Search results for: model driven rrchitecture (MDA)

Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

Abstract:

Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

Procedia PDF Downloads 131

Authors: Badiy Elmabrouk, Abdelhamid Boujarif, Zhiguo Zeng, Stephane Borrel, Robert Heidsieck

Abstract:

In an era where sustainability is paramount, this paper introduces a machine learning-driven testing protocol to accurately predict diode failures, merging reliability engineering with failure physics to enhance repair operations efficiency. Our approach refines the burn-in process, significantly curtailing its duration, which not only conserves energy but also elevates productivity and mitigates component wear. A case study from GE HealthCare’s repair center vividly demonstrates the method’s effectiveness, recording a high prediction of diode failures and a substantial decrease in energy consumption that translates to an annual reduction of 6.5 Tons of CO2 emissions. This advancement sets a benchmark for environmentally conscious practices in the electronics repair sector.

Keywords: maintenance, burn-in, failure physics, reliability testing

Procedia PDF Downloads 68

Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

Abstract:

In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

Procedia PDF Downloads 146

Authors: Mechlouch Ridha Fethi

Abstract:

This paper presents a recent mathematical model in political sciences concerning democracy. The model is represented by a logarithmic equation linking the Relative Index of Democracy (RID) to Participation Ratio (PR). Firstly the meanings of the different parameters of the model were presented; and the variation curve of the RID according to PR with different critical areas was discussed. Secondly, the model was applied to a virtual group where we show that the model can be applied depending on the gender. Thirdly, it was observed that the model can be extended to different language models of democracy and that little use to assess the state of democracy for some International organizations like UNO.

Keywords: democracy, mathematic, modelization, quantification

Procedia PDF Downloads 368

Authors: Glebert C. Dadol, Camila Flor Y. Lobarbio, Noel Peter B. Tan

Abstract:

Water scarcity is a global problem. One of the promising solutions to this challenge is the use of membrane-based desalination technologies. In this study, a passive solar-driven membrane (PSDM) distillation was employed to test its desalination performance. The PSDM was fabricated using a TiNOX sheet solar absorber, cellulose-based hydrophilic top and bottom layers, and a middle layer. The effects of the middle layer material and thickness on the desalination performance in terms of distillate flow rate, productivity, and salinity were investigated. An air-gap screen mesh (2 mm, 4 mm, 6 mm thickness) and a hydrophobic PTFE membrane (0.3 mm thickness) were used as middle-layer materials. Saltwater input (35 g/L NaCl) was used for the PSDM distiller on a rooftop setting at the University of San Carlos, Cebu City, Philippines. The highest distillate flow rate and productivity of 1.08 L/m²-h and 1.47 L/kWh, respectively, were achieved using a 2 mm air-gap middle layer, but it also resulted in a high salinity of 25.20 g/L. Increasing the air gap lowered the salinity but also decreased the flow rate and productivity. The lowest salinity of 1.07 g/L was achieved using 6 mm air gap, but the flow rate and productivity were reduced to 0.08 L/m²-h and 0.17 L/kWh, respectively. The use of a hydrophobic PTFE membrane, on the other hand, did not offer a significant improvement in its performance. A PDSM distiller with a thick air gap as the middle layer can deliver a distillate with low salinity and is preferred over a thin hydrophobic PTFE membrane. Various modifications and optimizations to the distiller can be done to improve its performance further.

Keywords: desalination, membrane distillation, passive solar-driven membrane distiller, solar distillation

Procedia PDF Downloads 123

Authors: Ziwei Xu

Abstract:

The increasing emphasis on mobility has driven the application of innovative communication technologies across various industries. In the retail sector, Near Field Communication (NFC) has emerged as a significant and transformative technology, particularly in the payment and retail supermarket sectors. NFC enables new payment methods, such as electronic wallets, and enhances information management in supermarkets, contributing to the growth of the trade. This report presents a comprehensive analysis of NFC technology, focusing on five key aspects. Firstly, it provides an overview of NFC, including its application methods and development history. Additionally, it incorporates Arthur's work on combinatorial evolution to elucidate the emergence and impact of NFC technology, while acknowledging the limitations of the model in analyzing NFC. The report then summarizes the positive influence of NFC on the retail industry along with its associated constraints. Furthermore, it explores the adoption of NFC from both organizational and individual perspectives, employing the Best Predictors of organizational IT adoption and UTAUT2 models, respectively. Finally, the report discusses the potential future replacement of NFC with biometrics technology, highlighting its advantages over NFC and leveraging Arthur's model to investigate its future development prospects.

