Search results for: computational intelligence

Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

Procedia PDF Downloads 147

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 115

Authors: Ihsane Modhaffar, Kamal Gueraoui, Abouelkacem Qais, Abderrahmane Maaouni, Samir Men-La-Yakhaf, Hamid Eltourroug

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The main objective of this paper is to develop a Computational Fluid Dynamics (CFD) model to simulate and characterize the fiber suspension in flow in rectangular cavities. The model is intended to describe the velocity profile and to predict the fiber orientation. The flow was considered to be incompressible, and behave as Newtonian fluid containing suspensions of short-fibers. The numerical model for determination of velocity profile and fiber orientation during mold-filling stage of injection molding process was solved using finite volume method. The governing equations of this problem are: the continuity, the momentum and the energy. The obtained results were compared to available experimental findings. A good agreement between the numerical results and the experimental data was achieved.

Keywords: injection, composites, short-fiber reinforced thermoplastics, fiber orientation, incompressible fluid, numerical simulation

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Authors: Elaid Bouchetob, Bouchra Nadji

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This research paper compares the efficiency and reliability (R(t)) of SiC-based and Si-based DC-DC buck converters in thin layer PV systems with an AI-based MPPT controller. Using Simplorer/Simulink simulations, the study assesses their performance under varying conditions. Results show that the SiC-based converter outperforms the Si-based one in efficiency and cost-effectiveness, especially in high temperature and low irradiance conditions. It also exhibits superior reliability, particularly at high temperature and voltage. Reliability calculation (R(t)) is analyzed to assess system performance over time. The SiC-based converter demonstrates better reliability, considering factors like component failure rates and system lifetime. The research focuses on the buck converter's role in charging a Lithium battery within the PV system. By combining the SiC-based converter and AI-based MPPT controller, higher charging efficiency, improved reliability, and cost-effectiveness are achieved. The SiC-based converter proves superior under challenging conditions, emphasizing its potential for optimizing PV system charging. These findings contribute insights into the efficiency, reliability, and reliability calculation of SiC-based and Si-based converters in PV systems. SiC technology's advantages, coupled with advanced control strategies, promote efficient and sustainable energy storage using Lithium batteries. The research supports PV system design and optimization for reliable renewable energy utilization.

Keywords: efficiency, reliability, artificial intelligence, sic device, thin layer, buck converter

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Authors: Mohamed M. Abo Elazm, Ali I. Shehata, Mohamed M. Khairat Dawood

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A computational fluid dynamics (CFD) program FLUENT has been used to predict the fluid flow and heat transfer distribution within concentric heat exchangers. The effect of inlet inclination angle has been investigated with Reynolds number range (3000 – 4000) and Pr=0.71. The heat exchanger is fabricated from copper concentric inner tube with a length of 750 mm. The effects of hot to cold inlet flow rate ratio (MH/MC), Reynolds's number and of inlet inclination angle of 30°, 45°, 60° and 90° are considered. The results showed that the numerical prediction shows a good agreement with experimental measurement. The results present an efficient design of concentric tube heat exchanger to enhance the heat transfer by increasing the swirling effect.

Keywords: heat transfer, swirling effect, CFD, inclination angle, concentric tube heat exchange

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Authors: Irfana M., Priyandi Kabasi

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Children with specific language impairment (SLI) have difficulty acquiring and using language despite having all the requirements of cognitive skills to support language acquisition. These children have normal non-verbal intelligence, hearing, and oral-motor skills, with no history of social/emotional problems or significant neurological impairment. Nevertheless, their language acquisition lags behind their peers. Phonological complexity can be considered to be the major factor that causes the inaccurate production of speech in this population. However, the implementation of various ranges of complex phonological stimuli in the treatment session of SLI should be followed for a better prognosis of speech accuracy. Hence there is a need to study the levels of phonological complexity. The present study consisted of 7 individuals who were diagnosed with SLI and 10 developmentally normal children. All of them were Hindi speakers with both genders and their age ranged from 4 to 5 years. There were 4 sets of stimuli; among them were minimal contrast vs maximal contrast nonwords, minimal coarticulation vs maximal coarticulation nonwords, minimal contrast vs maximal contrast words and minimal coarticulation vs maximal coarticulation words. Each set contained 10 stimuli and participants were asked to repeat each stimulus. Results showed that production of maximal contrast was significantly accurate, followed by minimal coarticulation, minimal contrast and maximal coarticulation. A similar trend was shown for both word and non-word categories of stimuli. The phonological complexity effect was evident in the study for each participant group. Moreover, present study findings can be implemented for the management of SLI, specifically for the selection of stimuli.

