Search results for: resistance-capacitance network model

Authors: Masato Nishi, Tetsushi Kaburagi, Masashi Kurose, Tei Hirashima, Tetsusei Kurasiki

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The process of thermoforming a carbon fiber reinforced thermoplastic (CFRTP) has increased its presence in the automotive industry for its wide applicability to the mass production car. A non-isothermal forming for CFRTP can shorten its cycle time to less than 1 minute. In this paper, the textile reinforcement FE model which the authors proposed in a previous work is extended to the CFRTP model for non-isothermal forming simulation. The effect of thermoplastic is given by adding shell elements which consider thermal effect to the textile reinforcement model. By applying Reuss model to the stress calculation of thermoplastic, the proposed model can accurately predict in-plane shear behavior, which is the key deformation mode during forming, in the range of the process temperature. Using the proposed model, thermoforming simulation was conducted and the results are in good agreement with the experimental results.

Keywords: carbon fiber reinforced thermoplastic, finite element analysis, pre-impregnated textile composite, non-isothermal forming

Procedia PDF Downloads 424

Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

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A digital signature is an electronic signature form used by an original signer to sign a specific document. When the original signer is not in his office or when he/she travels outside, he/she delegates his signing capability to a proxy signer and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on factoring and discrete logarithm problem.

Keywords: discrete logarithm, factoring, proxy signature, key agreement

Procedia PDF Downloads 301

Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

Procedia PDF Downloads 508

Authors: Zoran Barac, Maja Martinovic

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Higher Education Institutions (HEIs) are a special kind of organization, with its unique purpose and combination of actors. From the societal point of view, they are central institutions in the society that are involved in the activities of education, research, and innovation. At the same time, their societal function derives complex relationships between involved actors, ranging from students, faculty and administration, business community and corporate partners, government agencies, to the general public. HEIs are also particularly interesting as objects of governance research because of their unique public purpose and combination of stakeholders. Furthermore, they are the special type of institutions from an organizational viewpoint. HEIs are often described as “loosely coupled systems” or “organized anarchies“ that implies the challenging nature of their governance models. Governance models of HEIs describe roles, constellations, and modes of interaction of the involved actors in the process of strategic direction and holistic control of institutions, taking into account each particular context. Many governance models of the HEIs are primarily based on the balance of power among the involved actors. Besides the actors’ power and influence, leadership style and environmental contingency could impact the governance model of an HEI. Analyzing them through the frameworks of institutional and contingency theories, HEI governance models originate as outcomes of their institutional and contingency adaptation. HEIs tend to fit to institutional context comprised of formal and informal institutional rules. By fitting to institutional context, HEIs are converging to each other in terms of their structures, policies, and practices. On the other hand, contingency framework implies that there is no governance model that is suitable for all situations. Consequently, the contingency approach begins with identifying contingency variables that might impact a particular governance model. In order to be effective, the governance model should fit to contingency variables. While the institutional context creates converging forces on HEI governance actors and approaches, contingency variables are the causes of divergence of actors’ behavior and governance models. Finally, an HEI governance model is a balanced adaptation of the HEIs to the institutional context and contingency variables. It also encompasses roles, constellations, and modes of interaction of involved actors influenced by institutional and contingency pressures. Actors’ adaptation to the institutional context brings benefits of legitimacy and resources. On the other hand, the adaptation of the actors’ to the contingency variables brings high performance and effectiveness. HEI governance models outlined and analyzed in this paper are collegial, bureaucratic, entrepreneurial, network, professional, political, anarchical, cybernetic, trustee, stakeholder, and amalgam models.

Keywords: governance, governance models, higher education institutions, institutional context, situational context

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Authors: S. K. Gupta, Richa Bansal

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In a deregulated power system structure, power producers, and customers share a common transmission network for wheeling power from the point of generation to the point of consumption. All parties in this open access environment may try to purchase the energy from the cheaper source for greater profit margins, which may lead to overloading and congestion of certain corridors of the transmission network. This may result in violation of line flow, voltage and stability limits and thereby undermine the system security. Utilities therefore need to determine adequately their Available Transfer Capability (ATC) to ensure that system reliability is maintained while serving a wide range of bilateral and multilateral transactions. This paper presents power transfer distribution factor based on AC load flow for the determination and enhancement of ATC. The study has been carried out for IEEE 24 bus Reliability Test System.