Keywords: innovation, NFC, industry, biometrics

Procedia PDF Downloads 75

Authors: Le Kang

Abstract:

On the research question of 'how to achieve USR', this contribution reflects the concept of university social responsibility, identify three achievement models of USR as the society - diversified model, the university-cooperation model, the government - compound model, also conduct a case study to explore characteristics of Chinese achievement model of USR. The contribution concludes with discussion of how the university, government and society balance demands and roles, make necessarily strategic adjustment and innovative approach to repair the shortcomings of each achievement model.

Keywords: modern university, USR, achievement model, compound model

Procedia PDF Downloads 756

Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 130

Authors: Jackson Simon Lusagalika, Lai Hsin-Chih, Dai Yu-Tung

Abstract:

Particles with an aerodynamic diameter of 2.5 meters or less are referred to as "fine particulate matter" (PM₂.₅) are easily inhaled and can go deeper into the lungs than other particles in the atmosphere, where it may have detrimental health consequences. In this study, we use a hybrid model that combined CMAQ and AERMOD as well as initial meteorological fields from the Weather Research and Forecasting (WRF) model to study the impact of power plant PM₂.₅ emissions in South Taiwan since it frequently experiences higher PM₂.₅ levels. A specific date of March 3, 2022, was chosen as a result of a power outage that prompted the bulk of power plants to shut down. In some way, it is not conceivable anywhere in the world to turn off the power for the sole purpose of doing research. Therefore, this catastrophe involving a power outage and the shutdown of power plants offers a great occasion to evaluate the impact of air pollution driven by this power sector. As a result, four numerical experiments were conducted in the study using the Continuous Emission Data System (CEMS), assuming that the power plants continued to function normally after the power outage. The hybrid model results revealed that power plants have a minor impact in the study region. However, we examined the accumulation of PM₂.₅ in the study and discovered that once the vortex at 925hPa was established and moved to the north of Taiwan's coast, the study region experienced higher observed PM₂.₅ concentrations influenced by meteorological factors. This study recommends that decision-makers take into account not only control techniques, specifically emission reductions, but also the atmospheric and meteorological implications for future investigations.

Keywords: PM₂.₅ concentration, powerplants, hybrid air quality model, CEMS, Vorticity

Procedia PDF Downloads 76

Authors: Carlos Olave Lopez de Ayala

Abstract:

The concept of purpose has generated a remarkable interest as a subject of study by the scientific community in recent years. However, most authors have studied it from an organizational point of view. Concepts such as purpose-driven organization and purpose management have been the focal point of numerous research studies, as well as of empirical implementation in some organizations. On the other hand, theories of motivation have been traditionally focused on the purpose of an individual and have been used to refer to personal motivation. This paper aims to study the influence of organizational purpose on the dimensions of human motivations, involving the meaning that each individual gives to his/her work. The results show that the person’s alignment with the organizational purpose is connected with the meaning of work as a career, a calling, and a higher calling. This research adds to the knowledge of the impact of the organizational purpose and its influence on individuals.

Keywords: human motivations, meaningful work, organizational purpose, purpose management, purpose-driven organization

Procedia PDF Downloads 57

Authors: Mohammad Ehson Rangiha, Bill Karakostas

Abstract:

Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goal-based role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization.

Keywords: business process management, goal-based modelling, process recommendation social collaboration, social BPM

Procedia PDF Downloads 494

Authors: Sulemana Ibrahim

Abstract:

Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

Procedia PDF Downloads 62

Authors: Omid Tavasoli, Mahmoud Ghazavi

Abstract:

A numerical analysis of drivability of piles in different geometry is presented. In this paper, a three-dimensional finite difference analysis for plastic point extension and soil compaction in the effect of pile driving is analyzed. Four pile configurations such as cylindrical pile, fully tapered pile, T-C pile consists of a top tapered segment and a lower cylindrical segment and C-T pile has a top cylindrical part followed by a tapered part are investigated. All piles which driven up to a total penetration depth of 16 m have the same length with equivalent surface area and approximately with identical material volumes. An idealization for pile-soil system in pile driving is considered for this approach. A linear elastic material is assumed to model the vertical pile behaviors and the soil obeys the elasto-plastic constitutive low and its failure is controlled by the Mohr-Coulomb failure criterion. A slip which occurred at the pile-soil contact surfaces along the shaft and the toe in pile driving procedures is simulated with interface elements. All initial and boundary conditions are the same in all analyses. Quiet boundaries are used to prevent wave reflection in the lateral and vertical directions for the soil. The results obtained from numerical analyses were compared with available other numerical data and laboratory tests, indicating a satisfactory agreement. It will be shown that with increasing the angle of taper, the permanent piles toe settlement increase and therefore, the extension of plastic points increase. These are interesting phenomena in pile driving and are on the safe side for driven piles.