Keywords: coarticulation, minimal contrast, phonological complexity, specific language impairment

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Authors: Ishtiaq A. Chaudhry, Zia R. Tahir, F. A. Siddiqui, F. Noor, M. J. Rashid

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It has experimentally been proved that the performance of compression ignition (CI) engine is spray characteristics related. In modern diesel engine the spray formation and the eventual combustion process are the vital processes that offer more challenges towards enhancing the engine performance. In the present work, the numerical simulation has been carried out for evaporating diesel sprays using Fluent software. For computational fluid dynamics simulation “Meshing” is done using Gambit software before transmitting it into fluent. The simulation is carried out using hot bomb conditions under varying chamber conditions such as gas pressure, nozzle diameter and fuel injection pressure. For comparison purpose, the numerical simulations the chamber conditions were kept the same as that of the experimental data. At varying chamber conditions the spray penetration rates are compared with the existing experimental results.

Keywords: evaporating diesel sprays, penetration rates, hot bomb conditions

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Authors: Amara Prakasa Rao, N. V. S. N. Sarma

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This paper describes the synthesis of linear array geometry with minimum sidelobe level and null control using the Taguchi method. Based on the concept of the orthogonal array, Taguchi method effectively reduces the number of tests required in an optimization process. Taguchi method has been successfully applied in many fields such as mechanical, chemical engineering, power electronics, etc. Compared to other evolutionary methods such as genetic algorithms, simulated annealing and particle swarm optimization, the Taguchi method is much easier to understand and implement. It requires less computational/iteration processing to optimize the problem. Different cases are considered to illustrate the performance of this technique. Simulation results show that this method outperforms the other evolution algorithms (like GA, PSO) for smart antenna systems design.

Keywords: array factor, beamforming, null placement, optimization method, orthogonal array, Taguchi method, smart antenna system

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Authors: Samia Kouki

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Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.

Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing

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Authors: Karuna Tuchinda, Sasithon Bland

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This work investigates the wear of a steam turbine blade coated with titanium nitride (TiN), and compares to the wear of uncoated blades. The coating is deposited on by physical vapor deposition (PVD) method. The working conditions of the blade were simulated and surface temperature and pressure values as well as flow velocity and flow direction were obtained. This data was used in the finite element wear model developed here in order to predict the wear of the blade. The wear mechanisms considered are erosive wear due to particle impingement and fluid jet, and fatigue wear due to repeated impingement of particles and fluid jet. Results show that the life of the TiN-coated blade is approximately 1.76 times longer than the life of the uncoated one.

Keywords: physical vapour deposition, steam turbine blade, titanium-based coating, wear prediction

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Authors: Yake Zhang, Peng Xu

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The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

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Authors: Edip Kemal, Sheshov Vlatko, Bojadjieva Julijana, Bogdanovic ALeksandra, Gjorgjeska Irena

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The wave propagation phenomenon in porous domains is of great importance in the field of geotechnical earthquake engineering. In these kinds of problems, the elastic waves propagate from the interior to the exterior domain and require special treatment at the computational level since apart from displacement in the solid-state there is a p-wave that takes place in the pore water phase. In this paper, a study on the implementation of multiphase finite elements is presented. The proposed algorithm is implemented in the ANSYS finite element software and tested on one-dimensional wave propagation considering both pore pressure wave propagation and displacement fields. In the simulation of porous media such as soils, the behavior is governed largely by the interaction of the solid skeleton with water and/or air in the pores. Therefore, coupled problems of fluid flow and deformation of the solid skeleton are considered in a detailed way.

Keywords: wave propagation, multiphase model, numerical methods, finite element method

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Authors: Youngji Lee, Yonghyeon Jeon, Sunyoung Bu, Philsu Kim

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The aim of this paper is to construct an algorithm of time step control for the error correction method most recently developed by one of the authors for solving stiff initial value problems. It is achieved with the generalized Chebyshev polynomial and the corresponding error correction method. The main idea of the proposed scheme is in the usage of the duplicated node points in the generalized Chebyshev polynomials of two different degrees by adding necessary sample points instead of re-sampling all points. At each integration step, the proposed method is comprised of two equations for the solution and the error, respectively. The constructed algorithm controls both the error and the time step size simultaneously and possesses a good performance in the computational cost compared to the original method. Two stiff problems are numerically solved to assess the effectiveness of the proposed scheme.

Keywords: stiff initial value problem, error correction method, generalized Chebyshev polynomial, node points

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Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

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Authors: Aishwarya Rai

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This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency.