Keywords: available transfer capability, FACTS devices, power transfer distribution factors, electric

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

Procedia PDF Downloads 112

Authors: Davies Obaromi, Qin Yongsong, James Ndege, Azeez Adeboye, Akinwumi Odeyemi

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The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter.

Keywords: Besag2, CAR models, disease mapping, INLA, spatial models

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Authors: H. Ahmed, A. Schlenkhoff

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Recently, permeable breakwaters have been suggested to overcome the disadvantages of fully protection breakwaters. These protection structures have minor impacts on the coastal environment and neighboring beaches where they provide a more economical protection from waves and currents. For regular waves, a numerical model is used (FLOW-3D, VOF) to investigate the hydraulic performance of a permeable breakwater. The model of permeable breakwater consists of a pair of identical vertical slotted walls with an impermeable upper and lower part, where the draft is a decimal multiple of the total depth. The middle part is permeable with a porosity of 50%. The second barrier is located at distant of 0.5 and 1.5 of the water depth from the first one. The numerical model is validated by comparisons with previous laboratory data and semi-analytical results of the same model. A good agreement between the numerical results and both laboratory data and semi-analytical results has been shown and the results indicate the applicability of the numerical model to reproduce most of the important features of the interaction. Through the numerical investigation, the friction factor of the model is carefully discussed.

Keywords: coastal structures, permeable breakwater, slotted wall, numerical model, energy dissipation coefficient

Procedia PDF Downloads 385

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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The proliferation of social media platforms like Twitter has heightened the consequences of the spread of misinformation. To understand and model the spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological model to describe the underlying process that delineates the spread of misinformation on Twitter. Compared to the other epidemiological models, this model produces broader results because it includes the additional Skeptics (Z) compartment, wherein a user may be Exposed to an item of misinformation but not engage in any reaction to it, and the additional Exposed (E) compartment, wherein the user may need some time before deciding to spread a misinformation item. We analyzed misinformation regarding the unrest in Washington, D.C. in the month of March 2020, which was propagated by the use of the #DCblackout hashtag by different users across the U.S. on Twitter. Our analysis shows that misinformation can be modeled using the concept of epidemiology. To the best of our knowledge, this research is the first to attempt to apply the SEIZ epidemiological model to the spread of a specific item of misinformation, which is a category distinct from that of rumor and hoax on online social media platforms. Applying a mathematical model can help to understand the trends and dynamics of the spread of misinformation on Twitter and ultimately help to develop techniques to quickly identify and control it.

Keywords: Black Lives Matter, epidemiological model, mathematical modeling, misinformation, SEIZ model, Twitter

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

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In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Adebowale Olukayode Efunnowo

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This study was carried out to assess the awareness and utilization of Social Network Tools (SNTs) among agricultural science students in Colleges of Education in Ogun State, Nigeria. Simple random sampling techniques were used to select 280 respondents from the study area. Descriptive statistics was used to describe the objectives while Pearson Product Moment Correlation was used to test the hypothesis. The result showed that the majority (71.8%) of the respondents were single, with a mean age of 20 years. Almost all (95.7%) the respondents were aware of Facebook and 2go as a Social Network Tools (SNTs) while 85.0% of the respondents were not aware of Blackplanet, LinkedIn, MyHeritage and Bebo. Many (41.1%) of the respondents had views that using SNTs can enhance extensive literature survey, increase internet browsing potential, promote teaching proficiency, and update on outcomes of researches. However, 51.4% of the respondents perceived that SNTs usage as what is meant for the lecturers/adults only while 16.1% considered it as mainly used by internet fraudsters. Findings revealed that about 50.0% of the respondents browsed Facebook and 2go daily while more than 80% of the respondents used Blackplanet, MyHeritage, Skyrock, Bebo, LinkedIn and My YearBook as the need arise. Major constraints to the awareness and utilization of SNTs were high cost and poor quality of ICTs facilities (77.1%), epileptic power supply (75.0%), inadequate telecommunication infrastructure (71.1%), low technical know-how (62.9%) and inadequate computer knowledge (61.1%). The result of PPMC analysis showed that there was an inverse relationship between constraints and utilization of SNTs at p < 0.05. It can be concluded that constraints affect efficient and effective utilization of SNTs in the study area. It is hereby recommended that management of colleges of education and agricultural institutes should provide good internet connectivity, computer facilities, and alternative power supply in order to increase the awareness and utilization of SNTs among students.

