Search results for: modeling accuracy

Authors: Akshat Kumar, Vidushi

Abstract:

This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

Procedia PDF Downloads 53

Authors: Young S. Bang, Dong H. Yoon, Seung H. Yoo

Abstract:

Emergency Core Coolant Bypass (ECC Bypass) has been regarded as an important phenomenon to peak cladding temperature of large-break loss-of-coolant-accidents (LBLOCA) in nuclear power plants (NPP). A modeling scheme to address the ECC Bypass phenomena and the calculation of LBLOCA using that scheme are discussed in the present paper. A hydraulic form loss coefficient (HFLC) from the reactor vessel downcomer to the broken cold leg is predicted by the computational fluid dynamics (CFD) code with a variation of the void fraction incoming from the downcomer. The maximum, mean, and minimum values of FLC are derived from the CFD results and are incorporated into the LBLOCA calculation using a system thermal-hydraulic code, MARS-KS. As a relevant parameter addressing the ECC Bypass phenomena, the FLC to the break and its range are proposed.

Keywords: CFD analysis, ECC bypass, hydraulic form loss coefficient, system thermal-hydraulic code

Procedia PDF Downloads 224

Authors: Zhuoran Li, Guan Qin

Abstract:

A robust and efficient compositional equilibrium characterization model for hydrate-containing systems is required, especially for time-critical simulations such as subsea pipeline flow assurance analysis, compositional simulation in hydrate reservoirs etc. A multiphase flash calculation framework, which combines Gibbs energy minimization function and cubic plus association (CPA) EoS, is developed to describe the highly non-ideal phase behavior of hydrate-containing systems. A non-iterative eigenvalue problem-solving approach for the trust-region sub-problem is selected to guarantee efficiency. The developed flash model is based on the state-of-the-art objective function proposed by Michelsen to minimize the Gibbs energy of the multiphase system. It is conceivable that a hydrate-containing system always contains polar components (such as water and hydrate inhibitors), introducing hydrogen bonds to influence phase behavior. Thus, the cubic plus associating (CPA) EoS is utilized to compute the thermodynamic parameters. The solid solution theory proposed by van der Waals and Platteeuw is applied to represent hydrate phase parameters. The trust-region method combined with the trust-region sub-problem non-iterative eigenvalue problem-solving approach is utilized to ensure fast convergence. The developed multiphase flash model's accuracy performance is validated by three available models (one published and two commercial models). Hundreds of published hydrate-containing system equilibrium experimental data are collected to act as the standard group for the accuracy test. The accuracy comparing results show that our model has superior performances over two models and comparable calculation accuracy to CSMGem. Efficiency performance test also has been carried out. Because the trust-region method can determine the optimization step's direction and size simultaneously, fast solution progress can be obtained. The comparison results show that less iteration number is needed to optimize the objective function by utilizing trust-region methods than applying line search methods. The non-iterative eigenvalue problem approach also performs faster computation speed than the conventional iterative solving algorithm for the trust-region sub-problem, further improving the calculation efficiency. A new thermodynamic framework of the multiphase flash model for the hydrate-containing system has been constructed in this work. Sensitive analysis and numerical experiments have been carried out to prove the accuracy and efficiency of this model. Furthermore, based on the current thermodynamic model in the oil and gas industry, implementing this model is simple.

Keywords: equation of state, hydrates, multiphase equilibrium, trust-region method

Procedia PDF Downloads 162

Authors: Francesco Morgan Bono, Simone Cinquemani

Abstract:

This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

Procedia PDF Downloads 25

Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

Abstract:

The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

Procedia PDF Downloads 95

Authors: Fazlul Karim, Esa Al-Islam

Abstract:

