Pub Date : 2018-05-30DOI: 10.5772/INTECHOPEN.73332
S. Voronin, C. Zaroli
Inverse problems occur in a wide range of scientific applications, such as in the fields of signal processing, medical imaging, or geophysics. This work aims to present to the field practitioners, in an accessible and concise way, several established and newer cutting-edge computational methods used in the field of inverse problems — and when and how these techniques should be employed.
{"title":"Survey of Computational Methods for Inverse Problems","authors":"S. Voronin, C. Zaroli","doi":"10.5772/INTECHOPEN.73332","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.73332","url":null,"abstract":"Inverse problems occur in a wide range of scientific applications, such as in the fields of signal processing, medical imaging, or geophysics. This work aims to present to the field practitioners, in an accessible and concise way, several established and newer cutting-edge computational methods used in the field of inverse problems — and when and how these techniques should be employed.","PeriodicalId":404805,"journal":{"name":"Recent Trends in Computational Science and Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123999087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-30DOI: 10.5772/INTECHOPEN.73836
D. Akhmed-Zaki, M. Mansurova, Timur Imankulov, D. Lebedev, O. Turar, B. Daribayev, S. Aubakirov, A. Shomanov, K. Aidarov
This chapter discusses high-performance computational and information technologies for numerical models and data processing. In the first part of the chapter, the numerical model of the oil displacement problem was considered by injection of chemical reagents to increase oil recovery of reservoir. Moreover the fragmented algorithm was developed for solving this problem and the algorithm for high-performance visualization of calculated data. Analysis and comparison of parallel algorithms based on the fragmented approach and using MPI technologies are also presented.The algorithm for solving given problem on mobile platforms andanalysisofcomputationalresultsisgiventoo.Inthesecondpartofthechapter,theproblem ofunstructuredandsemi-structureddataprocessingwasconsidered.Itwasdecidedtoaddress the task of n-gram extraction which requires a lot of computing with large amount of textual data. In order to deal with such complexity, there was a need to adopt and implement parallelization patterns. The second part of the chapter also describes parallel implementation of the document clustering algorithm that used a heuristic genetic algorithm. Finally, a novel UPC implementation of MapReduce framework for semi-structured data processing was
{"title":"High-Performance Computational and Information Technologies for Numerical Models and Data Processing","authors":"D. Akhmed-Zaki, M. Mansurova, Timur Imankulov, D. Lebedev, O. Turar, B. Daribayev, S. Aubakirov, A. Shomanov, K. Aidarov","doi":"10.5772/INTECHOPEN.73836","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.73836","url":null,"abstract":"This chapter discusses high-performance computational and information technologies for numerical models and data processing. In the first part of the chapter, the numerical model of the oil displacement problem was considered by injection of chemical reagents to increase oil recovery of reservoir. Moreover the fragmented algorithm was developed for solving this problem and the algorithm for high-performance visualization of calculated data. Analysis and comparison of parallel algorithms based on the fragmented approach and using MPI technologies are also presented.The algorithm for solving given problem on mobile platforms andanalysisofcomputationalresultsisgiventoo.Inthesecondpartofthechapter,theproblem ofunstructuredandsemi-structureddataprocessingwasconsidered.Itwasdecidedtoaddress the task of n-gram extraction which requires a lot of computing with large amount of textual data. In order to deal with such complexity, there was a need to adopt and implement parallelization patterns. The second part of the chapter also describes parallel implementation of the document clustering algorithm that used a heuristic genetic algorithm. Finally, a novel UPC implementation of MapReduce framework for semi-structured data processing was","PeriodicalId":404805,"journal":{"name":"Recent Trends in Computational Science and Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129895216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-30DOI: 10.5772/INTECHOPEN.73831
V. MARTÍNEZ-LUACES, M. Ohanian
A well-known technique, electrochemical noise analysis (ENA), measures the potential fluctuations produced by kinetic variations along the electrochemical corrosion process. This practice requires the application of diverse signal processing methods. Therefore, in order to propose and evaluate new methodologies, it is absolutely necessary to simulate signals by computer data generation using different algorithms. In the first approach, data were simulated by superimposing Gaussian noise to nontrivial trend lines. Then, several methods were assessed by using this set of computer-simulated data. These results indicate that a new methodology based on medians of moving intervals and cubic splines interpolation show the best performance. Nevertheless, relative errors are acceptable for the trend but not for noise. In the second approach, we used artificial intelligence for trend removal, combining an interval signal processing with backpropagation neural networks. Finally, a non-Gaussian noise function that simulates non-stationary pits was proposed and all detrending methods were re-evaluated, resulting that when increasing difference between trend and noise, the accuracy of the artificial neural networks (ANNs) was reduced. In addition, when polynomial fitting, moving average removal (MAR) and moving median removal (MMR) were evaluated, MMR yielded best results, though it is not a definitive solution.