Keywords: awareness, utilization, social network tools, constraints, students

Procedia PDF Downloads 344

Authors: Michael Ayomoh, Adriaan Roux, Oyindamola Omotuyi

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This paper presents an efficient robot navigation model in a multi-target domain amidst static and dynamic workspace obstacles. The problem is that of developing an optimal algorithm to minimize the total travel time of a robot as it visits all target points within its task domain amidst unknown workspace obstacles and finally return to its initial position. In solving this problem, a classical algorithm was first developed to compute the optimal number of paths to be travelled by the robot amidst the network of paths. The principle of shortest distance between robot and targets was used to compute the target point visitation order amidst workspace obstacles. Algorithm premised on the standard polar coordinate system was developed to determine the length of obstacles encountered by the robot hence giving room for a geometrical estimation of the total surface area occupied by the obstacle especially when classified as a relevant obstacle i.e. obstacle that lies in between a robot and its potential visitation point. A stochastic model was developed and used to estimate the likelihood of a dynamic obstacle bumping into the robot’s navigation path and finally, the navigation/obstacle avoidance algorithm was hinged on the hybrid virtual force field (HVFF) method. Significant modelling constraints herein include the choice of navigation path to selected target points, the possible presence of static obstacles along a desired navigation path and the likelihood of encountering a dynamic obstacle along the robot’s path and the chances of it remaining at this position as a static obstacle hence resulting in a case of re-routing after routing. The proposed algorithm demonstrated a high potential for optimal solution in terms of efficiency and effectiveness.

Keywords: multi-target, mobile robot, optimal path, static obstacles, dynamic obstacles

Procedia PDF Downloads 276

Authors: Masood Beheshtirad

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Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed.

Keywords: landslide, mapping, multiple model, regression

Procedia PDF Downloads 318

Authors: Isabelle Snyder

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Microgrid protection presents challenges to conventional protection techniques due to the low induced fault current. Protection relays present in microgrid applications require a combination of settings groups to adjust based on the architecture of the microgrid in islanded and grid-connected mode. In a radial system where the microgrid is at the other end of the feeder, directional elements can be used to identify the direction of the fault current and switch settings groups accordingly (grid connected or microgrid connected). However, with multiple microgrid connections, this concept becomes more challenging, and the direction of the current alone is not sufficient to identify the source of the fault current contribution. ORNL has previously developed adaptive relaying schemes through other DOE-funded research projects that will be evaluated and used as a baseline for this research. The four protection techniques in this study are the following: (1) Adaptive Current only Protection System (ACPS), Intentional (2) Unbalanced Control for Protection Control (IUCPC), (3) Adaptive Protection System with Communication Controller (APSCC) (4) Adaptive Model-Driven Protective Relay (AMDPR). The first two methods focus on identifying the islanded mode without communication by monitoring the current sequence component generated by the system (ACPS) or induced with inverter control during islanded mode (IUCPC) to identify the islanding condition without communication at the relay to adjust the settings. These two methods are used as a backup to the APSCC, which relies on a communication network to communicate the islanded configuration to the system components. The fourth method relies on a short circuit model inside the relay that is used in conjunction with communication to adjust the system configuration and computes the fault current and adjusts the settings accordingly.

