use is the use which maximizes its value. In order to maintain land for agricultural activity or conservation, some efforts are needed to keep the land value of this activity as high as possible. In this case, the knowledge regarding the functional relationship between land value and its driving forces is necessary. In a fast growing region, development externalities are the assumed dominant driving force. Land value is the product of the past decision of its use leading to its value. It is also affected by the local characteristics and the observed surrounded land use (externalities) from the previous period. The effect of each factor on land value has dynamic and spatial virtues; an empirical spatial dynamic land value model will be more useful to capture them. The model will be useful to test and to estimate the extent of land use externalities on land value in the short run as well as in the long run. It serves as a basis to formulate an effective urban growth management’s policy. This study will apply the model to the case of land value in the fringe of Jakarta Metropolitan. The model will be used further to predict the effect of externalities on land value, in the form of prediction map. For the case of Jakarta’s fringe, there is some evidence about the significance of neighborhood urban activity – negative externalities, the previous land value and local accessibility on land value. The effects are accumulated dynamically over years, but they will fully affect the land value after six years.
Keywords: Growth management, land use externalities, land value, spatial panel dynamic.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 99020 Prediction of Seismic Damage Using Scalar Intensity Measures Based On Integration of Spectral Values
Authors: Konstantinos G. Kostinakis, Asimina M. Athanatopoulou
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A key issue in seismic risk analysis within the context of Performance-Based Earthquake Engineering is the evaluation of the expected seismic damage of structures under a specific earthquake ground motion. The assessment of the seismic performance strongly depends on the choice of the seismic Intensity Measure (IM), which quantifies the characteristics of a ground motion that are important to the nonlinear structural response. Several conventional IMs of ground motion have been used to estimate their damage potential to structures. Yet, none of them has been proved to be able to predict adequately the seismic damage. Therefore, alternative, scalar intensity measures, which take into account not only ground motion characteristics but also structural information have been proposed. Some of these IMs are based on integration of spectral values over a range of periods, in an attempt to account for the information that the shape of the acceleration, velocity or displacement spectrum provides. The adequacy of a number of these IMs in predicting the structural damage of 3D R/C buildings is investigated in the present paper. The investigated IMs, some of which are structure specific and some are non structure-specific, are defined via integration of spectral values. To achieve this purpose three symmetric in plan R/C buildings are studied. The buildings are subjected to 59 bidirectional earthquake ground motions. The two horizontal accelerograms of each ground motion are applied along the structural axes. The response is determined by nonlinear time history analysis. The structural damage is expressed in terms of the maximum interstory drift as well as the overall structural damage index. The values of the aforementioned seismic damage measures are correlated with seven scalar ground motion IMs. The comparative assessment of the results revealed that the structure-specific IMs present higher correlation with the seismic damage of the three buildings. However, the adequacy of the IMs for estimation of the structural damage depends on the response parameter adopted. Furthermore, it was confirmed that the widely used spectral acceleration at the fundamental period of the structure is a good indicator of the expected earthquake damage level.
Keywords: Damage measures, Bidirectional excitation, Spectral based IMs, R/C buildings.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 237119 A Grid Synchronization Method Based on Adaptive Notch Filter for SPV System with Modified MPPT
Authors: Priyanka Chaudhary, M. Rizwan
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This paper presents a grid synchronization technique based on adaptive notch filter for SPV (Solar Photovoltaic) system along with MPPT (Maximum Power Point Tracking) techniques. An efficient grid synchronization technique offers proficient detection of various components of grid signal like phase and frequency. It also acts as a barrier for harmonics and other disturbances in grid signal. A reference phase signal synchronized with the grid voltage is provided by the grid synchronization technique to standardize the system with grid codes and power quality standards. Hence, grid synchronization unit plays important role for grid connected SPV systems. As the output of the PV array is fluctuating in nature with the meteorological parameters like irradiance, temperature, wind etc. In order to maintain a constant DC voltage at VSC (Voltage Source Converter) input, MPPT control is required to track the maximum power point from PV array. In this work, a variable step size P & O (Perturb and Observe) MPPT technique with DC/DC boost converter has been used at first stage of the system. This algorithm divides the dPpv/dVpv curve of PV panel into three separate zones i.e. zone 0, zone 1 and zone 2. A fine value of tracking step size is used in zone 0 while zone 1 and zone 2 requires a large value of step size in order to obtain a high tracking speed. Further, adaptive notch filter based control technique is proposed for VSC in PV generation system. Adaptive notch filter (ANF) approach is used to synchronize the interfaced PV system with grid to maintain the amplitude, phase and frequency parameters as well as power quality improvement. This technique offers the compensation of harmonics current and reactive power with both linear and nonlinear loads. To maintain constant DC link voltage a PI controller is also implemented and presented in this paper. The complete system has been designed, developed and simulated using SimPower System and Simulink toolbox of MATLAB. The performance analysis of three phase grid connected solar photovoltaic system has been carried out on the basis of various parameters like PV output power, PV voltage, PV current, DC link voltage, PCC (Point of Common Coupling) voltage, grid voltage, grid current, voltage source converter current, power supplied by the voltage source converter etc. The results obtained from the proposed system are found satisfactory.