{"title":"Data Simulation and Trend Removal Optimization Applied to Electrochemical Noise","authors":"V. MARTÍNEZ-LUACES, M. Ohanian","doi":"10.5772/INTECHOPEN.73831","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.73831","url":null,"abstract":"A well-known technique, electrochemical noise analysis (ENA), measures the potential fluctuations produced by kinetic variations along the electrochemical corrosion process. This practice requires the application of diverse signal processing methods. Therefore, in order to propose and evaluate new methodologies, it is absolutely necessary to simulate signals by computer data generation using different algorithms. In the first approach, data were simulated by superimposing Gaussian noise to nontrivial trend lines. Then, several methods were assessed by using this set of computer-simulated data. These results indicate that a new methodology based on medians of moving intervals and cubic splines interpolation show the best performance. Nevertheless, relative errors are acceptable for the trend but not for noise. In the second approach, we used artificial intelligence for trend removal, combining an interval signal processing with backpropagation neural networks. Finally, a non-Gaussian noise function that simulates non-stationary pits was proposed and all detrending methods were re-evaluated, resulting that when increasing difference between trend and noise, the accuracy of the artificial neural networks (ANNs) was reduced. In addition, when polynomial fitting, moving average removal (MAR) and moving median removal (MMR) were evaluated, MMR yielded best results, though it is not a definitive solution.","PeriodicalId":404805,"journal":{"name":"Recent Trends in Computational Science and Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132204582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-02-26DOI: 10.5772/INTECHOPEN.73838
Sakir Yucel
A wireless network covering most of the city is a key component of a smart city. Although the wireless network offers many benefits, a key issue is the costs associated with laying out the infrastructure and services, making the bandwidth available and maintaining the services. We believe community involvement is important in building city-wide wireless networks. Indeed, many community wireless networks have been successful. Could the city inspire and assist the communities with building their wireless networks, and then unite them for a city-wide wireless network? We address the first question by presenting a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community where platform sides work together toward achieving smart community objectives. One challenge is to estimate the total cost, benefits and drawbacks of such platforms. Another challenge is to model risks and mitigation plans for their success. We examine relevant dynamics in measuring the total cost, benefits, drawbacks and risks of smart community wireless platforms and develop models for estimating their success under various scenarios. To develop models, we use an intelligence framework that incorporates systems dynamics modelling with statistical, economical and machine learning methods. plans, drawbacks, policies and strategies, success criteria for each service area. For this characterization, the size of the service area matters. The resources such as social and non-profit organizations and businesses in the area matter. Opportunities such as economic development opportunities in the service area matter. How the municipality sees the service area matters with respect to whether municipality considers significant investment or not in the area, and what social initiatives and public services are planned. Existence of substitutable offerings matters. drawbacks, risks, policies, strategies and criteria of success for a specific service area in a community. The same that we estimating for estimating the under various conditions and scenarios. In we developed a generic SD model for estimating the benefits and drawbacks, and for incor-porating the causal loops among benefits, drawbacks, risks and mitigation plans in existence of network externalities. We outlined how the generic model could be instantiated for specific dynamics and to analyze different scenarios.