Keywords: adaptive relaying, microgrid protection, sequence components, islanding detection, communication controlled protection, integrated short circuit model

Procedia PDF Downloads 90

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 181

Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Within agent-based modeling, opinion dynamics is the field that focuses on modeling people's opinions. In this prolific field, most of the literature is dedicated to the exploration of the two 'degrees of freedom' and how they impact the model’s properties (e.g., the average final opinion, the number of final clusters, etc.). These degrees of freedom are (1) the interaction rule, which determines how agents update their own opinion, and (2) the network topology, which defines the possible interaction among agents. In this work, we show that the third degree of freedom exists. This can be used to change a model's output up to 100% of its initial value or to transform two models (both from the literature) into each other. Since opinion dynamics models are representations of the real world, it is fundamental to understand how people’s opinions can be measured. Even for abstract models (i.e., not intended for the fitting of real-world data), it is important to understand if the way of numerically representing opinions is unique; and, if this is not the case, how the model dynamics would change by using different representations. The process of measuring opinions is non-trivial as it requires transforming real-world opinion (e.g., supporting most of the liberal ideals) to a number. Such a process is usually not discussed in opinion dynamics literature, but it has been intensively studied in a subfield of psychology called psychometrics. In psychometrics, opinion scales can be converted into each other, similarly to how meters can be converted to feet. Indeed, psychometrics routinely uses both linear and non-linear transformations of opinion scales. Here, we analyze how this transformation affects opinion dynamics models. We analyze this effect by using mathematical modeling and then validating our analysis with agent-based simulations. Firstly, we study the case of perfect scales. In this way, we show that scale transformations affect the model’s dynamics up to a qualitative level. This means that if two researchers use the same opinion dynamics model and even the same dataset, they could make totally different predictions just because they followed different renormalization processes. A similar situation appears if two different scales are used to measure opinions even on the same population. This effect may be as strong as providing an uncertainty of 100% on the simulation’s output (i.e., all results are possible). Still, by using perfect scales, we show that scales transformations can be used to perfectly transform one model to another. We test this using two models from the standard literature. Finally, we test the effect of scale transformation in the case of finite precision using a 7-points Likert scale. In this way, we show how a relatively small-scale transformation introduces both changes at the qualitative level (i.e., the most shared opinion at the end of the simulation) and in the number of opinion clusters. Thus, scale transformation appears to be a third degree of freedom of opinion dynamics models. This result deeply impacts both theoretical research on models' properties and on the application of models on real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

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Authors: Jamal Choudhry, Roland Larsson, Andreas Almqvist

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A multi-scale flash temperature model has been developed and validated against existing work. The core strength of the proposed model is that it can be adapted to predict flash contact temperatures occurring in various types of sliding systems. In this paper, it is used to investigate how different surface roughness parameters affect the flash temperatures. The results show that for decreasing Hurst exponents as well as increasing values of the high-frequency cut-off, the maximum flash temperature increases. It was also shown that the effect of surface roughness does not influence the average interface temperature. The model predictions were validated against data from an experiment conducted in a pin-on-disc machine. This also showed the importance of including a wear model when simulating flash temperature development in a sliding system.

Keywords: multiscale, pin-on-disc, finite element method, flash temperature, surface roughness

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Authors: A. Daftari, W. Kudla

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Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid cyclic loading. Liquefaction and related phenomena have been responsible for huge amounts of damage in historical earthquakes around the world. Modelling of soil behaviour is the main step in soil liquefaction prediction process. Nowadays, several constitutive models for sand have been presented. Nevertheless, only some of them can satisfy this mechanism. One of the most useful models in this term is UBCSAND model. In this research, the capability of this model is considered by using PLAXIS software. The real data of superstition hills earthquake 1987 in the Imperial Valley was used. The results of the simulation have shown resembling trend of the UBC3D-PLM model.

Keywords: liquefaction, plaxis, pore-water pressure, UBC3D-PLM

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Authors: Hamad M. Alhajeri

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Swirling chamber is a promising cooling method for heavily thermally loaded parts like turbine blades due to the additional circumferential velocity and therefore improved turbulent mixing of the fluid. This paper investigates numerically the effect of turbulence model on the heat convection of the swirling chamber. Grid independence analysis is conducted to obtain the proper grid dimension. The work validated with experimental data available in the literature. Flow analysis using improved delayed detached eddy simulation turbulence model and Reynolds averaged Navier-Stokes k-ɛ turbulence model is carried. The flow characteristic near the exit is reformed when improved delayed detached eddy simulation model used.