Keywords: Solar photovoltaic systems, MPPT, voltage source converter, grid synchronization technique.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 196018 A Study on the Differential Diagnostic Model for Newborn Hearing Loss Screening
Authors: Chun-Lang Chang
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According to the statistics, the prevalence of congenital hearing loss in Taiwan is approximately six thousandths; furthermore, one thousandths of infants have severe hearing impairment. Hearing ability during infancy has significant impact in the development of children-s oral expressions, language maturity, cognitive performance, education ability and social behaviors in the future. Although most children born with hearing impairment have sensorineural hearing loss, almost every child more or less still retains some residual hearing. If provided with a hearing aid or cochlear implant (a bionic ear) timely in addition to hearing speech training, even severely hearing-impaired children can still learn to talk. On the other hand, those who failed to be diagnosed and thus unable to begin hearing and speech rehabilitations on a timely manner might lose an important opportunity to live a complete and healthy life. Eventually, the lack of hearing and speaking ability will affect the development of both mental and physical functions, intelligence, and social adaptability. Not only will this problem result in an irreparable regret to the hearing-impaired child for the life time, but also create a heavy burden for the family and society. Therefore, it is necessary to establish a set of computer-assisted predictive model that can accurately detect and help diagnose newborn hearing loss so that early interventions can be provided timely to eliminate waste of medical resources. This study uses information from the neonatal database of the case hospital as the subjects, adopting two different analysis methods of using support vector machine (SVM) for model predictions and using logistic regression to conduct factor screening prior to model predictions in SVM to examine the results. The results indicate that prediction accuracy is as high as 96.43% when the factors are screened and selected through logistic regression. Hence, the model constructed in this study will have real help in clinical diagnosis for the physicians and actually beneficial to the early interventions of newborn hearing impairment.
Keywords: Data mining, Hearing impairment, Logistic regression analysis, Support vector machines
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 179317 Improved Computational Efficiency of Machine Learning Algorithms Based on Evaluation Metrics to Control the Spread of Coronavirus in the UK
Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick
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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.
Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14616 Entrepreneurial Intention and Social Entrepreneurship among Students in Malaysian Higher Education
Authors: Radin Siti Aishah Radin A Rahman, Norasmah Othman, Zaidatol Akmaliah Lope Pihie, Hariyaty Ab. Wahid
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The recent instability in economy was found to be influencing the situation in Malaysia whether directly or indirectly. Taking that into consideration, the government needs to find the best approach to balance its citizen’s socio-economic strata level urgently. Through education platform is among the efforts planned and acted upon for the purpose of balancing the effects of the influence, through the exposure of social entrepreneurial activity towards youth especially those in higher institution level. Armed with knowledge and skills that they gained, with the support by entrepreneurial culture and environment while in campus; indirectly, the students will lean more on making social entrepreneurship as a career option when they graduate. Following the issues of marketability and workability of current graduates that are becoming dire, research involving how far the willingness of student to create social innovation that contribute to the society without focusing solely on personal gain is relevant enough to be conducted. With that, this research is conducted with the purpose of identifying the level of entrepreneurial intention and social entrepreneurship among higher institution students in Malaysia. Stratified random sampling involves 355 undergraduate students from five public universities had been made as research respondents and data were collected through surveys. The data was then analyzed descriptively using min score and standard deviation. The study found that the entrepreneurial intention of higher education students are on moderate level, however it is the contrary for social entrepreneurship activities, where it was shown on a high level. This means that while the students only have moderate level of willingness to be a social entrepreneur, they are very committed to created social innovation through the social entrepreneurship activities conducted. The implication from this study can be contributed towards the higher institution authorities in prediction the tendency of student in becoming social entrepreneurs. Thus, the opportunities and facilities for realizing the courses related to social entrepreneurship must be created expansively so that the vision of creating as many social entrepreneurs as possible can be achieved.Keywords: Entrepreneurial intention, higher education institutions (HEIs), social entrepreneurship, social entrepreneurial activity, gender.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 325715 Case-Based Reasoning Application to Predict Geological Features at Site C Dam Construction Project
Authors: S. Behnam Malekzadeh, I. Kerr, T. Kaempffer, T. Harper, A Watson
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The Site C Hydroelectric dam is currently being constructed in north-eastern British Columbia on sub-horizontal sedimentary strata that dip approximately 15 meters from one bank of the Peace River to the other. More than 615 pressure sensors (Vibrating Wire Piezometers) have been installed on bedding planes (BPs) since construction began, with over 80 more planned before project completion. These pressure measurements are essential to monitor the stability of the rock foundation during and after construction and for dam safety purposes. BPs are identified by their clay gouge infilling, which varies in thickness from less than 1 to 20 mm and can be challenging to identify as the core drilling process often disturbs or washes away the gouge material. Without the use of depth predictions from nearby boreholes, stratigraphic markers, and downhole geophysical data, it is difficult to confidently identify BP targets for the sensors. In this paper, a Case-Based Reasoning (CBR) method was used to develop an empirical model called the Bedding Plane Elevation Prediction (BPEP) to help geologists and geotechnical engineers to predict geological features and BPs at new locations in a fast and accurate manner. To develop CBR, a database was developed based on 64 pressure sensors already installed on key bedding planes BP25, BP28, and BP31 on the Right Bank, including BP elevations and coordinates. 13 (20%) of the most recent cases were selected to validate and evaluate the accuracy of the developed model, while the similarity was defined as the distance between previous cases and recent cases to predict the depth of significant BPs. The average difference between actual BP elevations and predicted elevations for above BPs was ± 55 cm, while the actual results showed that 69% of predicted elevations were within ± 79 cm of actual BP elevations while 100% of predicted elevations for new cases were within ± 99 cm range. Eventually, the actual results will be used to develop the database and improve BPEP to perform as a learning machine to predict more accurate BP elevations for future sensor installations.