{"title":"Smart Community Wireless Platforms: Costs, Benefits, Drawbacks, Risks","authors":"Sakir Yucel","doi":"10.5772/INTECHOPEN.73838","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.73838","url":null,"abstract":"A wireless network covering most of the city is a key component of a smart city. Although the wireless network offers many benefits, a key issue is the costs associated with laying out the infrastructure and services, making the bandwidth available and maintaining the services. We believe community involvement is important in building city-wide wireless networks. Indeed, many community wireless networks have been successful. Could the city inspire and assist the communities with building their wireless networks, and then unite them for a city-wide wireless network? We address the first question by presenting a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community where platform sides work together toward achieving smart community objectives. One challenge is to estimate the total cost, benefits and drawbacks of such platforms. Another challenge is to model risks and mitigation plans for their success. We examine relevant dynamics in measuring the total cost, benefits, drawbacks and risks of smart community wireless platforms and develop models for estimating their success under various scenarios. To develop models, we use an intelligence framework that incorporates systems dynamics modelling with statistical, economical and machine learning methods. plans, drawbacks, policies and strategies, success criteria for each service area. For this characterization, the size of the service area matters. The resources such as social and non-profit organizations and businesses in the area matter. Opportunities such as economic development opportunities in the service area matter. How the municipality sees the service area matters with respect to whether municipality considers significant investment or not in the area, and what social initiatives and public services are planned. Existence of substitutable offerings matters. drawbacks, risks, policies, strategies and criteria of success for a specific service area in a community. The same that we estimating for estimating the under various conditions and scenarios. In we developed a generic SD model for estimating the benefits and drawbacks, and for incor-porating the causal loops among benefits, drawbacks, risks and mitigation plans in existence of network externalities. We outlined how the generic model could be instantiated for specific dynamics and to analyze different scenarios.","PeriodicalId":404805,"journal":{"name":"Recent Trends in Computational Science and Engineering","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122528647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-20DOI: 10.5772/INTECHOPEN.72320
F. Ferguson, J. Mendez, D. Dodoo-Amoo
Computational Fluid Dynamics (CFD) solutions have played an important role in the design and evaluation of complex problems where analytical solutions are not available. Among many practical applications, hypersonic flows have been an area of intense research because of the important challenges found in this flow regime. The numerical study conducted herein, focuses on solving the hypersonic flat plate problem under realistic conditions, at high Reynolds and Mach numbers. The numerical scheme implemented in this study solves the two-dimensional unsteady Navier Stokes Equations, using a novel technique called Integro-Differential Scheme (IDS) that combines the traditional finite volume and the finite difference methods. Moreover, this scheme is built on the premise of reducing the numerical errors through the implementation of a consistent averaging procedure. Unlike other numerical approaches, where free molecular effects are considered, this study enforces no-slip and fixed temperature as boundary conditions. The IDS approach accurately predicted the physics in the vicinity of the hypersonic leading edge at such realistic conditions. Even though there are slight discrepancies between the numerical solution and the available experimental data, the IDS solution revealed some interesting details about the flow field that was previously undiscovered.
{"title":"Evaluating the Hypersonic Leading-Edge Phenomena at High Reynolds and Mach Numbers","authors":"F. Ferguson, J. Mendez, D. Dodoo-Amoo","doi":"10.5772/INTECHOPEN.72320","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.72320","url":null,"abstract":"Computational Fluid Dynamics (CFD) solutions have played an important role in the design and evaluation of complex problems where analytical solutions are not available. Among many practical applications, hypersonic flows have been an area of intense research because of the important challenges found in this flow regime. The numerical study conducted herein, focuses on solving the hypersonic flat plate problem under realistic conditions, at high Reynolds and Mach numbers. The numerical scheme implemented in this study solves the two-dimensional unsteady Navier Stokes Equations, using a novel technique called Integro-Differential Scheme (IDS) that combines the traditional finite volume and the finite difference methods. Moreover, this scheme is built on the premise of reducing the numerical errors through the implementation of a consistent averaging procedure. Unlike other numerical approaches, where free molecular effects are considered, this study enforces no-slip and fixed temperature as boundary conditions. The IDS approach accurately predicted the physics in the vicinity of the hypersonic leading edge at such realistic conditions. Even though there are slight discrepancies between the numerical solution and the available experimental data, the IDS solution revealed some interesting details about the flow field that was previously undiscovered.","PeriodicalId":404805,"journal":{"name":"Recent Trends in Computational Science and Engineering","volume":"28 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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