Many problems in oceanography and environmental sciences require the solution of shallow water equations on physical domains having curvilinear coastlines and abrupt changes of ocean depth near the shore. Finite-difference technique for the shallow water equations representing the boundary as stair step may give inaccurate results near the coastline where results are of greatest interest for various applications. This suggests the use of methods which are capable of incorporating the irregular boundary in coastal belts. At the same time, large velocity gradient is expected near the beach and islands as water depth vary abruptly near the coast. A nested numerical scheme with fine resolution is the best resort to enhance the numerical accuracy with the least grid numbers for the region of interests where the velocity changes rapidly and which is unnecessary for the away of the region. This paper describes the development of a boundary fitted nested grid (BFNG) model to compute tsunami propagation of 2004 Indonesian tsunami in Southern Thailand coastal waters. In this paper, we develop a numerical model employing the shallow water nested model and an orthogonal boundary fitted grid to investigate the tsunami impact on the Southern Thailand due to the Indonesian tsunami of 2004. Comparisons of water surface elevation obtained from numerical simulations and field measurements are made.

Keywords: Indonesian tsunami of 2004, Boundary-fitted nested grid model, Southern Thailand, finite difference method

Procedia PDF Downloads 435

Authors: Iftekhar Hassan, Sayedil Morsalin, Easir A Khan

Abstract:

Accidents involving fire occur frequently in recent times and their causes showing a great deal of variety which require intervention methods and risk assessment strategies are unique in each case. On September 4, 2020, a fire and explosion occurred in a confined space caused by a methane gas leak from an underground pipeline in Baitus Salat Jame mosque during Night (Esha) prayer in Narayanganj District, Bangladesh that killed 34 people. In this research, this incident is simulated using Fire Dynamics Simulator (FDS) software to analyze and understand the nature of the accident and associated consequences. FDS is an advanced computational fluid dynamics (CFD) system of fire-driven fluid flow which solves numerically a large eddy simulation form of the Navier–Stokes’s equations for simulation of the fire and smoke spread and prediction of thermal radiation, toxic substances concentrations and other relevant parameters of fire. This study focuses on understanding the nature of the fire and consequence evaluation due to thermal radiation caused by vapor cloud explosion. An evacuation modeling was constructed to visualize the effect of evacuation time and fractional effective dose (FED) for different types of agents. The results were presented by 3D animation, sliced pictures and graphical representation to understand fire hazards caused by thermal radiation or smoke due to vapor cloud explosion. This study will help to design and develop appropriate respond strategy for preventing similar accidents.

Keywords: consequence modeling, fire and explosion, fire dynamics simulation (FDS), thermal radiation

Procedia PDF Downloads 213

Authors: Meng Su

Abstract:

High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

Procedia PDF Downloads 91

Authors: Alibakhsh Kasaeian, Mohammad Sameti, Zahra Noori, Mona Rastgoo Bahambari

Abstract:

Nowadays, the parabolic trough solar collector technology has become the most promising large-scale technology among various solar thermal generations. In this paper, a detailed numerical heat transfer model for a parabolic trough collector with nanofluid is presented based on the finite difference approach for which a MATLAB code was developed. The model was used to simulate the performance of a parabolic trough solar collector’s linear receiver, called a heat collector element (HCE). In this model, the heat collector element of the receiver was discretized into several segments in axial directions and energy balances were used for each control volume. All the heat transfer correlations, the thermodynamic equations and the optical properties were considered in details and the set of algebraic equations were solved simultaneously using iterative numerical solutions. The modeling assumptions and limitations are also discussed, along with recommendations for model improvement.

Keywords: heat transfer, nanofluid, numerical analysis, trough

Procedia PDF Downloads 363

Authors: Zhidong Zhang, Zhi-Chao Zhang

Abstract:

This study examines adolescent psychological and behavioral problems. The Achenbach systems of empirically based assessment (ASEBA) were used as the instrument. The problem framework consists of internal, external and social behavioral problems which are theoretically developed based on about 113 items plus relevant background variables. In this study, the sample consist of 1,975 sixth and seventh grade students in Northeast China. Stratified random sampling method was used to collect the data, meaning that samples were from different school districts, schools, and classes. The researchers looked at both macro and micro effect. Therefore, multilevel analysis techniques were used in the data analysis. The parts of the research results indicated that the background variables such as extracurricular activities were directly related to students’ internal problems.