Keywords: gas turbine, Nusselt number, flow characteristics, heat transfer

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Authors: Raphael Naryongo, Philip Ngare, Anthony Waititu

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This paper deals with numerical simulation of Wishart processes for a single asset risky pricing model whose volatility is described by Wishart affine diffusion processes. The multi-factor specification of volatility will make the model more flexible enough to fit the stock market data for short or long maturities for better returns. The Wishart process is a stochastic process which is a positive semi-definite matrix-valued generalization of the square root process. The aim of the study is to model the log asset stock returns under the double Wishart stochastic volatility model. The solution of the log-asset return dynamics for Bi-Wishart processes will be obtained through Euler-Maruyama discretization schemes. The numerical results on the asset returns are compared to the existing models returns such as Heston stochastic volatility model and double Heston stochastic volatility model

Keywords: euler schemes, log-asset return, infinitesimal generator, wishart diffusion affine processes

Procedia PDF Downloads 371

Authors: H. Z. Ulukan, E. Demircioğlu, M. Erol Genevois

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The scope of this paper and the aim of proposed model were to apply monetary Input –Output (I-O) analysis to point out the importance of reusing know-how and other requirements in order to reduce the production costs in a manufacturing process for a laptop computer. I-O approach using the monetary input-output model is employed to demonstrate the impacts of different factors in a manufacturing process. A sensitivity analysis showing the correlation between these different factors is also presented. It is expected that the recommended model would have an advantageous effect in the cost minimization process.

Keywords: input-output analysis, monetary input-output model, manufacturing process, laptop computer

Procedia PDF Downloads 386

Authors: Irtaza M. Syed, Kaamran Raahemifar

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In this paper, we present a comparative assessment of Space Vector Pulse Width Modulation (SVPWM) and Model Predictive Control (MPC) for two-level three phase (2L-3P) Voltage Source Inverter (VSI). VSI with associated system is subjected to both control techniques and the results are compared. Matlab/Simulink was used to model, simulate and validate the control schemes. Findings of this study show that MPC is superior to SVPWM in terms of total harmonic distortion (THD) and implementation.

Keywords: voltage source inverter, space vector pulse width modulation, model predictive control, comparison

Procedia PDF Downloads 501

Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar

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In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG

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Authors: Jun Tian, Yu Yang, Liping Zhang, Qianhua Kan

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Experimental results show that SA 508-3 steel exhibits temperature dependent cyclic softening characteristic and obvious ratcheting behaviors, and dynamic strain age was observed at temperature range of 200 ºC to 350 ºC. Based on these observations, a temperature dependent cyclic plastic constitutive model was proposed by introducing the nonlinear cyclic softening and kinematic hardening rules, and the dynamic strain age was also considered into the constitutive model. Comparisons between experiments and simulations were carried out to validate the proposed model at elevated temperature.

Keywords: constitutive model, elevated temperature, ratcheting, SA 508-3

Procedia PDF Downloads 295

Authors: Masica Jordan Alston, Angela N. Bullock, Angela S. Henderson, Stephanie Strianse, Sade Dunn, Joseph Hackett, Alaysia Black Hackett, Marcus Mason

Abstract:

The Energy Model of Cumulative Grief was created in 2018. The Energy Model of Cumulative Grief utilizes historic models of grief stage theories. The innovative model is additionally unique due to its focus on cultural responsiveness. The Energy Model of Cumulative Grief helps to train practitioners who work with clients dealing with grief and loss. This paper assists in introducing the world to this innovative model and exploring how this model positively impacted a convenience sample of 140 practitioners and individuals experiencing grief and loss. Respondents participated in Webinars provided by the National Grief and Loss Center of America (NGLCA). Participants in this cross-sectional research design study completed one of three Grief and Loss Surveys created by the Grief and Loss Centers of America. Data analysis for this study was conducted via SPSS and Survey Hero to examine survey results for respondents. Results indicate that the Energy Model of Cumulative Grief was an effective resource for participants in addressing grief and loss. The majority of participants found the Webinars to be helpful and a conduit to providing them with higher levels of hope. The findings suggest that using The Energy Model of Cumulative Grief is effective in providing culturally responsive grief and loss resources to practitioners and clients. There are far reaching implications with the use of technology to provide hope to those suffering from grief and loss worldwide through The Energy Model of Cumulative Grief.