Keywords: generative AI, digital transformation, LLM, artificial intelligence, startups, businesses

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Authors: Mohamed Elkholy, Ahmed Elfatatry

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Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Technology map. However, cloud computing brought about a number of security challenges as a result of the physical distribution of computational resources and the limited control that users have over the physical storage. This situation raises many security challenges for data integrity and confidentiality as well as authentication and access control. This work proposes a security mechanism for data integrity that allows a data owner to be aware of any modification that takes place to his data. The data integrity mechanism is integrated with an extended Kerberos authentication that ensures authorized access control. The proposed mechanism protects data confidentiality even if data are stored on an untrusted storage. The proposed mechanism has been evaluated against different types of attacks and proved its efficiency to protect cloud data storage from different malicious attacks.

Keywords: access control, data integrity, data confidentiality, Kerberos authentication, cloud security

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Yixun Shi

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Many interior point methods for convex programming solve an (n+m)x(n+m)linear system in each iteration. Many implementations solve this system in each iteration by considering an equivalent mXm system (4) as listed in the paper, and thus the job is reduced into solving the system (4). However, the system(4) has to be solved exactly since otherwise the error would be entirely passed onto the last m equations of the original system. Often the Cholesky factorization is computed to obtain the exact solution of (4). One Cholesky factorization is to be done in every iteration, resulting in higher computational costs. In this paper, two iterative methods for solving linear systems using vector division are combined together and embedded into interior point methods. Instead of computing one Cholesky factorization in each iteration, it requires only one Cholesky factorization in the entire procedure, thus significantly reduces the amount of computation needed for solving the problem. Based on that, a hybrid algorithm for solving convex programming problems is proposed.

Keywords: convex programming, interior point method, linear systems, vector division

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Authors: Ousmane Youme, Jean Marie Dembele, Eugene Ezin, Christophe Cambier

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In recent years, the convolutional neural networks (CNN) architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on a genetic algorithm to find the best individual in the optimal space. Our algorithms drastically reduce resource costs and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Keywords: CNN architecture, genetic algorithm, evolution algorithm, length constraints

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Authors: T. Godwin

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The design of a supply chain always entails the trade-off between responsiveness and efficiency. The launch of e-commerce has not only changed the way of shopping but also altered the supply chain design while trading off efficiency with responsiveness. A concept called ‘virtual responsiveness’ is introduced in the context of e-commerce supply chain. A simulation model is developed to compare actual responsiveness and virtual responsiveness to the customer in an e-commerce supply chain. The simulation is restricted to the movement of goods from the e-tailer to the customer. Customer demand follows a statistical distribution and is generated using inverse transformation technique. The two responsiveness schemes of the supply chain are compared in terms of the minimum number of inventory required at the e-tailer to fulfill the orders. Computational results show the savings achieved through virtual responsiveness. The insights gained from this study could be used to redesign e-commerce supply chain by incorporating virtual responsiveness. A part of the achieved cost savings could be passed back to the customer, thereby making the supply chain both effective and competitive.

Keywords: e-commerce, simulation modeling, supply chain, virtual responsiveness

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Authors: Yoonah Lee, Richard Kyung

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With technological advances in the computational biomedical field, the ability to measure neurotransmitters’ chemical imbalances that affect depression and anxiety has been established. By comparing the thermodynamics stability of amino acid supplements, such as glutamine, tyrosine, phe-nylalanine, and methionine, this research analyzes mood-regulating neurotransmitters, amino acid supplements, and antipsychotic substances (ie. Reserpine molecule and CRF complexes) in relation to depression and anxiety and suggests alternative complexes that are low in energy to act as more efficient treatments for mood disorders. To determine a molecule’s thermodynamic stability, this research examines the molecular energy using Avogadro, a software for building virtual molecules and calculating optimized geometry using GAFF (General Amber Force Field) and UFF (Universal Force Field). The molecules, built using Avogadro, is analyzed using their theoretical values and atomic properties.

Keywords: amino acids, anxiety, depression, neurotransmitters

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Authors: Oliver Mărunţălu, Gheorghe Lăzăroiu, Elena Elisabeta Manea, Dana Andreya Bondrea, Lăcrămioara Diana Robescu

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This paper presents the results obtained by numerical simulation of the pollutants dispersion in the atmosphere coming from the evacuation of combustion gases resulting from the fuel combustion used by electric thermal power plant using the software ANSYS CFX-CFD. The model uses the Navier-Stokes equation to simulate the dispersion of pollutants in the atmosphere. We considered as important factors in elaboration of simulation the atmospheric conditions (pressure, temperature, wind speed, wind direction), the exhaust velocity of the combustion gases, chimney height and the obstacles (buildings). Using the air quality monitoring stations we have measured the concentrations of main pollutants (SO2, NOx and PM). The pollutants were monitored over a period of 3 months, after that we calculated the average concentration, which is used by the software. The concentrations are: 8.915 μg/m3 (NOx), 9.587 μg/m3 (SO2) and 42 μg/m3 (PM). A comparison of test data with simulation results demonstrated that CFX was able to describe the dispersion of the pollutant as well the concentration of this pollutants in the atmosphere.