Keywords: Case-Based Reasoning, CBR, geological feature, geology, piezometer, pressure sensor, core logging, dam construction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17514 On-Line Geometrical Identification of Reconfigurable Machine Tool using Virtual Machining
Authors: Alexandru Epureanu, Virgil Teodor
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One of the main research directions in CAD/CAM machining area is the reducing of machining time. The feedrate scheduling is one of the advanced techniques that allows keeping constant the uncut chip area and as sequel to keep constant the main cutting force. They are two main ways for feedrate optimization. The first consists in the cutting force monitoring, which presumes to use complex equipment for the force measurement and after this, to set the feedrate regarding the cutting force variation. The second way is to optimize the feedrate by keeping constant the material removal rate regarding the cutting conditions. In this paper there is proposed a new approach using an extended database that replaces the system model. The feedrate scheduling is determined based on the identification of the reconfigurable machine tool, and the feed value determination regarding the uncut chip section area, the contact length between tool and blank and also regarding the geometrical roughness. The first stage consists in the blank and tool monitoring for the determination of actual profiles. The next stage is the determination of programmed tool path that allows obtaining the piece target profile. The graphic representation environment models the tool and blank regions and, after this, the tool model is positioned regarding the blank model according to the programmed tool path. For each of these positions the geometrical roughness value, the uncut chip area and the contact length between tool and blank are calculated. Each of these parameters are compared with the admissible values and according to the result the feed value is established. We can consider that this approach has the following advantages: in case of complex cutting processes the prediction of cutting force is possible; there is considered the real cutting profile which has deviations from the theoretical profile; the blank-tool contact length limitation is possible; it is possible to correct the programmed tool path so that the target profile can be obtained. Applying this method, there are obtained data sets which allow the feedrate scheduling so that the uncut chip area is constant and, as a result, the cutting force is constant, which allows to use more efficiently the machine tool and to obtain the reduction of machining time.Keywords: Reconfigurable machine tool, system identification, uncut chip area, cutting conditions scheduling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 144113 Development and Validation of Cylindrical Linear Oscillating Generator
Authors: Sungin Jeong
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This paper presents a linear oscillating generator of cylindrical type for hybrid electric vehicle application. The focus of the study is the suggestion of the optimal model and the design rule of the cylindrical linear oscillating generator with permanent magnet in the back-iron translator. The cylindrical topology is achieved using equivalent magnetic circuit considering leakage elements as initial modeling. This topology with permanent magnet in the back-iron translator is described by number of phases and displacement of stroke. For more accurate analysis of an oscillating machine, it will be compared by moving just one-pole pitch forward and backward the thrust of single-phase system and three-phase system. Through the analysis and comparison, a single-phase system of cylindrical topology as the optimal topology is selected. Finally, the detailed design of the optimal topology takes the magnetic saturation effects into account by finite element analysis. Besides, the losses are examined to obtain more accurate results; copper loss in the conductors of machine windings, eddy-current loss of permanent magnet, and iron-loss of specific material of electrical steel. The considerations of thermal performances and mechanical robustness are essential, because they have an effect on the entire efficiency and the insulations of the machine due to the losses of the high temperature generated in each region of the generator. Besides electric machine with linear oscillating movement requires a support system that can resist dynamic forces and mechanical masses. As a result, the fatigue analysis of shaft is achieved by the kinetic equations. Also, the thermal characteristics are analyzed by the operating frequency in each region. The results of this study will give a very important design rule in the design of linear oscillating machines. It enables us to more accurate machine design and more accurate prediction of machine performances.
Keywords: Equivalent magnetic circuit, finite element analysis, hybrid electric vehicle, free piston engine, cylindrical linear oscillating generator
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1352