Keywords: pile driving, finite difference method, non-uniform piles, pile geometry, pile set, plastic points, soil compaction

Procedia PDF Downloads 484

Authors: Redha Elhuni, M. Munir Ahmad

Abstract:

The main purpose of this research is to construct a generic model for successful implementation of Total Quality Management (TQM) in oil sector, and to find out the effects of this model on the organizational sustainability development (OSD) performance of Libyan oil and gas companies using the structured equation modeling (SEM) approach. The research approach covers both quantitative and qualitative methods. A questionnaire was developed in order to identify the quality factors that are seen by Libyan oil and gas companies to be critical to the success of TQM implementation. Hypotheses were developed to evaluate the impact of TQM implementation on O SD. Data analysis reveals that there is a significant positive effect of the TQM implementation on OSD. 24 quality factors are found to be critical and absolutely essential for successful TQM implementation. The results generated a structure of the TQMSD implementation framework based on the four major road map constructs (Top management commitment, employee involvement and participation, customer-driven processes, and continuous improvement culture).

Keywords: total quality management, critical success factors, oil and gas, organizational sustainability development (SD), Libya

Procedia PDF Downloads 273

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

Abstract:

Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

Procedia PDF Downloads 59

Authors: Dangut Maren David, Skaf Zakwan

Abstract:

Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

Procedia PDF Downloads 174

Authors: Vahid Nourani, Atefeh Ashrafi

Abstract:

Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

Procedia PDF Downloads 128

Authors: A. Aulinger, A. M. Backes, J. Bieser, V. Matthias, M. Quante

Abstract:

High concentrations of particles pose a threat to human health. Thus, legal maximum concentrations of PM10 and PM2.5 in ambient air have been steadily decreased over the years. In central Europe, the inorganic species ammonium sulphate and ammonium nitrate make up a large fraction of fine particles. Many studies investigate the influence of emission reductions of sulfur- and nitrogen oxides on aerosol concentration. Here, we focus on the influence of ammonia (NH3) emissions. While emissions of sulphate and nitrogen oxides are quite well known, ammonia emissions are subject to high uncertainty. This is due to the uncertainty of location, amount, time of fertilizer application in agriculture, and the storage and treatment of manure from animal husbandry. For this study, we implemented a crop growth model into the SMOKE emission model. Depending on temperature, local legislation, and crop type individual temporal profiles for fertilizer and manure application are calculated for each model grid cell. Additionally, the diffusion from soils and plants and the direct release from open and closed barns are determined. The emission data was used as input for the Community Multiscale Air Quality (CMAQ) model. Comparisons to observations from the EMEP measurement network indicate that the new ammonia emission module leads to a better agreement of model and observation (for both ammonia and ammonium). Finally, the ammonia emission model was used to create emission scenarios. This includes emissions based on future European legislation, as well as a dynamic evaluation of the influence of different agricultural sectors on particle formation. It was found that a reduction of ammonia emissions by 50% lead to a 24% reduction of total PM2.5 concentrations during winter time in the model domain. The observed reduction was mainly driven by reduced formation of ammonium nitrate. Moreover, emission reductions during winter had a larger impact than during the rest of the year.

Keywords: ammonia, ammonia abatement strategies, ctm, seasonal impact, secondary aerosol formation

Procedia PDF Downloads 351

Authors: Rita Emília Szabó, Róbert Polanek, Tünde Tőkés, Zoltán Szabó, Szabolcs Czifrus, Katalin Hideghéty

Abstract:

Purpose: Several feature of zebrafish are making them amenable for investigation on therapeutic approaches such as ionizing radiation. The establishment of zebrafish model for comprehensive radiobiological research stands in the focus of our investigation, comparing the radiation effect curves of neutron and photon irradiation. Our final aim is to develop an appropriate vertebrate model in order to investigate the relative biological effectiveness of laser driven ionizing radiation. Methods and Materials: After careful dosimetry series of viable zebrafish embryos were exposed to a single fraction whole-body neutron-irradiation (1,25; 1,875; 2; 2,5 Gy) at the research reactor of the Technical University of Budapest and to conventional 6 MeV photon beam at 24 hour post-fertilization (hpf). The survival and morphologic abnormalities (pericardial edema, spine curvature) of each embryo were assessed for each experiment at 24-hour intervals from the point of fertilization up to 168 hpf (defining the dose lethal for 50% (LD50)). Results: In the zebrafish embryo model LD50 at 20 Gy dose level was defined and the same lethality were found at 2 Gy dose from the reactor neutron beam resulting RBE of 10. Dose-dependent organ perturbations were detected on macroscopic (shortening of the body length, spine curvature, microcephaly, micro-ophthalmia, micrognathia, pericardial edema, and inhibition of yolk sac resorption) and microscopic (marked cellular changes in skin, cardiac, gastrointestinal system) with the same magnitude of dose difference. Conclusion: In our observations, we found that zebrafish embryo model can be used for investigating the effects of different type of ionizing radiation and this system proved to be highly efficient vertebrate model for preclinical examinations.