Keywords: behavioral problems, anxious/depressed problems, internalizing problems, mental health, under-represented groups, empirically-based assessment, hierarchical modeling, ASEBA, multilevel analysis

Procedia PDF Downloads 593

Authors: Haripriya Chakraborty

Abstract:

The healthcare system in the United States is considered to be one of the most inefficient and expensive systems when compared to other developed countries. Consequently, there are persistent concerns regarding the overall functioning of this system. For instance, the large number of uninsured individuals and high premiums are pressing issues that are shown to have a negative effect on health outcomes with possible life-threatening consequences. The Affordable Care Act (ACA), which was signed into law in 2010, was aimed at improving some of these inefficiencies. This paper aims at providing a computational mechanism to examine some of these inefficiencies and the effects that policy proposals may have on reducing these inefficiencies. Agent-based modeling is an invaluable tool that provides a flexible framework to model complex systems. It can provide an important perspective into the nature of some interactions that occur and how the benefits of these interactions are allocated. In this paper, we propose a novel and versatile agent-based model with realistic assumptions to simulate the dynamics of a health insurance marketplace that contains a mixture of private and public insurers and individuals. We use this model to analyze the characteristics, motivations, payoffs, and strategies of these agents. In addition, we examine the effects of certain policies, including some of the provisions of the ACA, aimed at reducing the uninsured rate and the cost of premiums to move closer to a system that is more equitable and improves health outcomes for the general population. Our test results confirm the usefulness of our agent-based model in studying this complicated issue and suggest some implications for public policies aimed at healthcare reform.

Keywords: agent-based modeling, healthcare reform, insurance markets, public policy

Procedia PDF Downloads 127

Authors: Khalid Abdelghany

Abstract:

Additive Manufacturing (AM) is the manufacturing technology that is used to fabricate fast products direct from CAD models in very short time and with minimum operation steps. Jointly with the advancement in medical computer modeling, AM proved to be a very efficient tool to help physicians, orthopedic surgeons and dentists design and fabricate patient-tailored surgical guides, templates and customized implants from the patient’s CT / MRI images. AM jointly with computer-assisted designing/computer-assisted manufacturing (CAD/CAM) technology have enabled medical practitioners to tailor physical models in a patient-and purpose-specific fashion and helped to design and manufacture of templates, appliances and devices with a high range of accuracy using biocompatible materials. In developing countries, there are some technical and financial limitations of implementing such advanced tools as an essential portion of medical applications. CMRDI institute in Egypt has been working in the field of Medical Additive Manufacturing since 2003 and has assisted in the recovery of hundreds of poor patients using these advanced tools. This paper focuses on the surgical and dental use of 3D printing technology in Egypt as a developing country. The presented case studies have been designed and processed using the software tools and additive manufacturing machines in CMRDI through cooperative engineering and medical works. Results showed that the implementation of the additive manufacturing tools in developed countries is successful and could be economical comparing to long treatment plans.

Keywords: additive manufacturing, dental and orthopeadic stents, patient specific surgical tools, titanium implants

Procedia PDF Downloads 306

Authors: Hugo Daneluzzo, Christelle Rabbat, Alan Jean-Marie

Abstract:

Water electrolysis is one of the most advanced technologies for producing hydrogen and can be easily combined with electricity from different sources. Under the influence of electric current, water molecules can be split into oxygen and hydrogen. The production of hydrogen by water electrolysis favors the integration of renewable energy sources into the energy mix by compensating for their intermittence through the storage of the energy produced when production exceeds demand and its release during off-peak production periods. Among the various electrolysis technologies, anion exchange membrane (AEM) electrolyser cells are emerging as a reliable technology for water electrolysis. Modeling and simulation are effective tools to save time, money, and effort during the optimization of operating conditions and the investigation of the design. The modeling and simulation become even more important when dealing with multiphysics dynamic systems. One of those systems is the AEM electrolysis cell involving complex physico-chemical reactions. Once developed, models may be utilized to comprehend the mechanisms to control and detect flaws in the systems. Several modeling methods have been initiated by scientists. These methods can be separated into two main approaches, namely equation-based modeling and graph-based modeling. The former approach is less user-friendly and difficult to update as it is based on ordinary or partial differential equations to represent the systems. However, the latter approach is more user-friendly and allows a clear representation of physical phenomena. In this case, the system is depicted by connecting subsystems, so-called blocks, through ports based on their physical interactions, hence being suitable for multiphysics systems. Among the graphical modelling methods, the bond graph is receiving increasing attention as being domain-independent and relying on the energy exchange between the components of the system. At present, few studies have investigated the modelling of AEM systems. A mathematical model and a bond graph model were used in previous studies to model the electrolysis cell performance. In this study, experimental data from literature were simulated using OpenModelica using bond graphs and mathematical approaches. The polarization curves at different operating conditions obtained by both approaches were compared with experimental ones. It was stated that both models predicted satisfactorily the polarization curves with error margins lower than 2% for equation-based models and lower than 5% for the bond graph model. The activation polarization of hydrogen evolution reactions (HER) and oxygen evolution reactions (OER) were behind the voltage loss in the AEM electrolyzer, whereas ion conduction through the membrane resulted in the ohmic loss. Therefore, highly active electro-catalysts are required for both HER and OER while high-conductivity AEMs are needed for effectively lowering the ohmic losses. The bond graph simulation of the polarisation curve for operating conditions at various temperatures has illustrated that voltage increases with temperature owing to the technology of the membrane. Simulation of the polarisation curve can be tested virtually, hence resulting in reduced cost and time involved due to experimental testing and improved design optimization. Further improvements can be made by implementing the bond graph model in a real power-to-gas-to-power scenario.

Keywords: hydrogen production, anion-exchange membrane, electrolyzer, mathematical modeling, multiphysics modeling

Procedia PDF Downloads 77

Authors: M. H. Aissa, Amar Bouzid Dj

Abstract:

As known, many countries are now planning to build new wind farms with high capacity up to 5MW. Consequently, the size of the foundation increase. These kinds of structures are subject to fatigue damage from environmental loading mainly due to wind and waves as well as from cyclic loading imposed through the rotational frequency (1P) through mass and aerodynamic imbalances and from the blade passing frequency (3P) of the wind turbine which make them behavior dynamically very sensitive. That is why natural frequency must be determined with accuracy from the existing data of the soil and the foundation stiffness sources of uncertainties, to avoid the resonance of the system. This paper presents analytical expressions of stiffness foundation with large diameter in linear soil behavior in different soil stiffness profile. To check the accuracy of the proposed formulas, a mathematical model approach based on non-dimensional parameters is used to calculate the natural frequency taking into account the soil structure interaction (SSI) compared with the p-y method and measured frequency in the North Sea Wind farms.

Keywords: offshore wind turbines, semi analytical FE analysis, p-y curves, piles foundations

Procedia PDF Downloads 457

Authors: B. Khatri, K. Lappe, M. Habedank, T. Müller, C. Megnin, T. Hanemann

Abstract:

We present a process flow for the development of ceramic-filled polymer composite filaments compatible with the fused deposition modeling (FDM) 3D printing process. Thermoplastic-ceramic composites were developed using acrylonitrile butadiene styrene (ABS) and 10- and 20 vol.% barium titanate (BaTiO3) powder (corresponding to 39.47- and 58.23 wt.% respectively) and characterized for their flow properties. To make them compatible with the existing FDM process, the composites were extruded into filaments. These composite filaments were subsequently structured into tensile stress specimens using a commercially available FDM 3D printer and characterized for their mechanical properties. Rheometric characterization of the material composites revealed non-Newtonian behavior with the viscosity logarithmically decreasing over increasing shear rates, as well as higher viscosities for samples with higher BaTiO3 filler content for a given shear rate (with the ABS+20vol.% BaTiO3 composite being over 50% more viscous compared to pure ABS at a shear rate of 1x〖10〗^3 s^(-1)). Mechanical characterization of the tensile stress specimens exhibited increasingly brittle behavior as well as a linearly decreasing ultimate tensile strength of the material composites with increasing volumetric ratio of BaTiO3 (from σ_max=32.4MPa for pure ABS to σ_max=21.3MPa for ABS+20vol.% BaTiO3). Further studies being undertaken include the development of composites with higher filler concentrations, sintering of the printed composites to yield pure dielectric structures and the determination of the dielectric characteristics of the composites.

Keywords: ceramic composites, fused deposition modeling, material characterization, rapid prototyping

Procedia PDF Downloads 326

Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

Procedia PDF Downloads 120

Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

Abstract:

Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

Procedia PDF Downloads 164

Authors: Yungtai Lo

Abstract:

Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-log-normal random effects model, a two-part logit-truncated normal random effects model, a two-part logit-gamma random effects model, and a two-part logit-skew normal random effects model were used to examine effects of a bottle-weaning intervention on reducing bottle use and daily milk intake from bottles in toddlers aged 11 to 13 months in a randomized controlled trial. We show in all four two-part models that the intervention promoted bottle-weaning and reduced daily milk intake from bottles in toddlers drinking from a bottle. We also show that there are no differences in model fit using either the logit link function or the probit link function for modeling the probability of bottle-weaning in all four models. Furthermore, prediction accuracy of the logit or probit link function is not sensitive to the distribution assumption on daily milk intake from bottles in toddlers not off bottles.

Keywords: two-part model, semi-continuous variable, truncated normal, gamma regression, skew normal, Pearson residual, receiver operating characteristic curve

Procedia PDF Downloads 339

Authors: D. Subedi, D. Sharma

Abstract:

Current transformers (CTs) are used to transform large primary currents to a small secondary current. Since most standard equipment’s are not designed to handle large primary currents the CTs have an important part in any electrical system for the purpose of Metering and Protection both of which are integral in Power system. Now a days due to advancement in solid state technology, the operation times of the protective relays have come to a few cycles from few seconds. Thus, in such a scenario it becomes important to study the transient response of the current transformers as it will play a vital role in the operating of the protective devices. This paper shows the steady state and transient behavior of current transformers and how it changes with change in connected burden. The transient and steady state response will be captured using the data acquisition software LabVIEW. Analysis is done on the real time data gathered using LabVIEW. Variation of current transformer characteristics with changes in burden will be discussed.

Keywords: accuracy, accuracy limiting factor, burden, current transformer, instrument security factor

Procedia PDF Downloads 337

Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

Abstract:

Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

Procedia PDF Downloads 245

Authors: Ma Lei-Lei, Zhou You

Abstract:

Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

Procedia PDF Downloads 90

Authors: Ali Hamza

Abstract:

Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

Procedia PDF Downloads 69

Authors: Changshan Zhang, Zhijin Yu, Shixing Fan

Abstract:

A comprehensive hazard evaluation for coal self-heating in composite long-wall gobs is heavily dependent on computational simulation. In this study, the spatial distributions of cracks which caused significant air leakage were simulated by universal distinct element code (UDEC) simulation. Based on the main routes of air leakage and characteristics of coal self-heating, a computational fluid dynamics (CFD) modeling was conducted to model the coal spontaneous combustion dangerous area in composite long-wall gobs. The results included the oxygen concentration distributions and temperature profiles showed that the numerical approach is validated by comparison with the test data. Furthermore, under the conditions of specific engineering, the major locations where some techniques for extinguishing and preventing long-wall gob fires need to be put into practice were also examined.