Keywords: grief, loss, grief energy, grieving brain

Procedia PDF Downloads 75

Authors: Shayem Saleh ALresheedi, Lu Song Feng, Abdulaziz Abdulwahab M. Fatani

Abstract:

Cloud computing is an emerging concept in the technological sphere. Its development enables many applications to avail information online and on demand. It is becoming an essential element for businesses due to its ability to diminish the costs of IT infrastructure and is being adopted in Saudi Arabia. However, there exist many factors that affect its adoption. Several researchers in the field have ignored the study of the TAM model for identifying the relevant factors and their impact for adopting of cloud computing. This study focuses on evaluating the acceptability of cloud computing and analyzing its impacting factors using Technology Acceptance Model (TAM) of technology adoption in Saudi Arabia. It suggests a model to examine the influential factors of the TAM model along with external factors of technical support in adapting the cloud computing. The proposed model has been tested through the use of multiple hypotheses based on calculation tools and collected data from customers through questionnaires. The findings of the study prove that the TAM model along with external factors can be applied in measuring the expected adoption of cloud computing. The study presents an investigation of influential factors and further recommendation in adopting cloud computing in Saudi Arabia.

Keywords: cloud computing, acceptability, adoption, determinants

Procedia PDF Downloads 188

Authors: Royia Soliman, Salma ElAnsary, Akram Amin Abdellatif, Florian Holzapfel

Abstract:

Model-Based Safety Analysis (MBSA) is an approach in which the system and safety engineers share a common system model created using a model-based development process. The model can also be extended by the failure modes of the system components. There are two famous approaches for the addition of fault behaviors to system models. The first one is to enclose the failure into the system design directly. The second approach is to develop a fault model separately from the system model, thus combining both independent models for safety analysis. This paper introduces a hybrid approach of MBSA. The approach tries to use informal abstracted models to investigate failure behaviors. The approach will combine various concepts such as directed graph traversal, event lists and Constraint Satisfaction Problems (CSP). The approach is implemented using an Object Oriented programming language. The components are abstracted to its failure logic and relationships of connected components. The implemented approach is tested on various flight control systems, including electrical and multi-domain examples. The various tests are analyzed, and a comparison to different approaches is represented.

Keywords: flight control systems, model based safety analysis, safety assessment analysis, system modelling

Procedia PDF Downloads 154

Authors: K. A. Adeleke

Abstract:

Often in epidemiological research, introducing stratified Cox model can account for the existence of interactions of some inherent factors with some major/noticeable factors. This research work aimed at modelling correlated variables in infant mortality with the existence of some inherent factors affecting the infant survival function. An alternative semiparametric Stratified Cox model is proposed with a view to take care of multilevel factors that have interactions with others. This, however, was used as a tool to model infant mortality data from Nigeria Demographic and Health Survey (NDHS) with some multilevel factors (Tetanus, Polio, and Breastfeeding) having correlation with main factors (Sex, Size, and Mode of Delivery). Asymptotic properties of the estimators are also studied via simulation. The tested model via data showed good fit and performed differently depending on the levels of the interaction of the strata variable Z*. An evidence that the baseline hazard functions and regression coefficients are not the same from stratum to stratum provides a gain in information as against the usage of Cox model. Simulation result showed that the present method produced better estimates in terms of bias, lower standard errors, and or mean square errors.

Keywords: stratified Cox, semiparametric model, infant mortality, multilevel factors, cofounding variables

Procedia PDF Downloads 553