Keywords: ionizing radiation, LD50, relative biological effectiveness, zebrafish embryo

Procedia PDF Downloads 309

Authors: Maor Farid, Oleg Gendelman

Abstract:

The following study deals with fluid vibration of a liquid in a partially filled vessel under periodic ground excitation. This external excitation might lead to hidraulic impact applied on the vessel inner walls. In order to model these sloshing dynamic regimes, several equivalent mechanical models were suggested in the literature, such as series of pendula or mass-spring systems that are able to impact the inner tank walls. In the following study, we use the latter methodology, use parameter values documented in literature corresponding to cylindrical tanks and consider structural elasticity of the tank. The hydraulic impulses are modeled by the high-exponent potential function. Additional system parameters are found with the help of Finite-Element (FE) analysis. Model-driven stress assessment method is developed. Finally, vibration mitigation performances of both tuned mass damper (TMD) and nonlinear energy sink (NES) are examined.

Keywords: nonlinear energy sink (NES), reduced-order modelling, liquid sloshing, vibration mitigation, vibro-impact dynamics

Procedia PDF Downloads 197

Authors: Bruno Coelho Lima, Joao F.A. Martos, Paulo G. P. Toro, Israel S. Rego

Abstract:

The shock tube is a ground-facility widely used in aerospace and aeronautics science and technology for studies on gas dynamic and chemical-physical processes in gases at high-temperature, explosions and dynamic calibration of pressure sensors. A shock tube in its simplest form is comprised of two separate tubes of equal cross-section by a diaphragm. The diaphragm function is to separate the two reservoirs at different pressures. The reservoir containing high pressure is called the Driver, the low pressure reservoir is called Driven. When the diaphragm is broken by pressure difference, a normal shock wave and non-stationary (named Incident Shock Wave) will be formed in the same place of diaphragm and will get around toward the closed end of Driven. When this shock wave reaches the closer end of the Driven section will be completely reflected. Now, the shock wave will interact with the boundary layer that was created by the induced flow by incident shock wave passage. The interaction between boundary layer and shock wave force the separation of the boundary layer. The aim of this paper is to make an analysis of influences of separation of the boundary layer in the reservoir pressure in the shock tube. A comparison among CDF (Computational Fluids Dynamics), experiments test and analytical analysis were performed. For the analytical analysis, some routines in Python was created, in the numerical simulations (Computational Fluids Dynamics) was used the Ansys Fluent, and the experimental tests were used T1 shock tube located in IEAv (Institute of Advanced Studies).

Keywords: boundary layer separation, moving shock wave, shock tube, transient simulation

Procedia PDF Downloads 315

Authors: Naghmeh Zamani, Ashkan Pourkand, David Grow

Abstract:

This paper presents the design and mechanical model of a hybrid impedance/admittance haptic device optimized for applications, like bone drilling, spinal awl probe use, and other surgical techniques were high force is required in the tool-axial direction, and low impedance is needed in all other directions. The performance levels required cannot be satisfied by existing, off-the-shelf haptic devices. This design may allow critical improvements in simulator fidelity for surgery training. The device consists primarily of two low-mass (carbon fiber) plates with a rod passing through them. Collectively, the device provides 6 DOF. The rod slides through a bushing in the top plate and it is connected to the bottom plate with a universal joint, constrained to move in only 2 DOF, allowing axial torque display the user’s hand. The two parallel plates are actuated and located by means of four cables pulled by motors. The forward kinematic equations are derived to ensure that the plates orientation remains constant. The corresponding equations are solved using the Newton-Raphson method. The static force/torque equations are also presented. Finally, we present the predicted distribution of location error, cables velocity, cable tension, force and torque for the device. These results and preliminary hardware fabrication indicate that this design may provide a revolutionary approach for haptic display of many surgical procedures by means of an architecture that allows arbitrary workspace scaling. Scaling of the height and width can be scaled arbitrarily.