Keywords: air pollutants, computational fluid dynamics, dispersion, simulation

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Authors: See-Yuan Cheng, Kwang-Yhee Chin, Shuhaimi Mansor

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Rear-roof spoiler is commonly used for improving the aerodynamic performance of road vehicles. This study aims to investigate the effect of yaw angle on the effectiveness of strip-type rear-roof spoiler in providing lower drag and lift coefficients of a hatchback model. A computational fluid dynamics (CFD) method was used. The numerically obtained results were compared to the experimental data for validation of the CFD method. At increasing yaw angle, both the drag and lift coefficients of the model were to increase. In addition, the effectiveness of spoiler was deteriorated. These unfavorable effects were due to the formation of longitudinal vortices around the side edges of the model that had caused the surface pressure of the model to drop. Furthermore, there were significant crossflow structures developed behind the model at larger yaw angle, which were associated with the drop in the surface pressure of the rear section of the model and cause the drag coefficient to rise.

Keywords: Ahmed model, aerodynamics, spoiler, yaw angle

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Puneet Katyal, Punit Kumar

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A great deal of efforts has been done in the field of thermal effects in electrohydrodynamic lubrication (TEHL) during the last five decades. The focus was primarily on the development of an efficient numerical scheme to deal with the computational challenges involved in the solution of TEHL model; however, some important aspects related to the accurate description of lubricant properties such as viscosity, rheology and thermal conductivity in EHL point contact analysis remain largely neglected. A few studies available in this regard are based upon highly complex mathematical models difficult to formulate and execute. Using a simplified thermal EHL model for point contacts, this work sheds some light on the importance of accurate characterization of the lubricant properties and demonstrates that the computed TEHL characteristics are highly sensitive to lubricant properties. It also emphasizes the use of appropriate mathematical models with experimentally determined parameters to account for correct lubricant behaviour.

Keywords: TEHL, shear thinning, rheology, conductivity

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Authors: Abbas Moradi, Mohsen Safajoy, Reza Yazdanparast

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Analysis of blade-disc dovetail joints is one of the biggest challenges facing designers of aero-engines. To avoid comparatively expensive experimental full-scale tests, numerical methods can be used to simulate loaded disc-blades assembly. Mortar method provides a powerful and flexible tool for solving frictional contact problems. In this study, 2D frictional contact in dovetail has been analysed based on the mortar algorithm. In order to model the friction, the classical law of coulomb and moving friction cone algorithm is applied. The solution is then obtained by solving the resulting set of non-linear equations using an efficient numerical algorithm based on Newton–Raphson Method. The numerical results show that this approach has better convergence rate and accuracy than other proposed numerical methods.

Keywords: computational contact mechanics, dovetail joints, nonlinear FEM, mortar approach

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Authors: A. M. Gheitaghy, H. Saffari, G. Q. Zhang

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Numerical approach based on the electrical simulation method is proposed to solve a nonlinear transient heat conduction problem with nonlinear boundary for a spherical body. This problem represents a strong nonlinearity in both the governing equation for temperature dependent thermal property and the boundary condition for combined convective and radiative cooling. By analysing the equivalent electrical model using the electrical circuit simulation program HSPICE, transient temperature and heat flux distributions at sphere can be obtained easily and fast. The solutions clearly illustrate the effect of the radiation-conduction parameter Nrc, the Biot number and the linear coefficient of temperature dependent conductivity and heat capacity. On comparing the results with corresponding numerical solutions, the accuracy and efficiency of this computational method are found to be good.

Keywords: convective and radiative boundary, electrical simulation method, nonlinear heat conduction, spherical coordinate

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Y. El Khchine, M. Sriti

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In the present work, the forced convection heat transfer and fluid flow past an unconfined semi-circular cylinder is investigated. The two-dimensional simulation is employed for Reynolds numbers ranging from 10 ≤ Re ≤ 200, employing air (Pr = 0.71) as an operating fluid with Newtonian constant physics property. Continuity, momentum, and energy equations with appropriate boundary conditions are solved using the Computational Fluid Dynamics (CFD) solver Ansys Fluent. Various parameters flow such as lift, drag, pressure, skin friction coefficients, Nusselt number, Strouhal number, and vortex strength are calculated. The transition from steady to time-periodic flow occurs between Re=60 and 80. The effect of the Reynolds number on heat transfer is discussed. Finally, a developed correlation of Nusselt and Strouhal numbers is presented.

Keywords: forced convection, semi-circular cylinder, Nusselt number, Prandtl number

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Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

Abstract:

In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

Procedia PDF Downloads 114