Keywords: computational simulation, UDEC simulation, coal self-heating, CFD modeling, long-wall gobs

Procedia PDF Downloads 301

Authors: Kbab H, Hamitouche T

Abstract:

In recent years, significant progress has been accomplished in the field of aerospace propulsion and propulsion systems. These developments are associated with efforts to enhance the accuracy of the analysis of aerothermodynamic phenomena in the engine. This applies in particular to the flow in the nozzles used. One of the most remarkable processes in this field is thrust vectoring by means of devices able to orientate the thrust vector and control the deflection of the exit jet in the engine nozzle. In the study proposed, we are interested in the fluid thrust vectoring using a second injection in the nozzle divergence. This fluid injection causes complex phenomena, such as boundary layer separation, which generates a shock wave in the primary jet upstream of the fluid interacting zone (primary jet - secondary jet). This will cause the deviation of the main flow, and therefore of the thrust vector with reference to the axis nozzle. In the modeling of the fluidic thrust vector, various parameters can be used. The Mach number of the primary jet and the injected fluid, the total pressures ratio, the injection rate, the thickness of the upstream boundary layer, the injector position in the divergent part, and the nozzle geometry are decisive factors in this type of phenomenon. The complexity of the latter challenges researchers to understand the physical phenomena of the turbulent boundary layer encountered in supersonic nozzles, as well as the calculation of its thickness and the friction forces induced on the walls. The present study aims to numerically simulate the thrust vectoring by secondary injection using the ANSYS-FLUENT, then to analyze and validate the results and the performances obtained (angle of deflection, efficiency...), which will then be compared with those obtained by other authors.

Keywords: CD Nozzle, TVC, SVC, NPR, CFD, NPR, SPR

Procedia PDF Downloads 128

Authors: Abu Afree Andalib, M. Mezbah Uddin, M. Rafiur Rahman, M. Abir Hossain, Rajia Sultana Kamol

Abstract:

This paper deals with the analysis of flow past the marine turbine blade which is designed by using the blade element theory and momentum theory for the purpose of using in the field of renewable energy. The designed blade is analyzed for various parameters using FSI module of Ansys. Computational Fluid Dynamics is used for the study of fluid flow past the blade and other fluidic phenomena such as lift, drag, pressure differentials, energy dissipation in water. Finite Element Analysis (FEA) module of Ansys was used to analyze the structural parameter such as stress and stress density, localization point, deflection, force propagation. Fine mesh is considered in every case for more accuracy in the result according to computational machine power. The relevance of design, search and optimization with respect to complex fluid flow and structural modeling is considered and analyzed. The relevancy of design and optimization with respect to complex fluid for minimum drag force using Ansys Adjoint Solver module is analyzed as well. The graphical comparison of the above-mentioned parameter using CFD and FEA and subsequently FSI technique is illustrated and found the significant conformity between both the results.

Keywords: blade element theory, computational fluid dynamics, finite element analysis, fluid-structure interaction, momentum theory

Procedia PDF Downloads 288

Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

Abstract:

Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

Procedia PDF Downloads 326

Authors: F. R. M. Nascimento, E. E. S. Lora, J. C. E. Palácio

Abstract:

A mathematical model to investigate the performance of a two stage fixed bed downdraft gasifier operating with air, steam and oxygen mixtures as the gasifying fluid has been developed. The various conditions of mixtures for a double stage fluid entry, have been performed. The model has been validated through a series of experimental tests performed by NEST – The Excellence Group in Thermal and Distributed Generation of the Federal University of Itajubá. Influence of mixtures are analyzed through the Steam to Biomass (SB), Equivalence Ratio (ER) and the Oxygen Concentration (OP) parameters in order to predict the best operating conditions to obtain adequate output gas quality, once is a key parameter for subsequent gas processing in the synthesis of biofuels, heat and electricity generation. Results show that there is an optimal combination in the steam and oxygen content of the gasifying fluid which allows the user find the best conditions to design and operate the equipment according to the desired application.