Keywords: cable direct driven robot, haptics, parallel plates, bone drilling

Procedia PDF Downloads 258

Authors: Zhou Jianhong

Abstract:

Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

Procedia PDF Downloads 520

Authors: Yingxu Wang, Jianhua Lu, Jun Peng, Jiawei Zhang

Abstract:

The fundamental demand in contemporary intelligent science towards Autonomous AI (AI*) is the creation of unprecedented formal means of Intelligent Mathematics (IM). It is discovered that natural intelligence is inductively created rather than exhaustively trained. Therefore, IM is a family of algebraic and denotational mathematics encompassing Inference Algebra, Real-Time Process Algebra, Concept Algebra, Semantic Algebra, Visual Frame Algebra, etc., developed in our labs. IM plays indispensable roles in training-free AI* theories and systems beyond traditional empirical data-driven technologies. A set of applications of IM-driven AI* systems will be demonstrated in contemporary intelligence science, AI*, and cognitive computers.

Keywords: intelligence mathematics, foundations of intelligent science, autonomous AI, cognitive computers, inference algebra, real-time process algebra, concept algebra, semantic algebra, applications

Procedia PDF Downloads 61

Authors: Cheng-Lung Yu, Ying-His Liao

Abstract:

Some evidence show that public art accelerates local economic growth. Even local governments award the collaboration of public-private partnership to sustain the creation of public art for urban economic development. Through the public-private partnership of public art establishment it is obvious that public construction projects have been led by the governmental policy yet the private developers have played crucial roles to drive the innovative business models such as tourism investment, real estate value up and community participation. This study shows that the types of collaboration have been driven by Taichung city governmental policy from the regulation of public art establishment in the past three years. Through some cases empirical analyzes the authors discover the trends concerning the public art development to support local economic growth in Taiwan.

Keywords: public art, public art establishment regulation, construction management, urban governance

Procedia PDF Downloads 32

Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

Abstract:

This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

Procedia PDF Downloads 73

Authors: Carole A. South-Winter

Abstract:

Research-based learning gives students the opportunity to experience problems that require critical thinking and idea development. The skills they gain in working through these problems 'hands-on,' develop into attributes that benefit their careers in the professional field. The partnerships developed between students and industries give advantages to both sides. The students gain knowledge and skills that will increase their likelihood of success in the future and the industries are given research on new advancements that will give them a competitive advantage in their given field of work. The future of these partnerships is dependent on the success of current programs, enabling the enhancement and improvement of the research efforts. Once more students can complete research, there will be an increase in reliability of the results for each industry. The overall goal is to continue the support for research-based learning and the partnerships formed between students and industries.

Keywords: global healthcare, industry partnerships, research-driven decisions, short-term study abroad

Procedia PDF Downloads 126

Authors: N. H. Z. Abidin, N. F. M. Mokhtar, S. S. A. Gani

Abstract:

The influence of diffusion of the thermal or known as Soret effect in a heated Binary fluid model with Coriolis force is investigated theoretically. The linear stability analysis is used, and the eigenvalue is obtained using the Galerkin method. The impact of the Soret and Coriolis force on the onset of stationary convection in a system is analysed with respect to various Binary fluid parameters and presented graphically. It is found that an increase of the Soret values, destabilize the Binary fluid layer system. However, elevating the values of the Coriolis force helps to lag the onset of convection in a system.

Keywords: Benard convection, binary fluid, Coriolis, Soret

Procedia PDF Downloads 386

Authors: Soufiane Benoumhani, Nadia Saifi, Boubekeur Dokkar, Mohamed Cherif Benzid

Abstract:

This work presents the design and simulation of electric equipment for a hybrid solar vehicle. The new drive train of this vehicle is a parallel hybrid system which means a vehicle driven by a great percentage of an internal combustion engine with 49.35 kW as maximal power and electric motor only as assistance when is needed. This assistance is carried out on the rear axle by a single electric motor of 7.22 kW as nominal power. The motor is driven by 12 batteries connecting in series, which are charged by three PV panels (300 W) installed on the roof and hood of the vehicle. The individual components are modeled and simulated by using the Matlab Simulink environment. The whole system is examined under different load conditions. The reduction of CO₂ emission is obtained by reducing fuel consumption. With the use of this hybrid system, fuel consumption can be reduced from 6.74 kg/h to 5.56 kg/h when the electric motor works at 100 % of its power. The net benefit of the system reaches 1.18 kg/h as fuel reduction at high values of power and torque.

Keywords: light car, hybrid system, PV panel, electric motor

Procedia PDF Downloads 121

Authors: Adel Edwar Waheeb Louka

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

Procedia PDF Downloads 73