Keywords: air, equilibrium, downdraft, fixed bed gasification, mathematical modeling, mixtures, oxygen steam

Procedia PDF Downloads 473

Authors: Mohamed Fathy Heweage, Xiao Wen, Ayman Mokhtar, Ahmed Eldamarawy

Abstract:

In this paper, we are interested in modeling, simulation, and measurement of the laser spot position with a quadrant detector. We enhance detection and tracking of semi-laser weapon decoding system based on microcontroller. The system receives the reflected pulse through quadrant detector and processes the laser pulses through a processing circuit, a microcontroller decoding laser pulse reflected by the target. The seeker accuracy will be enhanced by the decoding system, the laser detection time based on the receiving pulses number is reduced, a gate is used to limit the laser pulse width. The model is implemented based on Pulse Repetition Frequency (PRF) technique with two microcontroller units (MCU). MCU1 generates laser pulses with different codes. MCU2 decodes the laser code and locks the system at the specific code. The codes EW selected based on the two selector switches. The system is implemented and tested in Proteus ISIS software. The implementation of the full position determination circuit with the detector is produced. General system for the spot position determination was performed with the laser PRF for incident radiation and the mechanical system for adjusting system at different angles. The system test results show that the system can detect the laser code with only three received pulses based on the narrow gate signal, and good agreement between simulation and measured system performance is obtained.

Keywords: four quadrant detector, pulse code detection, laser guided weapons, pulse repetition frequency (PRF), Atmega 32 microcontrollers

Procedia PDF Downloads 372

Authors: Fatemeh Sadat Sharifi

Abstract:

In this research, a barotropic model was employed to consider the tidal studies in the Persian Gulf and Oman Sea, where the only sufficient force was the tidal force. To do that, a finite-difference, free-surface model called Regional Ocean Modeling System (ROMS), was employed on the data over the Persian Gulf and Oman Sea. To analyze flow patterns of the region, the results of limited size model of The Finite Volume Community Ocean Model (FVCOM) were appropriated. The two points were determined since both are one of the most critical water body in case of the economy, biology, fishery, Shipping, navigation, and petroleum extraction. The OSU Tidal Prediction Software (OTPS) tide and observation data validated the modeled result. Next, tidal elevation and speed, and tidal analysis were interpreted. Preliminary results determine a significant accuracy in the tidal height compared with observation and OTPS data, declaring that tidal currents are highest in Hormuz Strait and the narrow and shallow region between Iranian coasts and Islands. Furthermore, tidal analysis clarifies that the M_2 component has the most significant value. Finally, the Persian Gulf tidal currents are divided into two branches: the first branch converts from south to Qatar and via United Arab Emirate rotates to Hormuz Strait. The secondary branch, in north and west, extends up to the highest point in the Persian Gulf and in the head of Gulf turns counterclockwise.

Keywords: numerical model, barotropic tide, tidal currents, OSU tidal prediction software, OTPS

Procedia PDF Downloads 121

Authors: Nima Pirhadi, Yong Bo Shao, Xusheng Wa, Jianguo Lu

Abstract:

Several models based on the simplified method introduced by Seed and Idriss (1971) have been developed to assess the liquefaction potential of saturated sandy soils. The procedure includes determining the cyclic resistance of the soil as the cyclic resistance ratio (CRR) and comparing it with earthquake loads as cyclic stress ratio (CSR). Of all methods to determine CRR, the methods using shear-wave velocity (Vs) are common because of their low sensitivity to the penetration resistance reduction caused by fine content (FC). To evaluate the capability of the models, based on the Vs., the new data from Bachu-Jianshi earthquake case history collected, then the prediction results of the models are compared to the measured results; consequently, the accuracy of the models are discussed via three criteria and graphs. The evaluation demonstrates reasonable accuracy of the models in the Banchu region.

Keywords: seismic liquefaction, banchu-jiashi earthquake, shear-wave velocity, liquefaction potential evaluation

Procedia PDF Downloads 224